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1. Bosch Fla 206 Software Definition System

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Over one third (35.2%) of girls, aged 6 through 11 years, are overweight or obese, and 19.1% are obese (). Being overweight is associated with various cardiovascular disease risk factors in girls (). Causes of an overweight and obesity problem in children and adolescents are related to both genetics and lifestyle (; ). While altering an individual’s genetic make-up may not be possible, some behavioral causes related to lifestyle are indeed modifiable, particularly physical activity.

U.S. Department of Health and Human Services (USDHHS, 2008) recommendations for children and adolescents call for at least 60 minutes daily of moderate to vigorous physical activity (MVPA), however, only 34.7% of girls, ages 6 to 11, attain this level, and the percentage drops to between 3.4% and 5.4% during adolescence (). Inadequate physical activity is problematic among young girls, putting them at increased risk for becoming overweight or obese during adolescence () with progression of the problem into adulthood (). The purpose of this pilot study, which served as an important step toward intervention development, was twofold: (1) to examine demographic, cognitive, affective, and behavioral variables related to body mass index (BMI) among low-active 6^th and 7^th grade girls; and (2) to determine differences between girls who were (a) healthy weight versus overweight or obese, (b) overweight versus obese, and (c) non-obese versus obese in the variables. Low-active girls were selected to ensure inclusion of an adequate number of overweight or obese girls, while avoiding stigmatization ().

Theoretical Framework and Review of the Literature

The Health Promotion Model (HPM; Pender, Murdaugh, & Parsons, 2011) guided the selection of demographic, cognitive, affective, and behavioral variables that might be related to overweight and obesity. According to the HPM, demographic variables include age, academic grade, race, ethnicity, and socio-economic status (SES). Cognitive variables of the HPM (Pender et al., 2011) specifically reported to influence adolescent physical activity include perceived benefits, barriers (; ), physical activity self-efficacy (), and social support (). Two other cognitive variables not included in the HPM (Pender et al., 2011) were also assessed because of their reported influence on physical activity in this young age group and interest to the investigators. These two cognitive variables are perceived importance (Whitehead, Biddle, O’Donovan, & Nevill, 2006) and current physical activity self-definition (e.g., perception of being “an exerciser” at the present time; ). The HPM also indicates that affective variables are related to physical activity. Evidence supports that enjoyment, an affective variable, is a major factor influencing girls’ physical activity ().

Behavioral variables associated with overweight and obesity among children and adolescents include (a) inadequate physical activity (; ), (b) excessive sedentary time, such as screen time (), and (c) unhealthy eating habits, including drinking sugar-sweetened beverages (), eating fried foods at or from a fast food place (), and not eating daily breakfast (; ). In a representative sample of 4659 girls, those who met the criteria for healthy behavior (being physically active on at least five days of the week, having less than 2 hours of screen time per day, and eating at least one serving of a fruit or vegetable per day and less than one serving per day of sweets, sweetened soft drinks, chips or French fries) were less likely to be obese compared to girls who did not meet these criteria ().

Whether positive perceptions related to these potentially modifiable cognitive and affective variables are associated with a healthier BMI among adolescent girls is unknown. Knowledge of demographic, cognitive, affective, and behavioral variables related to overweight and obesity among girls in 6^th and 7^th grade can be used to identify those at risk and target specific areas for intervention.

Methods

A descriptive design with convenience sampling was used. The sample included 6^th through 7^th grade girls from two Midwestern U.S. urban public middle schools whose students were comparable regarding certain demographic characteristics. According to the Center for Educational Performance and Information (2009) school data, close to half of the students in the two schools were female. Approximately one-fourth of the students were White, slightly over half were African American, and those remaining were of other races. Close to 79% of the students in each school were economically disadvantaged.

Girls completed baseline data measures prior to their involvement in either a physical activity intervention, “Girls on the Move,” at one of the schools or an attention control condition at the other school. The investigators did not share information with the girls about each school’s randomization status until after the baseline data were collected. Results of the intervention study are reported elsewhere ().

A total of 209 girls from the two schools were invited to participate in the study. Girls who expressed an interest in participating received information packets from the investigators. Each packet contained a study flyer, an informational letter for parents/guardians, consent and assent form, and a brief screening tool for determining a girl’s eligibility for participation. Girls in 6^th or 7^th grade were included in the study if they met the following inclusion criteria: (a) reported not meeting national MVPA recommendations; (b) were available and willing to participate in the six-month study; and (c) were able to read, understand, and speak English. Exclusion criteria included: (a) involvement in school or community sports or other organized physical activities or lessons that involved MVPA and required participation three or more days a week during the school year; and (b) a health condition limiting ability to perform safe MVPA. Seventy-three assented girls who had written permission to participate from their parents/guardians completed baseline measures. A flow diagram depicting recruitment and enrollment information has been reported elsewhere ().

Measures

Girls responded to survey items that focused on demographic, cognitive, affective, and behavioral variables and wore an accelerometer to measure physical activity and sedentary time. Survey items related to the demographic and behavioral (e.g., screen time and intake of fruits and vegetables) variables were obtained from the 2009 Youth Risk Behavior Surveys that involved middle and high school students (Youth Risk Behavior Surveillance System [YRBSS]; Centers for Disease Control and Prevention [CDC], 2009). Additional single items that assessed other eating behaviors (e.g., intake of sugar-sweetened beverages, fried foods at or from a fast food place, and breakfast) were obtained from published studies involving a similar age group (; ; ). Girls also responded to a single item to report the number of days of participation in school physical education (PE) per week.

Demographic variables

Single item questions addressed age, academic grade, race, and ethnicity. Parents or guardians provided information on the consent form to indicate whether their daughters were enrolled in the free or reduced price lunch program at school; the data served as a proxy for SES.

Cognitive and affective variables

Cognitive variables included perceived benefits of physical activity, barriers to physical activity, physical activity self-efficacy, social support, importance, and current physical activity self-definition. The affective variable included enjoyment of physical activity. For all variables except perceived barriers to physical activity, a higher score indicated more positive perceptions than a lower score. For perceived barriers, a higher score indicated more negative perceptions ().

Perceived benefits, barriers, and physical activity self-efficacy

Perceived benefits of and barriers to physical activity and perceived physical activity self-efficacy were measured by three scales: 12-item Perceived Benefits Scale, 17-item Perceived Barriers Scale, and 17-item Perceived Physical Activity Self-Efficacy Scale. All have response choices ranging from not at all true (1) to very true (4). Cronbach’s α for the 10-item Perceived Benefits Scale, 9-item Perceived Barriers Scale, and 11-item Perceived Physical Activity Self-Efficacy Scale developed by the second author in prior research with adolescents were .80, .78, and .86, respectively (: ). Face, content, and construct validity has also been reported for each of the three scales (: ). Prior to this study, the investigators added new items to the existing instruments based on recommendations to enhance comprehensiveness received from 6^th- through 8^th-grade girls participating in focus groups. The additional items increased the Cronbach’s α associated with each scale to .85 (95% CI [.79, .90]), .88 (95% CI [.82, .92]), and .91 (95% CI [.87, .94]), respectively.

Social support

An 8-item Social Support Scale was used to evaluate the support received for physical activity from up to three persons identified by each girl. One item example was: Person X: Encourages me to exercise. Prior to this study, three additional items were added to a 5-item scale developed in the past by the second author (; ). Refinements were made based on evaluative feedback from girls participating in focus groups conducted by the second author. Response choices included: (1) Never, (2) Rarely, (3) Sometimes, and (4) Often. Scores related to individual items for each of the three persons were summed and averaged. Cronbach’s α for the Social Support Scale was .86 when used in physical activity research with girls in the past (). Various forms of social support, as indicated by scale items assessing encouragement and transportation, were found to be related to physical activity (). For this study, the three additional items resulted in an increased Cronbach’s α of .93 (95% CI [.88, .96]).

Perceived importance

Girls responded to five single items to indicate their perceived importance of being someone who: (a) is a physically active person, (b) does physical activity, (c) is seen by others as being a physically active person, (d) is good at physical activities, and (e) has fun doing physical activities. The four response choices ranged from not at all important to very important. For this study, the Cronbach’s α was .74 (95% CI [.62, .82]).

Current physical activity self-definition

Girls responded to a single item measuring current physical activity self-definition. Four response choices ranged from: Does (now): not describe me at all to describes me a lot. The item was obtained from a current physical activity self-definition scale used in prior physical activity research in which a Cronbach’s α of .90 was reported (). For this study, a single item was used to decrease the overall survey length and reduce the related response burden. Because a single item represented the variable, internal consistency was not assessed. Analysis of unpublished survey data collected by the second author in the years 2005 – 2006 from 101 girls of middle school age resulted in two-week test-retest reliability of .79 for the current physical activity self-definition item.

Enjoyment

Enjoyment of physical activity was measured by girls’ responses to six items with choices ranging from not at all true to very true. Items were obtained from the 16-item Physical Activity Enjoyment Scale, a measure that demonstrated construct validity when used with adolescent girls (). The subset of six items was selected to decrease the survey length and response burden for young girls and exclude unclear items, particularly those with related response choices having double negatives. For this study, the Cronbach’s α was .78 (95% CI [.68, .85]).

Behavioral variables

Behavioral variables included physical activity and sedentary time, screen time, and eating habits. All were measured via survey, except for physical activity and sedentary time which were measured by the ActiGraph GT1M accelerometer (ActiGraph LLC, Pensacola, FL).

Physical activity

Minutes of light, moderate, and vigorous physical activity per hour were measured using accelerometers. These small devices record duration and intensity of physical activity and generate data from movement patterns in the form of accelerations, which are translated into “counts.” Counts were classified into light, moderate, and vigorous physical activity based on the following thresholds recommended for use with middle school-aged girls: 51-1499, 1500-2600, and greater than 2600 counts per 30 seconds, respectively (). Accelerometers recorded data at 30-second intervals.

Girls were asked to wear the accelerometer on an elastic belt at the right hip for seven consecutive days with the exception of showering, swimming, and sleeping at night. Data from girls who wore the accelerometer for eight or more hours per day on at least three weekdays and one weekend day were considered adequate for analysis (McMurray, Baggett, Harrell, Pennell, & Bangdiwala, 2004). Investigators downloaded the data using the ActiLife software program (ActiLife© 2011, ActiGraph; Pensacola, FL). Data were processed with a modified version of the Statistical Analysis System (SAS Institute Inc., Cary, NC) program created for the Trial of Activity for Adolescent Girls study () to calculate average minutes per hour of light, moderate, and vigorous physical activity.

Sedentary and screen time

Sedentary time was measured in minutes per hour using the accelerometers. Counts between 0 and 50 per 30 seconds indicated sedentary time. Twenty minutes or more of consecutive zeros was considered to be non-wear time and not included in the analysis ().

Screen time is defined as the amount of time spent engaging in television (TV) or video game viewing and computer use and considered to be a sedentary behavior (). Girls responded to four items about their typical screen time (TV or video game viewing; computer use) that did not involve schoolwork on both weekdays and weekends. Six response choices ranged from I do not (the specific behavior) to 5 or more hours per day. Cronbach’s α for the four items was .67 (95% CI [.52, .78]). To create an overall screen time variable for the purposes of analysis, the investigators added the number of hours of TV or video game viewing to the time spent using the computer.

Eating habits

Girls were asked about their intake of sugar-sweetened beverages (punch, kool aid, sports drinks, fruit drinks, soda or pop, excluding “diet” soda or pop or 100% fruit juices such as orange, apple, grape or grapefruit juice); fruits and vegetables; fried foods at or from a fast food place (French fries, burgers, or chicken nuggets); and breakfast. Girls responded to one item about the number of medium-sized glasses or containers (8 - 12 fluid ounces) of sugar-sweetened beverages they drank during the past week or 7 days. A life-size picture of an actual measuring cup was provided to assist the girls in estimating the number of ounces. Response choices ranged from I did not drink sugar-sweetened beverages during the past 7 days to 4 or more glasses or containers per day.

Girls responded to two items to indicate how many servings of fruit and vegetables they had eaten during the past week or 7 days. Response choices ranged from I did not eat (fruit; vegetables) during the past 7 days to 4 or more times per day. Girls responded to one item worded: During the past month, about how many times per week did you eat fried foods at or from a fast food place? The six responses choices ranged from I did not eat any fried foods at or from a fast food place during the past month to 5 or more times per week. The final item was: During the past week or 7 days, about how often did you eat breakfast in the morning? Four response choices ranged from never to every morning.

BMI

Research assistants (RAs) completed two 8-hour days of training and became certified in height and weight measurements. The RAs followed procedures used in prior research with middle school-aged girls (; ). These procedures included ensuring privacy during the data gathering. Height was determined with shoes removed and measured to the nearest 0.1 cm using a portable stadiometer (Shorr Productions, Olney, MD; 1985). Two RAs verified that each girl was standing erect with head positioned in the Frankfort horizontal plane. The RAs measured height twice; if the two measurements differed by 1.0 cm or more, the measurement was repeated a third time.

The RAs measured weight to the nearest 0.1 kg using an electronic scale (Tanita Corporation, Arlington Heights, IL; 2005). Girls were asked to remove any extra layers of clothing. Girls stood barefoot on the scale, and two weight measurements were done. If the two weights differed by more than 0.5 kg, a third measurement was obtained. For both height and weight, the investigators averaged two measurements deemed to be acceptable within the defined parameters (1.0 cm for height; 0.5 kg for weight).

Height and weight were used to calculate each girl’s BMI-for-age weight status category and the corresponding percentile, as indicated by the CDC (2011). Categories were defined for underweight as a BMI less than the 5^th percentile, healthy weight as a BMI equal to or greater than the 5^th and less than the 85^th percentile, overweight as a BMI equal to or greater than the 85^th percentile and less than the 95^th percentile, and obese as a BMI equal to or greater than the 95^th percentile (CDC, 2011).

Procedures

The University Biomedical and Health Institutional Review Board and school district administrators approved the study. Investigators explained the nature of the study and procedures to the girls and their parents through information packages provided at the time of recruitment. Girls who were interested in the study returned the signed assent and parental permission forms to the school. At the time of data collection, small groups of enrolled girls met with trained RAs in the library or a large private room within the school to complete the questionnaires and physical measures, respectively. Immediately prior to administering the questionnaires and obtaining the physical measures, the investigators again explained the nature of the study and procedures to the girls.

Data Analysis

The data were analyzed using Predictive Analytics Software (PASW) for Windows version 19.0 (SPSS Inc., 2010, Chicago, IL). Preliminary analysis involved descriptive statistics and independent t-tests for variables by categories of girls who were non-obese versus those who were obese. Chi-square tests were used to examine variables by comparing girls in the following BMI-for-age weight status categories and percentiles: (a) healthy weight (greater than 5^th and less than 85^th) versus overweight plus obese (equal to or greater than 85^th), (b) overweight (equal to or greater than 85^th and less than 95^th) versus obese (equal to or greater than 95^th), and (c) non-obese (less than 95^th) versus obese (equal to or greater than 95^th). Bivariate analyses, specifically Pearson correlation coefficients, were used to test for associations between the demographic, cognitive, affective, and behavioral variables, and BMI.

Results

Total Sample Characteristics

Of the 73 girls participating in the study, 61 (83.6%) reported no involvement in sports, and 47 of 72 (65.3%) reported having no involvement in organized physical activity programs either at school or in the community. Thirty-six (49.3%) girls reported participating in zero days of school PE per week. Forty (54.8%) girls reported no involvement in sports or organized physical activity programs either at school or in the community. Table 1 presents information related to the demographic, cognitive, and affective variables for the total sample. Table 2 presents information related to the behavioral variables for the total sample.

Table 1

Sample Characteristics: Demographic, Cognitive, and Affective Variables (N = 73)

Table 2

Sample Characteristics: Behavioral Variables (N = 73)

Note. M (SD) = mean (standard deviation); min = minutes; hr = hour; # = number.

Demographic variables

The majority of girls were 11 to 12 years of age (n = 63, 86.3%), in the 6^th- grade (n = 50, 68.5%), and from races and ethnicities other than Non-Hispanic White (n = 57, 78.1%). The majority of parents or guardians (n = 56, 76.7%) reported that their daughters participated in the free or reduced-price lunch program.

Cognitive and affective variables

The girls’ mean scores for perceived benefits, physical activity self-efficacy, importance, current physical activity self-definition, and enjoyment were all higher than the scale midpoint of 2.5, indicating positive cognitive and affective perceptions related to these variables. Girls’ mean score for perceived barriers was less than the scale midpoint of 2.5, indicating, on the whole, that these obstacles did not hinder their physical activity. The girls’ mean scores for social support for physical activity were slightly higher than the scale midpoint of 36.0 indicating a moderate positive influence related to this variable.

Bosch Fla 206 Software Definition System

Behavioral variables

Forty-three (58.9%) girls provided adequate physical activity data for analysis by wearing the accelerometer for at least eight hours per day on three week days and one weekend day. Accelerometer wear time averaged 13.6 hours per day. Table 2 indicates that the average amount of time the girls engaged in MVPA was 0.8 minutes per hour (SD = 0.5) with a maximum of 3.0 minutes per hour.

Girls averaged 41.4 (SD = 6.2) minutes per hour of accelerometer-measured sedentary time (see Table 2). The percentage of girls who reported spending more than two hours of TV or video game viewing and computer use per weekday was 42.5% (n = 31) and 36.9% (n = 27), respectively. The percentage of girls who reported spending more than two hours per day of TV or video game viewing and computer use per weekend day was 38.3% (n = 28) and 28.7% (n = 21), respectively. Mean screen time, estimated by combining TV or video game viewing and computer use, was not significantly different (4.8 hours per day) on the weekdays than on the weekend (4.3 hours per day).

As shown in Table 2, for healthy dietary behaviors, the majority of girls reported drinking an average of less than one glass of sugar-sweetened beverages per day (n = 54, 74.1%) and eating breakfast daily (n = 45, 62.5%). For unhealthy dietary behaviors, the majority of girls reported consumption of fewer than two servings per day of fruits (n = 49, 67.0%) and vegetables (n = 58, 79.4%). Girls ate fewer vegetables than fruit servings per day (t[72] = 2.2, p = .03). Fried foods at or from a fast food place were consumed two times or more weekly by 44 (63.8%) girls.

BMI

According to the CDC (2011) guidelines, 43 (58.9%) of the 71 girls (two had missing data) were either overweight or obese. Of the 71 girls with sufficient data for calculating BMI, one (1.4%) was underweight, 27 (37.0%) were at a healthy weight, 15 (20.5%) were overweight, and 28 (38.4%) were obese.

Based on a 95% trimmed sample that removed one outlier, BMI was significantly correlated to several cognitive, affective, and behavioral variables but not with any demographic variables. As noted in Table 3, BMI was correlated positively with perceived barriers (r = 0.27, p = .03) and negatively with enjoyment of physical activity (r = −0.28, p = .02). BMI was correlated with several behavioral variables, including accelerometer-measured light physical activity (r = −0.35, p = .02), MVPA (r = −0.32, p = .04), vigorous physical activity (r = −0.35, p = .02) and sedentary time (r = 0.36, p = .02). None of the self-reported eating habits were correlated to BMI.

Table 3

Pearson Bivariate Correlations (r) Between Factors and BMI

Between-Group Differences

Demographic variables

For the between-group analyses, the one girl who was underweight was included in the healthy weight group. No statistically significant differences were found between girls who were (a) healthy weight versus overweight or obese and (b) overweight versus obese for age, grade, race, ethnicity and SES. When categorized by non-obese versus obese, no significant between-group differences were noted for any demographic variables.

Cognitive and affective variables

Independent t-tests indicated no statistically significant differences in the cognitive and affective variables between the 28 girls who were at a healthy weight versus the 43 girls who were overweight or obese except for current physical activity self-definition. Girls who were at a healthy weight reported higher current physical activity self-definition (M = 3.3, SD = 0.9) than those who were overweight or obese (M = 2.8, SD = 0.9; t[68] = 2.2, p = .03). Differences were found for enjoyment of physical activity between girls who were overweight (M = 3.5, SD = 0.6) and those who were obese (M = 3.0, SD = 0.6; t[38] = 2.6, p = .01). As noted in Table 4, when categorized by obesity, non-obese girls had significantly greater enjoyment of physical activity than obese girls (t[65] = 2.6, p = .01). No statistically significant differences were found between non-obese and obese girls in barriers to physical activity.

Table 4

Independent t-Tests for Variables by Girls’ Categories of Non-Obese (A) Versus Obese (B)

Behavioral variables

With regard to physical activity, sufficient accelerometer data was received from 14 girls who were at a healthy weight, 12 girls who were overweight, and 17 girls who were obese. No statistically significant differences were noted between girls who were at a healthy weight and those who were overweight or obese in minutes per hour of light, MVPA, or vigorous physical activity. The only statistically significant difference in accelerometer data was for vigorous physical activity between girls who were overweight (M = 0.14, SD = 0.13) and those who were obese (M = 0.04, SD = 0.06; t[13.9] = 2.5, p = .02). As indicated in Table 4, when compared to obese girls, those who were non-obese engaged in more light (t[41] = 2.1, p = .04) and vigorous physical activity (t[41] = 2.9, p = .01). Although girls who were obese participated in fewer minutes per hour of MVPA than those who were non-obese, the differences were not statistically significant.

No statistically significant differences were noted between girls who were (a) healthy weight versus overweight or obese, (b) overweight versus obese, and (c) non-obese versus obese in screen time on weekdays or weekends or eating habits. No statistically significant differences in sedentary time were noted between girls who were (a) healthy weight versus overweight or obese and (b) overweight versus obese. As noted in Table 4, girls who were non-obese had fewer minutes per hour of sedentary time than those who were obese (t[41] = −2.2, p = .04).

Discussion

The broad racial and ethnic diversity of the girls in the sample was representative of the urban school district, which is comprised of a student body that is 45.2% Black, 31.1% White, 15.2% Hispanic, and 5.3% other race (Federal Education Budget Project [FEBP], 2008). For this study, the percentage of parents or guardians (n = 56, 76.7%) reporting that their daughters participated in the free or reduced price lunch program was far greater than the state (37.1%) and school district (62.0%) averages (FEBP, 2008), indicating the low SES of the sample.

National data support the seriousness of the overweight and obesity problem in middle school-aged girls (). Although female sex is associated with higher BMI in childhood (), the high prevalence of obesity in girls of middle school age is even more disconcerting because obesity in adolescence has been reported to persist into adulthood (). Many of these girls are at high risk for future obesity-related health complications, such as diabetes () and cardiovascular disease ().

Findings from this study that obese girls had more accelerometer-measured sedentary time than non-obese girls are similar to those from a systematic review of girls, ages 12 through 18 year of age (). noted that screen time exceeding two hours daily was positively associated with BMI.

Contrary to other research, this study did not demonstrate that certain eating habits, such as intake of sugar-sweetened beverages, fruits, vegetables, fried fast foods, and breakfast, were significantly associated with obesity (; ). The majority of girls in this sample, however, demonstrated unhealthy eating habits such as drinking sugar-sweetened beverages and not eating daily breakfast, similar to studies of high school students (; ; ). Whether this occurrence contributed to the lack of difference noted between obese and non-obese girls’ eating habits is unclear.

The lack of statistically significant associations between the demographic variables and BMI is not surprising due to the small sample size and narrow age- and grade-specific criteria. Both increasing age and Non-White race have been shown to be significant predictors of obesity in a longitudinal study (). found an increase in obesity from 12.4% in kindergarten to 20.8% by eighth grade. Although SES has been noted to influence risk for obesity (), it was not a significant factor in this small, age- and grade-specific sample primarily comprised of girls of low SES.

For the cognitive and affective variables, the findings of a positive association between perceived barriers to physical activity and obesity, and a negative association between enjoyment of physical activity and obesity are similar to those noted in another study that involved girls of various ages participating in physical activity during a PE class (). Of importance in this study is that, compared to those who were non-obese, obese girls perceived physical activity to be less enjoyable, even though both groups had relatively high mean scores for perceived importance of physical activity.

Although girls in this study perceived that physical activity was important, they still failed to attain the recommended minutes of MVPA. Reasons for the discrepancy between perceived importance and lack of attainment of adequate MVPA are unknown. Similar to findings from another study (), girls who were obese participated in less accelerometer-measured light and vigorous physical activity and more sedentary time than non-obese girls. In this study, however, response bias related to physical activity was avoided by using accelerometers.

The ability to recruit low-active, racially, and ethnically diverse girls of low SES, many of whom had BMIs above the 85^th and 95^th percentile, was an important strength of the study. reported that recruiting Black students and those of low SES was difficult, and found that only 51 (8%) of the 618 Non-White girls of low SES who originally expressed interest in the study were recruited and retained.

In this study, recruitment materials indicated that the investigators were interested in girls who were not participating in school or community sports or organized physical activity programs on three or more days per week. This strategy resulted in the successful recruitment of a large number of overweight and obese girls to the planned intervention. The finding is important because it indicates that: (a) a high number of overweight or obese girls can be recruited to participate in a study even if they are not specifically targeted, and (b) investigators can avoid the stigma associated with recruiting only those who are overweight or obese, yet still achieve their objective. Another strength of the study involved the use of accelerometry, as opposed to self-report, to measure physical activity.

Despite notable strengths, generalizability of the findings was limited due to the small sample size and use of convenience sampling in only one urban school district. The small sample size could also be responsible for the lack of statistically significant findings noted when non-obese and obese girls were compared. In addition, the investigators had to rely on self-report from the girls for some information, such as screen time and eating habits. Self-report in middle school-aged girls has been shown to be somewhat unreliable for behaviors that are important to weight control ().

Nursing Implications

Nursing interventions to increase physical activity among middle school-aged girls are needed due to the high prevalence of overweight and obesity noted in this group. Interventions that encourage girls’ participation in sports, dance, or other physical activity programs and decrease sedentary time by reducing perceived barriers to physical activity and increasing enjoyment of the behavior may be beneficial. Each encounter with girls inherently provides an opportunity for nurses and nurse practitioners to screen for risk factors, overweight status, or sequelae associated with being overweight or obese. Nurses and nurse practitioners can inquire about a girl’s current patterns of physical activity and explore what activities she enjoys in physical education class or if the girl has a particular interest in athletics (). Barriers to physical activity and potential ways to overcome them can be discussed.

Nurses and nurse practitioners, particularly those in schools, can serve as role models of physical activity for girls of this age and can be instrumental in tailoring programs and changing policies to meet the needs of girls to help them increase their physical activity. Specific strategies to decrease barriers to physical activity and increase girls’ enjoyment of physical activity both during and outside the school day warrant continued investigation in order to underscore those that are most effective. Translation of evidence derived from research is the foundation of nursing practice and essential for effecting positive behavior change among girls.

Acknowledgements

The project was supported by Grant Number R21HL090705 from the National Heart, Lung, and Blood Institute (NHLBI) at the National Institutes of Health (NIH); PI: L. B. Robbins, Michigan State University College of Nursing. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NHLBI or NIH. The “Middle School Physical Activity Intervention for Girls” study was also funded by the Michigan State University (MSU) College of Nursing and MSU Families and Communities Together Coalition. The funding bodies did not have a role in or influence the various phases of the project, the writing of the manuscript, or the decision to submit it for publication. The first author, Melodee L. Vanden Bosch, received financial support for the research and/or authorship of this article from the MSU Graduate School and College of Nursing PhD Nurse Fellowship and the Michigan Nurse Corps funding provided by the Michigan Department of Community Health and Michigan Department of Labor and Economic Growth. The authors would like to thank the project manager, Ms. Stacey M. Wesolek, for her assistance with accessing information that contributed to the development of this manuscript.

Footnotes

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The authors declare no conflicts of interest.

Trial Registration: ClinicalTrials.gov Identifier NCT01351649

Contributor Information

Melodee L. Vanden Bosch, Grand Valley State University, Kirkhof College of Nursing, 376 CHS, 301 Michigan St NE, Grand Rapids, MI 49503, ude.usvg@mbnednav. Phone: 616-331-5773. FAX: 616-331-2510.

Lorraine B. Robbins, Michigan State University, College of Nursing, Bott Building for Nursing Education and Research, C-245, 1355 Bogue Street, East Lansing, MI, 48824. ude.usm@67nibbor.

Karin A. Pfeiffer, Michigan State University, Department of Kinesiology, College of Education, 27R IM Sports Circle, East Lansing, MI, 48824. ude.usm@pak.

Anamaria S. Kazanis, Michigan State University, College of Nursing, Bott Building for Nursing Education and Research, 1355 Bogue Street, East Lansing, MI, 48824. ude.usm@asinazak.

Kimberly S. Maier, Michigan State University, Measurement and Quantitative Methods Program, College of Education, 451 Erickson Hall, East Lansing, MI, 48824. ude.usm@reiamk.

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A self-driving car, also known as an autonomous car, driverless car, or robotic car,^[1][2][3] is a vehicle that is capable of sensing its environment and moving safely with little or no human input.^[1][4]

Self-driving cars combine a variety of sensors to perceive their surroundings, such as radar, lidar, sonar, GPS, odometry and inertial measurement units.^[1] Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant signage.^[5][6]

Long distance trucks are seen as being in the forefront of adopting and implementing the technology.^[7]

• 2Definitions
  • 2.5Classification
• 6Nature of the digital technology
• 9Fields of application
• 10Potential advantages
• 13Potential changes for different industries
• 14Incidents
• 15Policy implications
  • 15.2Legislation
• 20In fiction

History[edit]

Experiments have been conducted on automated driving systems (ADS) since at least the 1920s;^[8] trials began in the 1950s. The first semi-automated car was developed in 1977, by Japan's Tsukuba Mechanical Engineering Laboratory, which required specially marked streets that were interpreted by two cameras on the vehicle and an analog computer. The vehicle reached speeds up to 30 kilometres per hour (19 mph) with the support of an elevated rail.^[9][10]

The first truly autonomous cars appeared in the 1980s, with Carnegie Mellon University's Navlab^[11] and ALV^[12][13] projects funded by DARPA starting in 1984 and Mercedes-Benz and Bundeswehr University Munich's EUREKA Prometheus Project^[14] in 1987. By 1985, the ALV had demonstrated self-driving speeds on two-lane roads of 31 kilometres per hour (19 mph) with obstacle avoidance added in 1986 and off-road driving in day and nighttime conditions by 1987.^[15] A major milestone was achieved in 1995, with CMU'sNavLab 5 completing the first autonomous coast-to-coast drive of the United States. Of the 2,849 miles between Pittsburgh, PA and San Diego, CA, 2,797 miles were autonomous (98.2%), completed with an average speed of 63.8 miles per hour (102.3 km/h).^{[16][17][18][19]} From the 1960s through the second DARPA Grand Challenge in 2005, automated vehicle research in the U.S. was primarily funded by DARPA, the US Army, and the U.S. Navy, yielding incremental advances in speeds, driving competence in more complex conditions, controls, and sensor systems.^[20] Companies and research organizations have developed prototypes.^{[14][21][22][23][24][25][26][27][28]}

The U.S. allocated $650 million in 1991 for research on the National Automated Highway System, which demonstrated automated driving through a combination of automation, embedded in the highway with automated technology in vehicles and cooperative networking between the vehicles and with the highway infrastructure. The program concluded with a successful demonstration in 1997 but without clear direction or funding to implement the system on a larger scale.^[29] Partly funded by the National Automated Highway System and DARPA, the Carnegie Mellon University Navlab drove 4,584 kilometres (2,848 mi) across America in 1995, 4,501 kilometres (2,797 mi) or 98% of it autonomously.^[30] Navlab's record achievement stood unmatched for two decades until 2015 when Delphi improved it by piloting an Audi, augmented with Delphi technology, over 5,472 kilometres (3,400 mi) through 15 states while remaining in self-driving mode 99% of the time.^[31] In 2015, the US states of Nevada, Florida, California, Virginia, and Michigan, together with Washington, D.C., allowed the testing of automated cars on public roads.^[32]

In 2017, Audi stated that its latest A8 would be automated at speeds of up to 60 kilometres per hour (37 mph) using its 'Audi AI'. The driver would not have to do safety checks such as frequently gripping the steering wheel. The Audi A8 was claimed to be the first production car to reach level 3 automated driving, and Audi would be the first manufacturer to use laser scanners in addition to cameras and ultrasonic sensors for their system.^[33]

In November 2017, Waymo announced that it had begun testing driverless cars without a safety driver in the driver position;^[34] however, there was still an employee in the car.^[35] In October 2018, Waymo announced that its test vehicles had traveled in automated mode for over 10,000,000 miles (16,000,000 km), increasing by about 1,000,000 miles (1,600,000 kilometres) per month.^[36] In December 2018, Waymo was the first to commercialize a fully autonomous taxi service in the U.S.^[37]

Definitions[edit]

There is some inconsistency in the terminology used in the self-driving car industry. Various organizations have proposed to define an accurate and consistent vocabulary.

Such confusion has been documented in SAE J3016 which states that 'Some vernacular usages associate autonomous specifically with full driving automation (level 5), while other usages apply it to all levels of driving automation, and some state legislation has defined it to correspond approximately to any ADS at or above level 3 (or to any vehicle equipped with such an ADS).'

Words definition and safety considerations[edit]

Modern vehicles provide partly automated features such as keeping the car within its lane, speed controls or emergency braking. Nonetheless, differences remain between a fully autonomous self-driving car on one hand and driver assistance technologies on the other hand. According to the BBC, confusion between those concepts leads to deaths.^[38]

Association of British Insurers considers the usage of the word autonomous in marketing for modern cars to be dangerous because car ads make motorists think 'autonomous' and 'autopilot' means a vehicle can drive itself when they still rely on the driver to ensure safety. Technology alone still is not able to drive the car.

When some car makers suggest or claim vehicles are self-driving, when they are only partly automated, drivers risk becoming excessively confident, leading to crashes, while fully self-driving cars are still a long way off in the UK.^[39]

Autonomous vs. automated[edit]

Autonomous means self-governing.^[40] Many historical projects related to vehicle automation have been automated (made automatic) subject to a heavy reliance on artificial aids in their environment, such as magnetic strips. Autonomous control implies satisfactory performance under significant uncertainties in the environment and the ability to compensate for system failures without external intervention.^[40]

One approach is to implement communication networks both in the immediate vicinity (for collision avoidance) and farther away (for congestion management). Such outside influences in the decision process reduce an individual vehicle's autonomy, while still not requiring human intervention.

Wood et al. (2012) wrote, 'This Article generally uses the term 'autonomous,' instead of the term 'automated.' ' The term 'autonomous' was chosen 'because it is the term that is currently in more widespread use (and thus is more familiar to the general public). However, the latter term is arguably more accurate. 'Automated' connotes control or operation by a machine, while 'autonomous' connotes acting alone or independently. Most of the vehicle concepts (that we are currently aware of) have a person in the driver's seat, utilize a communication connection to the Cloud or other vehicles, and do not independently select either destinations or routes for reaching them. Thus, the term 'automated' would more accurately describe these vehicle concepts.'^[41] As of 2017, most commercial projects focused on automated vehicles that did not communicate with other vehicles or with an enveloping management regime.EuroNCAP defines autonomous in 'Autonomous Emergency Braking' as: 'the system acts independently of the driver to avoid or mitigate the accident.' which implies the autonomous system is not the driver.^[42]

Autonomous versus cooperative[edit]

To enable a car to travel without any driver embedded within the vehicle, some companies use a remote driver.

According to SAE J3016,

Some driving automation systems may indeed be autonomous if they perform all of their functions independently and self-sufficiently, but if they depend on communication and/or cooperation with outside entities, they should be considered cooperative rather than autonomous.

Self-driving car[edit]

PC Mag defines a self-driving car as 'A computer-controlled car that drives itself.'^[43] UCSUSA states that self-driving cars are 'cars or trucks in which human drivers are never required to take control to safely operate the vehicle. Also known as autonomous or 'driverless' cars, they combine sensors and software to control, navigate, and drive the vehicle.'^[44]

Classification[edit]

A classification system based on six different levels (ranging from fully manual to fully automated systems) was published in 2014 by SAE International, an automotive standardization body, as J3016, Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems.^[46][47] This classification system is based on the amount of driver intervention and attentiveness required, rather than the vehicle capabilities, although these are very loosely related. In the United States in 2013, the National Highway Traffic Safety Administration (NHTSA) released a formal classification system,^[48] but abandoned this system in favor of the SAE standard in 2016. Also in 2016, SAE updated its classification, called J3016_201609.^[49]

Levels of driving automation[edit]

In SAE's automation level definitions, 'driving mode' means 'a type of driving scenario with characteristic dynamic driving task requirements (e.g., expressway merging, high speed cruising, low speed traffic jam, closed-campus operations, etc.)'^[1][50]

• Level 0: Automated system issues warnings and may momentarily intervene but has no sustained vehicle control.
• Level 1 ('hands on'): The driver and the automated system share control of the vehicle. Examples are systems where the driver controls steering and the automated system controls engine power to maintain a set speed (Cruise Control) or engine and brake power to maintain and vary speed (Adaptive Cruise Control or ACC); and Parking Assistance, where steering is automated while speed is under manual control. The driver must be ready to retake full control at any time. Lane Keeping Assistance (LKA) Type II is a further example of level 1 self-driving.
• Level 2 ('hands off'): The automated system takes full control of the vehicle (accelerating, braking, and steering). The driver must monitor the driving and be prepared to intervene immediately at any time if the automated system fails to respond properly. The shorthand 'hands off' is not meant to be taken literally. In fact, contact between hand and wheel is often mandatory during SAE 2 driving, to confirm that the driver is ready to intervene.
• Level 3 ('eyes off'): The driver can safely turn their attention away from the driving tasks, e.g. the driver can text or watch a movie. The vehicle will handle situations that call for an immediate response, like emergency braking. The driver must still be prepared to intervene within some limited time, specified by the manufacturer, when called upon by the vehicle to do so.
• Level 4 ('mind off'): As level 3, but no driver attention is ever required for safety, e.g. the driver may safely go to sleep or leave the driver's seat. Self-driving is supported only in limited spatial areas (geofenced) or under special circumstances, like traffic jams. Outside of these areas or circumstances, the vehicle must be able to safely abort the trip, e.g. park the car, if the driver does not retake control.
• Level 5 ('steering wheel optional'): No human intervention is required at all. An example would be a robotic taxi.

In the formal SAE definition below, note in particular what happens in the shift from SAE 2 to SAE 3: the human driver no longer has to monitor the environment. This is the final aspect of the 'dynamic driving task' that is now passed over from the human to the automated system. At SAE 3, the human driver still has the responsibility to intervene when asked to do so by the automated system. At SAE 4 the human driver is relieved of that responsibility and at SAE 5 the automated system will never need to ask for an intervention.

Legal definition[edit]

In the District of Columbia (DC) code,

'Autonomous vehicle' means a vehicle capable of navigating District roadways and interpreting traffic-control devices without a driver actively operating any of the vehicle's control systems. The term 'autonomous vehicle' excludes a motor vehicle enabled with active safety systems or driver- assistance systems, including systems to provide electronic blind-spot assistance, crash avoidance, emergency braking, parking assistance, adaptive cruise control, lane-keep assistance, lane-departure warning, or traffic-jam and queuing assistance, unless the system alone or in combination with other systems enables the vehicle on which the technology is installed to drive without active control or monitoring by a human operator.

In the same district code, it is considered that:

An autonomous vehicle may operate on a public roadway; provided, that the vehicle:

• (1) Has a manual override feature that allows a driver to assume control of the autonomous vehicle at any time;
• (2) Has a driver seated in the control seat of the vehicle while in operation who is prepared to take control of the autonomous vehicle at any moment; and
• (3) Is capable of operating in compliance with the District's applicable traffic laws and motor vehicle laws and traffic control devices.

Semi-automated vehicles[edit]

Between manually driven vehicles (SAE Level 0) and fully autonomous vehicles (SAE Level 5), there are a variety of vehicle types that can be described to have some degree of automation. These are collectively known as semi-automated vehicles. As it could be a while before the technology and infrastructure are developed for full automation, it is likely that vehicles will have increasing levels of automation. These semi-automated vehicles could potentially harness many of the advantages of fully automated vehicles, while still keeping the driver in charge of the vehicle.

Technical challenges[edit]

There are different systems that help the self-driving car control the car. Systems that currently need improvement include the car navigation system, the location system, the electronic map, the map matching, the global path planning, the environment perception, the laser perception, the radar perception, the visual perception, the vehicle control, the perception of vehicle speed and direction, the vehicle control method.^[51]

The challenge for driverless car designers is to produce control systems capable of analyzing sensory data in order to provide accurate detection of other vehicles and the road ahead.^[52] Modern self-driving cars generally use Bayesiansimultaneous localization and mapping (SLAM) algorithms,^[53] which fuse data from multiple sensors and an off-line map into current location estimates and map updates. Waymo has developed a variant of SLAM with detection and tracking of other moving objects (DATMO), which also handles obstacles such as cars and pedestrians. Simpler systems may use roadside real-time locating system (RTLS) technologies to aid localization. Typical sensors include lidar, stereo vision, GPS and IMU.^[54][55] Control systems on automated cars may use Sensor Fusion, which is an approach that integrates information from a variety of sensors on the car to produce a more consistent, accurate, and useful view of the environment.^[56]

Driverless vehicles require some form of machine vision for the purpose of visual object recognition. Automated cars are being developed with deep neural networks,^[54] a type of deep learning architecture with many computational stages, or levels, in which neurons are simulated from the environment that activate the network.^[57] The neural network depends on an extensive amount of data extracted from real-life driving scenarios,^[54] enabling the neural network to 'learn' how to execute the best course of action.^[57]

In May 2018, researchers from MIT announced that they had built an automated car that can navigate unmapped roads.^[58] Researchers at their Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new system, called MapLite, which allows self-driving cars to drive on roads that they have never been on before, without using 3D maps. The system combines the GPS position of the vehicle, a 'sparse topological map' such as OpenStreetMap, (i.e. having 2D features of the roads only), and a series of sensors that observe the road conditions.^[59]

Heavy rainfall, hail, or snow could impede the car sensors.

Nature of the digital technology[edit]

Autonomous vehicles, as digital technology, have certain characteristics that distinguish them from other types of technologies and vehicles. Due to these characteristics, autonomous vehicles are able to be more transformative and agile to possible changes. The characteristics will be explained based on the following subjects: homogenization and decoupling, connectivity, reprogrammable and smart, digital traces and modularity.

Homogenization and decoupling[edit]

Homogenization comes from the fact that all digital information assumes the same form. During the ongoing evolution of the digital era, certain industry standards have been developed on how to store digital information and in what type of format. This concept of homogenization also applies to autonomous vehicles. In order for autonomous vehicles to perceive their surroundings, they have to use different techniques each with their own accompanying digital information (e.g. radar, GPS, motion sensors and computer vision). Due to homogenization, the digital information from these different techniques is stored in a homogeneous way. This implies that all digital information comes in the same form, which means their differences are decoupled, and digital information can be transmitted, stored and computed in a way that the vehicles and its operating system can better understand and act upon it. Homogenization also helps to exponentially increase the computing power of hard- and software (Moore's law) which also supports the autonomous vehicles to understand and act upon the digital information in a more cost-effective way, therefore lowering the marginal costs.;

Connectivity[edit]

Connectivity means that users of a certain digital technology can connect easily with other users, other applications or even (other) enterprises. In the case of autonomous vehicles, it is essential for them to connect with other 'devices' in order to function most effectively. Autonomous vehicles are equipped with communication systems which allow them to communicate with other autonomous vehicles and roadside units to provide them, amongst other things, with information about road work or traffic congestion. In addition, scientists believe that the future will have computer programs that connect and manage each individual autonomous vehicle as it navigates through an intersection. This type of connectivity must replace traffic lights and stop signs.^[60] These types of characteristics drive and further develop the ability of autonomous vehicles to understand and cooperate with other products and services (such as intersection computer systems) in the autonomous vehicles market. This could lead to a network of autonomous vehicles all using the same network and information available on that network. Eventually, this can lead to more autonomous vehicles using the network because the information has been validated through the usage of other autonomous vehicles. Such movements will strengthen the value of the network and is called network externalities.;

Reprogrammable[edit]

Another characteristic of autonomous vehicles is that the core product will have a greater emphasis on the software and its possibilities, instead of the chassis and its engine. This is because autonomous vehicles have software systems that drive the vehicle meaning that updates through reprogramming or editing the software can enhance the benefits of the owner (e.g. update in better distinguishing blind person vs. non-blind person so that the vehicle will take extra caution when approaching a blind person). A characteristic of this reprogrammable part of autonomous vehicles is that the updates need not only to come from the supplier, because through machine learning (smart) autonomous vehicles can generate certain updates and install them accordingly (e.g. new navigation maps or new intersection computer systems). These reprogrammable characteristics of the digital technology and the possibility of smart machine learning give manufacturers of autonomous vehicles the opportunity to differentiate themselves on software. This also implies that autonomous vehicles are never finished because the product can be continuously be improved.

Digital traces[edit]

Autonomous vehicles are equipped with different sorts of sensors and radars. As said, this allows them to connect and interoperate with computers from other autonomous vehicles and/or roadside units. This implies that autonomous vehicles leave digital traces when they connect or interoperate. The data that comes from these digital traces can be used to develop new (to be determined) products or updates to enhance autonomous vehicles' driving ability or safety.

Modularity[edit]

Traditional vehicles and their accompanying (traditional) technologies are manufactured as a product that will be complete, and unlike autonomous vehicles, they can only be improved if they are redesigned or reproduced. As said, autonomous vehicles are produced but due to their digital characteristics never finished. This is because autonomous vehicles are more modular since they are made up out of several modules which will be explained hereafter through a Layered Modular Architecture. The Layered Modular Architecture extends the architecture of purely physical vehicles by incorporating four loosely coupled layers of devices, networks, services and contents into Autonomous Vehicles. These loosely coupled layers can interact through certain standardized interfaces.

• (1) The first layer of this architecture consists of the device layer. This layer consists of the following two parts: logical capability and physical machinery. The physical machinery refers to the actual vehicle itself (e.g. chassis and carrosserie). When it comes to digital technologies, the physical machinery is accompanied by a logical capability layer in the form of operating systems that helps to guide the vehicles itself and make it autonomous. The logical capability provides control over the vehicle and connects it with the other layers.;
• (2) On top of the device layer comes the network layer. This layer also consists of two different parts: physical transport and logical transmission. The physical transport layer refers to the radars, sensors and cables of the autonomous vehicles which enable the transmission of digital information. Next to that, the network layer of autonomous vehicles also has a logical transmission which contains communication protocols and network standard to communicate the digital information with other networks and platforms or between layers. This increases the accessibility of the autonomous vehicles and enables the computational power of a network or platform.;
• (3) The service layer contains the applications and their functionalities that serves the autonomous vehicle (and its owners) as they extract, create, store and consume content with regards to their own driving history, traffic congestion, roads or parking abilities for example.; and
• (4) The final layer of the model is the contents layer. This layer contains the sounds, images and videos. The autonomous vehicles store, extract and use to act upon and improve their driving and understanding of the environment. The contents layer also provides metadata and directory information about the content's origin, ownership, copyright, encoding methods, content tags, geo-time stamps, and so on (Yoo et al., 2010).

The consequence of layered modular architecture of autonomous vehicles (and other digital technologies) is that it enables the emergence and development of platforms and ecosystems around a product and/or certain modules of that product. Traditionally, automotive vehicles were developed, manufactured and maintained by traditional manufacturers. Nowadays app developers and content creators can help to develop more comprehensive product experience for the consumers which creates a platform around the product of autonomous vehicles.

Human factor challenges[edit]

Self-driving cars are already exploring the difficulties of determining the intentions of pedestrians, bicyclists, and animals, and models of behavior must be programmed into driving algorithms. Human road users also have the challenge of determining the intentions of autonomous vehicles, where there is no driver with which to make eye contact or exchange hand signals. Drive.ai is testing a solution to this problem that involves LED signs mounted on the outside of the vehicle, announcing status such as 'going now, don't cross' vs. 'waiting for you to cross'.^[61]

Two human-factor challenges are important for safety. One is the handoff from automated driving to manual driving, which may become necessary due to unfavorable or unusual road conditions, or if the vehicle has limited capabilities. A sudden handoff could leave a human driver dangerously unprepared in the moment. In the long term, humans who have less practice at driving might have a lower skill level and thus be more dangerous in manual mode. The second challenge is known as risk compensation: as a system is perceived to be safer, instead of benefiting entirely from all of the increased safety, people engage in riskier behavior and enjoy other benefits. Semi-automated cars have been shown to suffer from this problem, for example with users of Tesla Autopilot ignoring the road and using electronic devices or other activities against the advice of the company that the car is not capable of being completely autonomous. In the near future, pedestrians and bicyclists may travel in the street in a riskier fashion if they believe self-driving cars are capable of avoiding them.

In order for people to buy self-driving cars and vote for the government to allow them on roads, the technology must be trusted as safe.^[62][63] Self-driving elevators were invented in 1900, but the high number of people refusing to use them slowed adoption for several decades until operator strikes increased demand and trust was built with advertising and features like the emergency stop button.^[64][65]

Testing[edit]

The testing of vehicles with varying degrees of automation can be carried out either physically, in a closed environment^[66] or, where permitted, on public roads (typically requiring a license or permit,^[67] or adhering to a specific set of operating principles),^[68] or in a virtual environment, i.e. using computer simulations.^[69][70]When driven on public roads, automated vehicles require a person to monitor their proper operation and 'take over' when needed. For example, New York state has strict requirements for the test driver, such that the vehicle can be corrected at all times by a licensed operator; highlighted by Cardian Cube Company's application and discussions with New York State officials and the NYS DMV.^[71]

Apple is currently testing self-driving cars, and has increased its fleet of test vehicles from three in April 2017, to 27 in January 2018,^[72] and 45 in March 2018.^[73]

Russian internet-company Yandex started to develop self-driving cars in 2016. In February 2018, they tested the prototype of an unmanned taxi on the streets of Moscow.^[74] In June 2018, a Yandex self-driving vehicle completed a 485 mile (780 km) trip on a federal highway from Moscow to Kazan, staying in autonomous mode for 99% of the time.^[75][76] In August 2018, Yandex-taxi began working with self-driving cars in the Russian town of Innopolis, and they plan to operate two unmanned vehicles with five stops within the town.^[77] In Las Vegas in January 2019, Yandex tested an unmanned vehicle for the first time outside Russia. Testing continued during the international Consumer Electronics Show between 8 and 11 January.^[78] Yandex received permission from the Israeli Ministry of Transport to test the company's unmanned vehicle on the public roads in 2019.^[79]

The progress of automated vehicles can be assessed by computing the average distance driven between 'disengagements', when the automated system is switched off, typically by the intervention of a human driver. In 2017, Waymo reported 63 disengagements over 352,545 miles (567,366 km) of testing, an average distance of 5,596 miles (9,006 km) between disengagements, the highest among companies reporting such figures. Waymo also traveled a greater total distance than any of the other companies. Their 2017 rate of 0.18 disengagements per 1,000 miles (1,600 km) was an improvement over the 0.2 disengagements per 1,000 miles (1,600 km) in 2016, and 0.8 in 2015. In March 2017, Uber reported an average of just 0.67 miles (1.08 km) per disengagement. In the final three months of 2017, Cruise (now owned by GM) averaged 5,224 miles (8,407 km) per disengagement over a total distance of 62,689 miles (100,888 km).^[80] In July 2018, the first electric driverless racing car, 'Robocar', completed a 1.8-kilometer track, using its navigation system and artificial intelligence.^[81]

Fields of application[edit]

Autonomous trucks[edit]

Transport systems[edit]

In Europe, cities in Belgium, France, Italy and the UK are planning to operate transport systems for automated cars,^[82][83][84] and Germany, the Netherlands, and Spain have allowed public testing in traffic. In 2015, the UK launched public trials of the LUTZ Pathfinder automated pod in Milton Keynes.^[85] Beginning in summer 2015, the French government allowed PSA Peugeot-Citroen to make trials in real conditions in the Paris area. The experiments were planned to be extended to other cities such as Bordeaux and Strasbourg by 2016.^[86] The alliance between French companies THALES and Valeo (provider of the first self-parking car system that equips Audi and Mercedes premi) is testing its own system.^[87] New Zealand is planning to use automated vehicles for public transport in Tauranga and Christchurch.^{[88][89][90][91]}

In China, Baidu and King Long produce automated minibus, a vehicle with 14 seats, but without driving seat. With 100 vehicles produced, 2018 will be the first year with commercial automated service in China. Those minibuses should be at level 4, that is driverless in closed roads.^[92][93]

Potential advantages[edit]

Safety[edit]

Driving safety experts predict that once driverless technology has been fully developed, traffic collisions (and resulting deaths and injuries and costs), caused by human error, such as delayed reaction time, tailgating, rubbernecking, and other forms of distracted or aggressive driving should be substantially reduced.^{[1][94][95][96][97]} Consulting firm McKinsey & Company estimated that widespread use of autonomous vehicles could 'eliminate 90% of all auto accidents in the United States, prevent up to US$190 billion in damages and health-costs annually and save thousands of lives'.^[98]

According to motorist website 'TheDrive.com' operated by Time magazine, none of the driving safety experts they were able to contact were able to rank driving under an autopilot system at the time (2017) as having achieved a greater level of safety than traditional fully hands-on driving, so the degree to which these benefits asserted by proponents will manifest in practice cannot be assessed.^[99] Confounding factors that could reduce the net effect on safety may include unexpected interactions between humans and partly or fully automated vehicles, or between different types of vehicle system; complications at the boundaries of functionality at each automation level (such as handover when the vehicle reaches the limit of its capacity); the effect of the bugs and flaws that inevitably occur in complex interdependent software systems; sensor or data shortcomings; and successful compromise by malicious interveners.

To help reduce the possibility of these confounding factors, some companies have begun to open-source parts of their driverless systems. Udacity for instance is developing an open-source software stack,^[100] and some companies are having similar approaches.^[101][102]

Welfare[edit]

Automated cars could reduce labor costs;^[103][104] relieve travelers from driving and navigation chores, thereby replacing behind-the-wheel commuting hours with more time for leisure or work;^[94][97] and also would lift constraints on occupant ability to drive, distracted and texting while driving, intoxicated, prone to seizures, or otherwise impaired.^{[105][106][107]} For the young, the elderly, people with disabilities, and low-income citizens, automated cars could provide enhanced mobility.^{[108][109][110]} The removal of the steering wheel—along with the remaining driver interface and the requirement for any occupant to assume a forward-facing position—would give the interior of the cabin greater ergonomic flexibility. Large vehicles, such as motorhomes, would attain appreciably enhanced ease of use.^[111]

Traffic[edit]

Additional advantages could include higher speed limits;^[112] smoother rides;^[113] and increased roadway capacity; and minimized traffic congestion, due to decreased need for safety gaps and higher speeds.^[114][115] Currently, maximum controlled-access highway throughput or capacity according to the U.S. Highway Capacity Manual is about 2,200 passenger vehicles per hour per lane, with about 5% of the available road space is taken up by cars. One study estimated that automated cars could increase capacity by 273% (≈8,200 cars per hour per lane). The study also estimated that with 100% connected vehicles using vehicle-to-vehicle communication, capacity could reach 12,000 passenger vehicles per hour (up 545% from 2,200 pc/h per lane) traveling safely at 120 km/h (75 mph) with a following gap of about 6 m (20 ft) of each other. Currently, at highway speeds drivers keep between 40 to 50 m (130 to 160 ft) away from the car in front. These increases in highway capacity could have a significant impact in traffic congestion, particularly in urban areas, and even effectively end highway congestion in some places.^[116] The ability for authorities to manage traffic flow would increase, given the extra data and driving behavior predictability^[117] combined with less need for traffic police and even road signage.

Lower costs[edit]

Safer driving is expected to reduce the costs of vehicle insurance.^[103][118]

Energy and environmental impacts[edit]

Vehicle automation can improve fuel economy of the car by optimizing the drive cycle.^[119] Reduced traffic congestion and the improvements in traffic flow due to widespread use of automated cars will translate into higher fuel efficiency.^[120] Additionally, self-driving cars will be able to accelerate and brake more efficiently, meaning higher fuel economy from reducing wasted energy typically associated with inefficient changes to speed. However, the improvement in vehicle energy efficiency does not necessarily translate to net reduction in energy consumption and positive environmental outcomes. It is expected that convenience of the automated vehicles encourages the consumers to travel more, and this induced demand may partially or fully offset the fuel efficiency improvement brought by automation.^[119] Overall, the consequences of vehicle automation on global energy demand and emissions are highly uncertain, and heavily depends on the combined effect of changes in consumer behavior, policy intervention, technological progress and vehicle technology.^[119]

Parking space[edit]

Manually driven vehicles are reported to be used only 4–5% of the time, and being parked and unused for the remaining 95–96% of the time.^[121][122] Autonomous vehicles could, on the other hand, be used continuously after it has reached its destination. This could dramatically reduce the need for parking space. For example, in Los Angeles, 14% of the land is used for parking alone,^[123] equivalent to some 17,020,594 square meters.^[124] This combined with the potential reduced need for road space due to improved traffic flow, could free up tremendous amounts of land in urban areas, which could then be used for parks, recreational areas, buildings, among other uses; making cities more livable.

Related effects[edit]

By reducing the (labor and other) cost of mobility as a service, automated cars could reduce the number of cars that are individually owned, replaced by taxi/pooling and other car-sharing services.^[125][126] This would also dramatically reduce the size of the automotive production industry, with corresponding environmental^[127] and economic effects. Assuming the increased efficiency is not fully offset by increases in demand, more efficient traffic flow could free roadway space for other uses such as better support for pedestrians and cyclists.

The vehicles' increased awareness could aid the police by reporting on illegal passenger behavior, while possibly enabling other crimes, such as deliberately crashing into another vehicle or a pedestrian.^[128] However, this may also lead to much expanded mass surveillance if there is wide access granted to third parties to the large data sets generated.

The future of passenger rail transport in the era of automated cars is not clear.^[129]

Potential limits or obstacles[edit]

The sort of hoped-for potential benefits from increased vehicle automation described may be limited by foreseeable challenges, such as disputes over liability (will each company operating a vehicle accept that they are its 'driver' and thus responsible for what their car does, or will some try to project this liability onto others who are not in control?),^[130][131] the time needed to turn over the existing stock of vehicles from non-automated to automated,^[132] and thus a long period of humans and autonomous vehicles sharing the roads, resistance by individuals to having to forfeit control of their cars,^[133] concerns about the safety of driverless in practice,^[134] and the implementation of a legal framework and consistent global government regulations for self-driving cars.^[135]

Other obstacles could include de-skilling and lower levels of driver experience for dealing with potentially dangerous situations and anomalies,^[136] ethical problems where an automated vehicle's software is forced during an unavoidable crash to choose between multiple harmful courses of action ('the trolley problem'),^{[137][138][139]} concerns about making large numbers of people currently employed as drivers unemployed (at the same time as many other alternate blue collar occupations may be undermined by automation), the potential for more intrusive mass surveillance of location, association and travel as a result of police and intelligence agency access to large data sets generated by sensors and pattern-recognition AI (making anonymous travel difficult), and possibly insufficient understanding of verbal sounds, gestures and non-verbal cues by police, other drivers or pedestrians.^[140]

Possible technological obstacles for automated cars are:

• Artificial Intelligence is still not able to function properly in chaotic inner-city environments.^[141]
• A car's computer could potentially be compromised, as could a communication system between cars.^{[142][143][144][145][146]}
• Susceptibility of the car's sensing and navigation systems to different types of weather (such as snow) or deliberate interference, including jamming and spoofing.^[140]
• Avoidance of large animals requires recognition and tracking, and Volvo found that software suited to caribou, deer, and elk was ineffective with kangaroos.^[147]
• Autonomous cars may require very high-quality specialised maps to operate properly. Where these maps may be out of date, they would need to be able to fall back to reasonable behaviors.
• Competition for the radio spectrum desired for the car's communication.^[148]
• Field programmability for the systems will require careful evaluation of product development and the component supply chain.^[146]
• Current road infrastructure may need changes for automated cars to function optimally.^[149]

Social challenges include:

• Government over-regulation, or even uncertainty about potential future regulation, may delay deployment of automated cars on the road.^[150]
• Employment – Companies working on the technology have an increasing recruitment problem in that the available talent pool has not grown with demand.^[151] As such, education and training by third-party organisations such as providers of online courses and self-taught community-driven projects such as DIY Robocars^[152] and Formula Pi have quickly grown in popularity, while university level extra-curricular programmes such as Formula Student Driverless^[153] have bolstered graduate experience. Industry is steadily increasing freely available information sources, such as code,^[154] datasets^[155] and glossaries^[156] to widen the recruitment pool.

Potential disadvantages[edit]

A direct impact of widespread adoption of automated vehicles is the loss of driving-related jobs in the road transport industry.^{[1][103][104][157]} There could be resistance from professional drivers and unions who are threatened by job losses.^[158] In addition, there could be job losses in public transit services and crash repair shops. The automobile insurance industry might suffer as the technology makes certain aspects of these occupations obsolete.^[110] A frequently cited paper by Michael Osborne and Carl Benedikt Frey found that automated cars would make many jobs redundant.^[159]

Privacy could be an issue when having the vehicle's location and position integrated into an interface in which other people have access to.^[1][160] In addition, there is the risk of automotive hacking through the sharing of information through V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) protocols.^{[161][162][163]} There is also the risk of terrorist attacks. Self-driving cars could potentially be loaded with explosives and used as bombs.^[164]

The lack of stressful driving, more productive time during the trip, and the potential savings in travel time and cost could become an incentive to live far away from cities, where land is cheaper, and work in the city's core, thus increasing travel distances and inducing more urban sprawl, more fuel consumption and an increase in the carbon footprint of urban travel.^{[119][165][166]} There is also the risk that traffic congestion might increase, rather than decrease.^[119][110] Appropriate public policies and regulations, such as zoning, pricing, and urban design are required to avoid the negative impacts of increased suburbanization and longer distance travel.^[110][166]

Some^[who?] believe that once automation in vehicles reaches higher levels and becomes reliable, drivers will pay less attention to the road.^[167] Research shows that drivers in automated cars react later when they have to intervene in a critical situation, compared to if they were driving manually.^[168] Depending on the capabilities of automated vehicles and the frequency with which human intervention is needed, this may counteract any increase in safety, as compared to all-human driving, that may be delivered by other factors.

Ethical and moral reasoning come into consideration when programming the software that decides what action the car takes in an unavoidable crash; whether the automated car will crash into a bus, potentially killing people inside; or swerve elsewhere, potentially killing its own passengers or nearby pedestrians.^[169] A question that programmers of AI systems find difficult to answer (as do ordinary people, and ethicists) is 'what decision should the car make that causes the 'smallest' damage to people's lives?' One proposed solution is the implementation of ethics bots in self-driving vehicles, which learn from user preferences to ultimately guide autonomous instruments in accordance with the owner's values and preferences.^[170]

The ethics of automated vehicles are still being articulated, and may lead to controversy.^[171] They may also require closer consideration of the variability, context-dependency, complexity and non-deterministic nature of human ethics. Different human drivers make various ethical decisions when driving, such as avoiding harm to themselves, or putting themselves at risk to protect others. These decisions range from rare extremes such as self-sacrifice or criminal negligence, to routine decisions good enough to keep the traffic flowing but bad enough to cause accidents, road rage and stress.

Human thought and reaction time may sometimes be too slow to detect the risk of an upcoming fatal crash, think through the ethical implications of the available options, or take an action to implement an ethical choice. Whether a particular automated vehicle's capacity to correctly detect an upcoming risk, analyse the options or choose a 'good' option from among bad choices would be as good or better than a particular human's may be difficult to predict or assess. This difficulty may be in part because the level of automated vehicle system understanding of the ethical issues at play in a given road scenario, sensed for an instant from out of a continuous stream of synthetic physical predictions of the near future, and dependent on layers of pattern recognition and situational intelligence, may be opaque to human inspection because of its origins in probabilistic machine learning rather than a simple, plain English 'human values' logic of parsable rules. The depth of understanding, predictive power and ethical sophistication needed will be hard to implement, and even harder to test or assess.

The scale of this challenge may have other effects. There may be few entities able to marshal the resources and AI capacity necessary to meet it, as well as the capital necessary to take an automated vehicle system to market and sustain it operationally for the life of a vehicle, and the legal and 'government affairs' capacity to deal with the potential for liability for a significant proportion of traffic accidents. This may have the effect of narrowing the number of different system opertors, and eroding the presently quite diverse global vehicle market down to a small number of system suppliers.

Potential changes for different industries[edit]

The traditional automobile industry is subject to changes driven by technology and market demands. These changes include breakthrough technological advances and when the market demands and adopts new technology quickly. In the rapid advance of both factors, the end of the era of incremental change was recognized. When the transition is made to a new technology, new entrants to the automotive industry present themselves, which can be distinguished as mobility providers such as Uber and Lyft, as well as tech giants such as Google and Nvidia. As new entrants to the industry arise, market uncertainty naturally occurs due to the changing dynamics. For example, the entrance of tech giants, as well as the alliances between them and traditional car manufacturers causes a variation in the innovation and production process of autonomous vehicles. Additionally, the entrance of mobility providers has caused ambiguous user preferences. As a result of the rise of mobility providers, the number of vehicles per capita has flatlined. In addition, the rise of the sharing economy also contributes to market uncertainty and causes forecasters to question whether private ownership of vehicles is still relevant as new transportation technology and mobility providers are becoming preferred among consumers.

Taxis[edit]

With the aforementioned ambiguous user preference regarding the private ownership of autonomous vehicles, it is possible that the current mobility provider trend will continue as it rises in popularity. Established providers such as Uber and Lyft are already significantly present within the industry, and it is likely that new entrants will enter when business opportunities arise.^[172]

Healthcare, car repair, and car insurance[edit]

With the increasing reliance of autonomous vehicles on interconnectivity and the availability of big data which is made usable in the form of real-time maps, driving decisions can be made much faster in order to prevent collisions. Numbers made available by the US government state that 94% of the vehicle accidents are due to human failures. As a result, major implications for the healthcare industry become apparent. Numbers from the National Safety Council on killed and injured people on U.S. roads multiplied by the average costs of a single incident reveal that an estimated 500-billion-dollar loss may be imminent for the US healthcare industry when autonomous vehicles are dominating the roads. It is likely the anticipated decrease in traffic accidents will positively contribute to the widespread acceptance of autonomous vehicles, as well as the possibility to better allocate healthcare resources. As collisions are less likely to occur, and the risk for human errors is reduced significantly, the repair industry will face an enormous reduction of work that has to be done on the reparation of car frames. Meanwhile, as the generated data of the autonomous vehicle is likely to predict when certain replaceable parts are in need of maintenance, car owners and the repair industry will be able to proactively replace a part that will fail soon. This 'Asset Efficiency Service' would implicate a productivity gain for the automotive repair industry. As fewer collisions implicate less money spent on repair costs, the role of the insurance industry is likely to be altered as well. It can be expected that the increased safety of transport due to autonomous vehicles will lead to a decrease in payouts for the insurers, which is positive for the industry, but fewer payouts may imply a demand drop for insurances in general. The insurance industry may have to create new insurance models in the near future to accommodate the changes. An unexpected disadvantage of the widespread acceptance of autonomous vehicles would be a reduction in organs available for transplant.^[173]

Rescue, emergency response, and military[edit]

The technique used in autonomous driving also ensures life savings in other industries. The implementation of autonomous vehicles with rescue, emergency response, and military applications has already led to a decrease in deaths.^{[citation needed]} Military personnel use autonomous vehicles to reach dangerous and remote places on earth to deliver fuel, food and general supplies, and even rescue people. In addition, a future implication of adopting autonomous vehicles could lead to a reduction in deployed personnel, which will lead to a decrease in injuries, since the technological development allows Autonomous Vehicles (AVs) to become more and more autonomous. Another future implication is the reduction of emergency drivers when autonomous vehicles are deployed as fire trucks or ambulances. An advantage could be the use of real-time traffic information and other generated data to determine and execute routes more efficiently than human drivers. The time savings can be invaluable in these situations.^[174]

Interior design and entertainment[edit]

For the interior design industry, there are exciting times ahead. The driver is decreasingly focused on the actual driving, this implies that the interior design- and media-entertainment industry has to reconsider what passengers of autonomous vehicles are doing when they are on the road. Vehicles need to be redesigned, and possibly even be prepared for multipurpose usage. In practice, it will show that travelers have more time for business and/or leisure. In both cases, this gives increasing opportunities for the media-entertainment industry to demand attention. Moreover, the advertisement business is able to provide location based ads without risking driver safety.^[175]

Telecommunication and energy[edit]

All cars can benefit from information and connections, but autonomous cars “Will be fully capable of operating without C-V2X.'^[176] In addition, the earlier mentioned entertainment industry is also highly dependent on this network to be active in this market segment. This implies higher revenues for the telecommunication industry.

Since many autonomous vehicles are going to rely on electricity to operate, the demand for lithium batteries increases. Similarly, radar, sensors, lidar, and high-speed internet connectivity require higher auxiliary power from vehicles, which manifests as greater power draw from batteries.^[119] The larger battery requirement causes a necessary increase in supply of these type of batteries for the chemical industry. On the other hand, with the expected increase of battery powered (autonomous) vehicles, the petroleum industry is expected to undergo a decline in demand. As this implication depends on the adoption rate of autonomous vehicles, it is unsure to what extent this implication will disrupt this particular industry. This transition phase of oil to electricity allows companies to explore whether there are business opportunities for them in the new energy ecosystem.

Restaurant, hotels, and airlines[edit]

Driver interactions with the vehicle will be less common within the near future, and in the more distant future the responsibility will lie entirely with the vehicle. As indicated above, this will have implications for the entertainment- and interior design industry. For roadside restaurants, the implication will be that the need for customers to stop driving and enter the restaurant will vanish, and the autonomous vehicle will have a double function. Moreover, accompanied with the rise of disruptive platforms such as Airbnb that have shaken up the hotel industry, the fast increase of developments within the autonomous vehicle industry might cause another implication for their customer bases. In the more distant future, the implication for motels might be that a decrease in guests will occur, since autonomous vehicles could be redesigned as fully equipped bedrooms. The improvements regarding the interior of the vehicles might additionally have implications for the airline industry. In the case of relatively short-haul flights, waiting times at customs or the gate imply lost time and hassle for customers. With the improved convenience in future car travel, it is possible that customers might go for this option, causing a loss in customer bases for airline industry.^[177]

Elderly, disabled, and children[edit]

Autonomous vehicles will have a severe impact on the mobility options of persons that are not able to drive a vehicle themselves. To remain socially engaged with society or even able to do groceries, the elderly people of today are depending on caretakers to drive them to these places. In addition to the perceived freedom of the elderly people of the future, the demand for human aides will decrease. When we also consider the increased health of the elderly, it is safe to state that care centers will experience a decrease in the number of clients. Not only elderly people face difficulties of their decreased physical abilities, also disabled people will perceive the benefits of autonomous vehicles in the near future, causing their dependency on caretakers to decrease. Both industries are largely depending on informal caregivers, who are mostly relatives of the persons in need. Since there is less of a reliance on their time, employers of informal caregivers or even governments will experience a decrease of costs allocated to this matter. Children and teens, who are not able to drive a vehicle themselves, are also benefiting of the introduction of autonomous cars. Daycares and schools are able to come up with automated pick up and drop off systems, causing a decrease of reliance on parents and childcare workers. The extent to which human actions are necessary for driving will vanish. Since current vehicles require human actions to some extent, the driving school industry will not be disrupted until the majority of autonomous transportation is switched to the emerged dominant design. It is plausible that in the distant future driving a vehicle will be considered as a luxury, which implies that the structure of the industry is based on new entrants and a new market.^[178]

Incidents[edit]

Tesla Autopilot[edit]

In mid‑October 2015, Tesla Motors rolled out version 7 of their software in the U.S. that included Tesla Autopilot capability.^[179] On 9 January 2016, Tesla rolled out version 7.1 as an over-the-air update, adding a new 'summon' feature that allows cars to self-park at parking locations without the driver in the car.^[180] Tesla's automated driving features is currently classified as a level 2 driver assistance system according to the Society of Automotive Engineers' (SAE International) five levels of vehicle automation. At this level the car can be automated but requires the full attention of the driver, who must be prepared to take control at a moment's notice.^{[181][182][183][184]} Autopilot should be used only on limited-access highways, and sometimes it will fail to detect lane markings and disengage itself. In urban driving the system will not read traffic signals or obey stop signs. The system also does not detect pedestrians or cyclists.^[185]

On 20 January 2016, the first known fatal crash of a Tesla with Autopilot occurred in China's Hubei province. According to China's 163.com news channel, this marked 'China's first accidental death due to Tesla's automatic driving (system)'. Initially, Tesla pointed out that the vehicle was so badly damaged from the impact that their recorder was not able to conclusively prove that the car had been on Autopilot at the time; however, 163.com pointed out that other factors, such as the car's absolute failure to take any evasive actions prior to the high speed crash, and the driver's otherwise good driving record, seemed to indicate a strong likelihood that the car was on Autopilot at the time. A similar fatal crash occurred four months later in Florida.^[186][187] In 2018, in a subsequent civil suit between the father of the driver killed and Tesla, Tesla did not deny that the car had been on Autopilot at the time of the accident, and sent evidence to the victim's father documenting that fact.^[188]

The second known fatal accident involving a vehicle being driven by itself took place in Williston, Florida on 7 May 2016 while a Tesla Model S electric car was engaged in Autopilot mode. The occupant was killed in a crash with an 18-wheel tractor-trailer. On 28 June 2016 the National Highway Traffic Safety Administration (NHTSA) opened a formal investigation into the accident working with the Florida Highway Patrol. According to the NHTSA, preliminary reports indicate the crash occurred when the tractor-trailer made a left turn in front of the Tesla at an intersection on a non-controlled access highway, and the car failed to apply the brakes. The car continued to travel after passing under the truck's trailer.^[189][190] The NHTSA's preliminary evaluation was opened to examine the design and performance of any automated driving systems in use at the time of the crash, which involved a population of an estimated 25,000 Model S cars.^[191] On 8 July 2016, the NHTSA requested Tesla Motors provide the agency detailed information about the design, operation and testing of its Autopilot technology. The agency also requested details of all design changes and updates to Autopilot since its introduction, and Tesla's planned updates schedule for the next four months.^[192]

According to Tesla, 'neither autopilot nor the driver noticed the white side of the tractor-trailer against a brightly lit sky, so the brake was not applied.' The car attempted to drive full speed under the trailer, 'with the bottom of the trailer impacting the windshield of the Model S'. Tesla also claimed that this was Tesla's first known autopilot death in over 130 million miles (210 million kilometers) driven by its customers with Autopilot engaged, however by this statement, Tesla was apparently refusing to acknowledge claims that the January 2016 fatality in Hubei China had also been the result of an autopilot system error. According to Tesla there is a fatality every 94 million miles (151 million kilometers) among all type of vehicles in the U.S.^{[189][190][193]} However, this number also includes fatalities of the crashes, for instance, of motorcycle drivers with pedestrians.^[194][195]

In July 2016, the U.S. National Transportation Safety Board (NTSB) opened a formal investigation into the fatal accident while the Autopilot was engaged. The NTSB is an investigative body that has the power to make only policy recommendations. An agency spokesman said 'It's worth taking a look and seeing what we can learn from that event, so that as that automation is more widely introduced we can do it in the safest way possible.'^[196] In January 2017, the NTSB released the report that concluded Tesla was not at fault; the investigation revealed that for Tesla cars, the crash rate dropped by 40 percent after Autopilot was installed.^[197]

According to Tesla, starting 19 October 2016, all Tesla cars are built with hardware to allow full self-driving capability at the highest safety level (SAE Level 5).^[198] The hardware includes eight surround cameras and twelve ultrasonic sensors, in addition to the forward-facing radar with enhanced processing capabilities.^[199] The system will operate in 'shadow mode' (processing without taking action) and send data back to Tesla to improve its abilities until the software is ready for deployment via over-the-air upgrades.^[200] After the required testing, Tesla hopes to enable full self-driving by the end of 2019 under certain conditions.

Waymo[edit]

Waymo originated as a self-driving car project within Google. In August 2012, Google announced that their vehicles had completed over 300,000 automated-driving miles (500,000 km) accident-free, typically involving about a dozen cars on the road at any given time, and that they were starting to test with single drivers instead of in pairs.^[201] In late-May 2014, Google revealed a new prototype that had no steering wheel, gas pedal, or brake pedal, and was fully automated .^[202] As of March 2016, Google had test-driven their fleet in automated mode a total of 1,500,000 mi (2,400,000 km).^[203] In December 2016, Google Corporation announced that its technology would be spun off to a new company called Waymo, with both Google and Waymo becoming subsidiaries of a new parent company called Alphabet.^[204][205]

According to Google's accident reports as of early 2016, their test cars had been involved in 14 collisions, of which other drivers were at fault 13 times, although in 2016 the car's software caused a crash.^[206]

In June 2015, Brin confirmed that 12 vehicles had suffered collisions as of that date. Eight involved rear-end collisions at a stop sign or traffic light, two in which the vehicle was side-swiped by another driver, one in which another driver rolled through a stop sign, and one where a Google employee was controlling the car manually.^[207] In July 2015, three Google employees suffered minor injuries when their vehicle was rear-ended by a car whose driver failed to brake at a traffic light. This was the first time that a collision resulted in injuries.^[208] On 14 February 2016 a Google vehicle attempted to avoid sandbags blocking its path. During the maneuver it struck a bus. Google stated, 'In this case, we clearly bear some responsibility, because if our car hadn't moved, there wouldn't have been a collision.'^[209][210] Google characterized the crash as a misunderstanding and a learning experience. No injuries were reported in the crash.^[206]

Uber[edit]

In March 2017, an Uber test vehicle was involved in a crash in Tempe, Arizona when another car failed to yield, flipping the Uber vehicle. There were no injuries in the accident.^[211]

By 22 December 2017, Uber had completed 2 million miles (3.2 million kilometers) in automated mode.^[212]

On 18 March 2018, Elaine Herzberg became the first pedestrian to be killed by a self-driving car in the United States after being hit by an Uber vehicle, also in Tempe. Herzberg was crossing outside of a crosswalk, approximately 400 feet from an intersection.^[213] This marks the first time an individual outside an auto-piloted car is known to have been killed by such a car.

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The first death of an essentially uninvolved third party is likely to raise new questions and concerns about the safety of automated cars in general.^[214] Some experts say a human driver could have avoided the fatal crash.^[215] Arizona Governor Doug Ducey later suspended the company's ability to test and operate its automated cars on public roadways citing an 'unquestionable failure' of the expectation that Uber make public safety its top priority.^[216] Uber has pulled out of all self-driving-car testing in California as a result of the accident.^[217] On 24 May 2018 the National Transport Safety Board issued a preliminary report.^[218]

Navya automated bus driving system[edit]

On 9 November 2017, a Navya automated self-driving bus with passengers was involved in a crash with a truck. The truck was found to be at fault of the crash, reversing into the stationary automated bus. The automated bus did not take evasive actions or apply defensive driving techniques such as flashing its headlights, or sounding the horn. As one passenger commented, 'The shuttle didn't have the ability to move back. The shuttle just stayed still.'^[219]

Policy implications[edit]

Urban planning[edit]

According to a Wonkblog reporter, if fully automated cars become commercially available, they have the potential to be a disruptive innovation with major implications for society. The likelihood of widespread adoption is still unclear, but if they are used on a wide scale, policy makers face a number of unresolved questions about their effects.^[149]

One fundamental question is about their effect on travel behavior. Some people believe that they will increase car ownership and car use because it will become easier to use them and they will ultimately be more useful.^[149] This may, in turn, encourage urban sprawl and ultimately total private vehicle use. Others argue that it will be easier to share cars and that this will thus discourage outright ownership and decrease total usage, and make cars more efficient forms of transportation in relation to the present situation.^[220]

Policy-makers will have to take a new look at how infrastructure is to be built and how money will be allotted to build for automated vehicles. The need for traffic signals could potentially be reduced with the adoption of smart highways.^[221] Due to smart highways and with the assistance of smart technological advances implemented by policy change, the dependence on oil imports may be reduced because of less time being spent on the road by individual cars which could have an effect on policy regarding energy.^[222] On the other hand, automated vehicles could increase the overall number of cars on the road which could lead to a greater dependence on oil imports if smart systems are not enough to curtail the impact of more vehicles.^[223] However, due to the uncertainty of the future of automated vehicles, policy makers may want to plan effectively by implementing infrastructure improvements that can be beneficial to both human drivers and automated vehicles.^[224] Caution needs to be taken in acknowledgment to public transportation and that the use may be greatly reduced if automated vehicles are catered to through policy reform of infrastructure with this resulting in job loss and increased unemployment.^[225]

Other disruptive effects will come from the use of automated vehicles to carry goods. Self-driving vans have the potential to make home deliveries significantly cheaper, transforming retail commerce and possibly making hypermarkets and supermarkets redundant. As of right now the U.S. Government defines automation into six levels, starting at level zero which means the human driver does everything and ending with level five, the automated system performs all the driving tasks. Also under the current law, manufacturers bear all the responsibility to self-certify vehicles for use on public roads. This means that currently as long as the vehicle is compliant within the regulatory framework, there are no specific federal legal barriers to a highly automated vehicle being offered for sale. Iyad Rahwan, an associate professor in the MIT Media lab said, 'Most people want to live in a world where cars will minimize casualties, but everyone wants their own car to protect them at all costs.' Furthermore, industry standards and best practice are still needed in systems before they can be considered reasonably safe under real-world conditions.^[226]

Legislation[edit]

The 1968 Vienna Convention on Road Traffic, subscribed to by over 70 countries worldwide, establishes principles to govern traffic laws. One of the fundamental principles of the Convention has been the concept that a driver is always fully in control and responsible for the behavior of a vehicle in traffic.^[227] The progress of technology that assists and takes over the functions of the driver is undermining this principle, implying that much of the groundwork must be rewritten.

Legal status in the United States[edit]

In the United States, a non-signatory country to the Vienna Convention, state vehicle codes generally do not envisage — but do not necessarily prohibit — highly automated vehicles.^[228][229] To clarify the legal status of and otherwise regulate such vehicles, several states have enacted or are considering specific laws.^[230] In 2016, 7 states (Nevada, California, Florida, Michigan, Hawaii, Washington, and Tennessee), along with the District of Columbia, have enacted laws for automated vehicles. Incidents such as the first fatal accident by Tesla's Autopilot system have led to discussion about revising laws and standards for automated cars.

In September 2016, the US National Economic Council and Department of Transportation released federal standards that describe how automated vehicles should react if their technology fails, how to protect passenger privacy, and how riders should be protected in the event of an accident. The new federal guidelines are meant to avoid a patchwork of state laws, while avoiding being so overbearing as to stifle innovation.^[231]

In June 2011, the Nevada Legislature passed a law to authorize the use of automated cars. Nevada thus became the first jurisdiction in the world where automated vehicles might be legally operated on public roads. According to the law, the Nevada Department of Motor Vehicles (NDMV) is responsible for setting safety and performance standards and the agency is responsible for designating areas where automated cars may be tested.^{[232][233][234]} This legislation was supported by Google in an effort to legally conduct further testing of its Google driverless car.^[235] The Nevada law defines an automated vehicle to be 'a motor vehicle that uses artificial intelligence, sensors and global positioning system coordinates to drive itself without the active intervention of a human operator'. The law also acknowledges that the operator will not need to pay attention while the car is operating itself. Google had further lobbied for an exemption from a ban on distracted driving to permit occupants to send text messages while sitting behind the wheel, but this did not become law.^{[235][236][237]} Furthermore, Nevada's regulations require a person behind the wheel and one in the passenger's seat during tests.^[238]

In April 2012, Florida became the second state to allow the testing of automated cars on public roads,^[239] and California became the third when Governor Jerry Brown signed the bill into law at Google Headquarters in Mountain View.^[240] In December 2013, Michigan became the fourth state to allow testing of driverless cars on public roads.^[241] In July 2014, the city of Coeur d'Alene, Idaho adopted a robotics ordinance that includes provisions to allow for self-driving cars.^[242]

On 19 February 2016, Assembly Bill No. 2866 was introduced in California that would allow automated vehicles to operate on the road, including those without a driver, steering wheel, accelerator pedal, or brake pedal. The Bill states the Department of Motor Vehicles would need to comply with these regulations by 1 July 2018 for these rules to take effect. This bill has yet to pass the house of origin.^[243]

In September 2016, the U.S. Department of Transportation released its Federal Automated Vehicles Policy,^[244] and California published discussions on the subject in October 2016.^[245]

In December 2016, the California Department of Motor Vehicles ordered Uber to remove its self-driving vehicles from the road in response to two red-light violations. Uber immediately blamed the violations on 'human-error', and has suspended the drivers.^[246]

Legislation in Europe[edit]

In 2013, the government of the United Kingdom permitted the testing of automated cars on public roads.^[247] Before this, all testing of robotic vehicles in the UK had been conducted on private property.^[247]

In 2014, the Government of France announced that testing of automated cars on public roads would be allowed in 2015. 2000 km of road would be opened through the national territory, especially in Bordeaux, in Isère, Île-de-France and Strasbourg. At the 2015 ITS World Congress, a conference dedicated to intelligent transport systems, the very first demonstration of automated vehicles on open road in France was carried out in Bordeaux in early October 2015.^[248]

In 2015, a preemptive lawsuit against various automobile companies such as GM, Ford, and Toyota accused them of 'Hawking vehicles that are vulnerable to hackers who could hypothetically wrest control of essential functions such as brakes and steering.'^[249]

In spring of 2015, the Federal Department of Environment, Transport, Energy and Communications in Switzerland (UVEK) allowed Swisscom to test a driverless Volkswagen Passat on the streets of Zurich.^[250]

As of April 2017, it is possible to conduct public road tests for development vehicles in Hungary, furthermore the construction of a closed test track, the Zala Zone test track,^[251] suitable for testing highly automated functions is also under way near the city of Zalaegerszeg.^[252]

Legislation in Asia[edit]

In 2016, the Singapore Land Transit Authority in partnership with UK automotive supplier Delphi Automotive Plc will launch preparations for a test run of a fleet of automated taxis for an on-demand automated cab service to take effect in 2017.^[253]

Liability[edit]

Self-driving car liability is a developing area of law and policy that will determine who is liable when an automated car causes physical damage to persons, or breaks road rules.^[1][254] When automated cars shift the control of driving from humans to automated car technology, there may be a need for existing liability laws to evolve in order to fairly identify the parties responsible for damage and injury, and to address the potential for conflicts of interest between human occupants, system operator, insurers, and the public purse.^[110] Increases in the use of automated car technologies (e.g. advanced driver-assistance systems) may prompt incremental shifts in this responsibility for driving. It is claimed by proponents to have potential to affect the frequency of road accidents, although it is difficult to assess this claim in the absence of data from substantial actual use.^[255] If there was a dramatic improvement in safety, the operators may seek to project their liability for the remaining accidents onto others as part of their reward for the improvement. However, there is no obvious reason why they should escape liability if any such effects were found to be modest or nonexistent, since part of the purpose of such liability is to give an incentive to the party controlling something to do whatever is necessary to avoid it causing harm. Potential users may be reluctant to trust an operator if it seeks to pass its normal liability on to others.

In any case, a well-advised person who is not controlling a car at all (Level 5) would be understandably reluctant to accept liability for something out of their control. And when there is some degree of sharing control possible (Level 3 or 4), a well-advised person would be concerned that the vehicle might try to pass back control at the last seconds before an accident, to pass responsibility and liability back too, but in circumstances where the potential driver has no better prospects of avoiding the crash than the vehicle, since they have not necessarily been paying close attention, and if it is too hard for the very smart car it might be too hard for a human. Since operators, especially those familiar with trying to ignore existing legal obligations (under a motto like 'seek forgiveness, not permission'), such as Waymo or Uber, could be normally expected to try to avoid responsibility to the maximum degree possible, there is potential for attempt to let the operators evade being held liable for accidents while they are in control.

As higher levels of automation are commercially introduced (level 3 and 4), the insurance industry may see a greater proportion of commercial and product liability lines while personal automobile insurance shrinks.^[256]

Vehicular communication systems[edit]

Vehicle networking may be desirable due to difficulty with computer vision being able to recognize brake lights, turn signals, buses, and similar things. However, the usefulness of such systems would be diminished by the fact current cars are equipped with them; they may also pose privacy concerns.^{[citation needed]}

Individual vehicles may benefit from information obtained from other vehicles in the vicinity, especially information relating to traffic congestion and safety hazards. Vehicular communication systems use vehicles and roadside units as the communicating nodes in a peer-to-peer network, providing each other with information. As a cooperative approach, vehicular communication systems can allow all cooperating vehicles to be more effective. According to a 2010 study by the National Highway Traffic Safety Administration, vehicular communication systems could help avoid up to 79 percent of all traffic accidents.^[257]

There have so far been no complete implementation of peer-to-peer networking on the scale required for traffic: each individual vehicle would have to connect with potentially hundreds of different vehicles that could be going in and out of range.^{[citation needed]}

In 2012, computer scientists at the University of Texas in Austin began developing smart intersections designed for automated cars. The intersections will have no traffic lights and no stop signs, instead using computer programs that will communicate directly with each car on the road.^[258]

In 2017, Researchers from Arizona State University developed a 1/10 scale intersection and proposed an intersection management technique called Crossroads. It was shown that Crossroads is very resilient to network delay of both V2I communication and Worst-case Execution time of the intersection manager.^[259] In 2018, a robust approach was introduced which is resilient to both model mismatch and external disturbances such as wind and bumps.^[260]

Among connected cars, an unconnected one is the weakest link and will be increasingly banned from busy high-speed roads, predicted a Helsinki think tank in January 2016.^[261]

Public opinion surveys[edit]

In a 2011 online survey of 2,006 US and UK consumers by Accenture, 49% said they would be comfortable using a 'driverless car'.^[262]

A 2012 survey of 17,400 vehicle owners by J.D. Power and Associates found 37% initially said they would be interested in purchasing a 'fully autonomous car'. However, that figure dropped to 20% if told the technology would cost $3,000 more.^[263]

In a 2012 survey of about 1,000 German drivers by automotive researcher Puls, 22% of the respondents had a positive attitude towards these cars, 10% were undecided, 44% were skeptical and 24% were hostile.^[264]

A 2013 survey of 1,500 consumers across 10 countries by Cisco Systems found 57% 'stated they would be likely to ride in a car controlled entirely by technology that does not require a human driver', with Brazil, India and China the most willing to trust automated technology.^[265]

In a 2014 US telephone survey by Insurance.com, over three-quarters of licensed drivers said they would at least consider buying a self-driving car, rising to 86% if car insurance were cheaper. 31.7% said they would not continue to drive once an automated car was available instead.^[266]

In a February 2015 survey of top auto journalists, 46% predict that either Tesla or Daimler will be the first to the market with a fully autonomous vehicle, while (at 38%) Daimler is predicted to be the most functional, safe, and in-demand autonomous vehicle.^[267]

In 2015 a questionnaire survey by Delft University of Technology explored the opinion of 5,000 people from 109 countries on automated driving. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. 22% of the respondents did not want to spend any money for a fully automated driving system. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. Finally, respondents from more developed countries (in terms of lower accident statistics, higher education, and higher income) were less comfortable with their vehicle transmitting data.^[268] The survey also gave results on potential consumer opinion on interest of purchasing an automated car, stating that 37% of surveyed current owners were either 'definitely' or 'probably' interested in purchasing an automated car.^[268]

In 2016, a survey in Germany examined the opinion of 1,603 people, who were representative in terms of age, gender, and education for the German population, towards partially, highly, and fully automated cars. Results showed that men and women differ in their willingness to use them. Men felt less anxiety and more joy towards automated cars, whereas women showed the exact opposite. The gender difference towards anxiety was especially pronounced between young men and women but decreased with participants' age.^[269]

In 2016, a PwC survey, in the United States, showing the opinion of 1,584 people, highlights that '66 percent of respondents said they think autonomous cars are probably smarter than the average human driver'. People are still worried about safety and mostly the fact of having the car hacked. Nevertheless, only 13% of the interviewees see no advantages in this new kind of cars.^[270]

A Pew Research Center survey of 4,135 U.S. adults conducted 1–15 May 2017 finds that many Americans anticipate significant impacts from various automation technologies in the course of their lifetimes—from the widespread adoption of automated vehicles to the replacement of entire job categories with robot workers.^[271]

Results from two opinion surveys of 54 and 187 US adults respectively were published in 2019. A new standardised questionnaire, the autonomous vehicle acceptance model (AVAM) was developed, including additional description to help respondents better understand the implications of different automation levels. Results showed that users were less accepting of high autonomy levels and displayed significantly lower intention to use highly autonomous vehicles. Additionally, partial autonomy (regardless of level) was perceived as requiring uniformly higher driver engagement (usage of hands, feet and eyes) than full autonomy.^[272]

Moral issues[edit]

With the emergence of automated automobiles, various ethical issues arise. While the introduction of automated vehicles to the mass market is said to be inevitable due to a (presumed but untestable) potential for reduction of crashes by 'up to' 90%^[273] and their potential greater accessibility to disabled, elderly, and young passengers, a range of ethical issues have not been fully addressed. Those include, but are not limited to: the moral, financial, and criminal responsibility for crashes and breaches of law; the decisions a car is to make right before a (fatal) crash; privacy issues including potential for mass surveillance; potential for massive job losses and unemployment among drivers; de-skilling and loss of independence by vehicle users; exposure to hacking and malware; and the further concentration of market and data power in the hands of a few global conglomerates capable of consolidating AI capacity, and of lobbying governments to facilitate the shift of liability onto others and their potential destruction of existing occupations and industries.

There are different opinions on who should be held liable in case of a crash, especially with people being hurt. Many experts see the car manufacturers themselves responsible for those crashes that occur due to a technical malfunction or misconstruction.^[274] Besides the fact that the car manufacturer would be the source of the problem in a situation where a car crashes due to a technical issue, there is another important reason why car manufacturers could be held responsible: it would encourage them to innovate and heavily invest into fixing those issues, not only due to protection of the brand image, but also due to financial and criminal consequences. However, there are also voices^[who?] that argue those using or owning the vehicle should be held responsible since they know the risks involved in using such a vehicle. Experts^[who?] suggest introducing a tax or insurances that would protect owners and users of automated vehicles of claims made by victims of an accident.^[274] Other possible parties that can be held responsible in case of a technical failure include software engineers that programmed the code for the automated operation of the vehicles, and suppliers of components of the AV.^[275]

Taking aside the question of legal liability and moral responsibility, the question arises how automated vehicles should be programmed to behave in an emergency situation where either passengers or other traffic participants like: pedestrians, bicyclists and other drivers are endangered. A moral dilemma that a software engineer or car manufacturer might face in programming the operating software is described in an ethical thought experiment, the trolley problem: a conductor of a trolley has the choice of staying on the planned track and running over five people, or turn the trolley onto a track where it would kill only one person, assuming there is no traffic on it.^[276] When a self-driving car is in following scenario: it's driving with passengers and suddenly a person appears in its way. The car has to decide between the two options, either to run the person over or to avoid hitting the person by swerving into a wall, killing the passengers.^[277] There are two main considerations that need to be addressed. First, what moral basis would be used by an automated vehicle to make decisions? Second, how could those be translated into software code? Researchers have suggested, in particular, two ethical theories to be applicable to the behavior of automated vehicles in cases of emergency: deontology and utilitarianism.^[278] Asimov's three laws of robotics are a typical example of deontological ethics. The theory suggests that an automated car needs to follow strict written-out rules that it needs to follow in any situation. Utilitarianism suggests the idea that any decision must be made based on the goal to maximize utility. This needs a definition of utility which could be maximizing the number of people surviving in a crash. Critics suggest that automated vehicles should adapt a mix of multiple theories to be able to respond morally right in the instance of a crash.^[278]

Many 'Trolley' discussions skip over the practical problems of how a probabilistic machine learning vehicle AI could be sophisticated enough to understand that a deep problem of moral philosophy is presenting itself from instant to instant while using a dynamic projection into the near future, what sort of moral problem it actually would be if any, what the relevant weightings in human value terms should be given to all the other humans involved who will be probably unreliably identified, and how reliably it can assess the probable outcomes. These practical difficulties, and those around testing and assessment of solutions to them, may present as much of a challenge as the theoretical abstractions.^{[citation needed]}

While most trolley conundrums involve hyperbolic and unlikely fact patterns, it is inevitable mundane ethical decisions and risk calculations such as the precise millisecond a car should yield to a yellow light or how closely to drive to a bike lane will need to be programmed into the software of autonomous vehicles.^[279] Algorithms dictate, for example, how closely to drive to a bike lane or the precise moment an autonomous car should yield to a yellow light.^[279] Mundane ethical situations may even be more relevant than rare fatal circumstances because of the specificity implicated and their large scope.^[279] Mundane situations involving drivers and pedestrians are so prevalent that, in the aggregate, produce large amounts of injuries and deaths.^[279] Hence, even incremental permutations of moral algorithms can have a notable effect when considered in their entirety.^[279]

Privacy-related issues arise mainly from the interconnectivity of automated cars, making it just another mobile device that can gather any information about an individual. This information gathering ranges from tracking of the routes taken, voice recording, video recording, preferences in media that is consumed in the car, behavioral patterns, to many more streams of information.^[280][281] The data and communications infrastructure needed to support these vehicles may also be capable of surveillance, especially if coupled to other data sets and advanced analytics.

The implementation of automated vehicles to the mass market might cost up to 5 million jobs in the US alone, making up almost 3% of the workforce.^[282] Those jobs include drivers of taxis, buses, vans, trucks, and e-hailing vehicles. Many industries, such as the auto insurance industry are indirectly affected. This industry alone generates an annual revenue of about $220 billion, supporting 277,000 jobs.^[283] To put this into perspective – this is about the number of mechanical engineering jobs.^[284] The potential loss of a majority of those jobs will have a tremendous impact on those individuals involved.^[285] Both India and China have placed bans on automated cars with the former citing protection of jobs.^{[citation needed]}

The Massachusetts Institute of Technology has animated the trolley problem in the context of autonomous cars in a website called The Moral Machine.^[286] The Moral Machine generates random scenarios in which autonomous cars malfunction and forces the user to choose between two harmful courses of action.^[286] MIT’s Moral Machine experiment has collected data involving over 40 million decisions from people in 233 countries to ascertain peoples’ moral preferences. The MIT study illuminates that ethical preferences vary among cultures and demographics and likely correlate with modern institutions and geographic traits.^[286]

Global trends of the MIT study highlight that, overall, people prefer to save the lives of humans over other animals, prioritize the lives of many rather than few, and spare the lives of young rather than old.^[286] Men are slightly more likely to spare the lives of women, and religious affiliates are slightly more likely to prioritize human life. The lives of criminals were prioritized more than cats, but the lives of dogs were prioritized more than the lives of criminals.^[287] The lives of homeless were spared more than the elderly, but the lives of homeless were spared less often than the obese.^[287]

People overwhelmingly express a preference for autonomous vehicles to be programmed with utilitarian ideas, that is, in a manner that generates the least harm and minimizes driving casualties.^[288] While people want others to purchase utilitarian promoting vehicles, they themselves prefer to ride in vehicles that prioritize the lives of people inside the vehicle at all costs.^[288] This presents a paradox in which people prefer that others drive utilitarian vehicles designed to maximize the lives preserved in a fatal situation but want to ride in cars that prioritize the safety of passengers at all costs.^[288] People disapprove of regulations that promote utilitarian views and would be less willing to purchase a self-driving car that may opt to promote the greatest good at the expense of its passengers.^[288]

Bonnefon et al. conclude that the regulation of autonomous vehicle ethical prescriptions may be counterproductive to societal safety.^[288] This is because, if the government mandates utilitarian ethics and people prefer to ride in self-protective cars, it could prevent the large scale implementation of self-driving cars.^[288] Delaying the adoption of autonomous cars vitiates the safety of society as a whole because this technology is projected to save so many lives.^[288] This is a paradigmatic example of the tragedy of the commons in which rational actors cater to their self-interested preferences at the expense of societal utility.^[289]

Anticipated launch of cars[edit]

In December 2015, Tesla CEO Elon Musk predicted that a completely automated car would be introduced by the end of 2018;^[290] in December 2017, he announced that it would take another two years to launch a fully self-driving Tesla onto the market.^[291]Waymo launched a ride hailing service in Phoenix in December, 2018. It seems the clear leader in self driving cars, although its crash rate in California is still higher than a novice driver. Drive.ai is doing a trial run in Frisco, TX and Arlington TX.^{[citation needed]}

In fiction[edit]

In film[edit]

The automated and occasionally sentient self-driving car story has earned its place in both literary science fiction and pop sci-fi.^[292]

• A VW Beetle named Dudu ^[de] features in the 1971 to 1978 German Superbug (film series) of movies similar to Disney's Herbie, but with an electronic brain. (Herbie, also a Beetle, was depicted as an anthropomorphic car with its own spirit.)
• In the film Batman (1989), starring Michael Keaton, the Batmobile is shown to be able to drive to Batman's current location with some navigation commands from Batman and possibly some automation. In the 1992 sequel Batman Returns the Batmobile's self-driving system is hijacked by The Penguin, who wrecks havoc through the city to frame Batman until Bruce undoes the sabotage.
• The film Total Recall (1990), starring Arnold Schwarzenegger, features taxis called Johnny Cabs controlled by artificial intelligence in the shape of an android bust, while still possessing a joystick for manual control.
• The film Demolition Man (1993), starring Sylvester Stallone and set in 2032, features vehicles that can be self-driven or commanded to 'Auto Mode' where a voice-controlled computer operates the vehicle.
• The film Timecop (1994), starring Jean-Claude Van Damme, set in 2004 and 1994, has automated cars.
• Another Arnold Schwarzenegger movie, The 6th Day (2000), features an automated car commanded by Michael Rapaport.
• The film Minority Report (2002), set in Washington, D.C. in 2054, features an extended chase sequence involving automated cars. The vehicle of protagonist John Anderton is transporting him when its systems are overridden by police in an attempt to bring him into custody.
• The film The Incredibles (2004), Mr. Incredible makes his car automated while it changes him into his supersuit when driving to catch up to a car of robbers on the run.

• The film I, Robot (2004), set in Chicago in 2035, features automated vehicles driving on highways, allowing the car to travel safer at higher speeds than if manually controlled. The option to manually operate the vehicles is available.
• In the film Eagle Eye (2008) Shia LaBeouf and Michelle Monaghan are driven around in a Porsche Cayenne that is controlled by ARIIA (a giant supercomputer).
• Geostorm (2017), set in 2022, features a self-driving taxi stolen by protagonists Max Lawson and Sarah Wilson to protect the President from mercenaries and a superstorm.
• The film Logan (2017), set in 2029, features fully automated trucks.
• Blade Runner 2049 (2017) opens with LAPDReplicant cop K waking up in his modern Spinner (a flying police car, now featuring automatic driver and separable surveillance roof drone) on approach to a protein farm in northern California.
• Upgrade (2018), set in a not too distant future, highlights the hazardous side to automated cars as their driving systems can get hijacked and imperil the passengers.
• In the film Child's Play (2019) Chucky hijacks a self-driving 'Kaslan Car' for the murder of Mike's mother.

In literature[edit]

Intelligent or self-driving cars are a common theme in science fiction literature. Examples include:

• In Isaac Asimov's science-fiction short story, 'Sally' (first published May–June 1953), automated cars have 'positronic brains' and communicate via honking horns and slamming doors, and save their human caretaker. Due to the high cost of the brain, few can afford a personal vehicle, so buses have become the norm.
• Peter F. Hamilton's Commonwealth Saga series features intelligent or self-driving vehicles.
• In Robert A Heinlein's novel, The Number of the Beast (1980), Zeb Carter's driving and flying car 'Gay Deceiver' is at first semi-automated and later, after modifications by Zeb's wife Deety, becomes sentient and capable of fully autonomous operation.
• In Edizioni Piemme's series Geronimo Stilton, a robotic vehicle called 'Solar' is in the 54th book.
• Alastair Reynolds' series, Revelation Space, features intelligent or self-driving vehicles.
• In Daniel Suarez' novels Daemon (2006) and Freedom™ (2010) driverless cars and motorcycles are used for attacks in a software-based open-source warfare. The vehicles are modified for this using 3D printers and distributed manufacturing^[293] and are also able to operate as swarms.

In television[edit]

• 'Gone in 60 Seconds' season 2, episode 6 of 2015 TV series CSI: Cyber features three seemingly normal customized vehicles, a 2009 Nissan Fairlady Z Roadster, a BMW M3 E90 and a Cadillac CTS-V, and one stock luxury BMW 7 Series, being remote-controlled by a computer hacker.
• 'Handicar', season 18, episode 4 of 2014 TV series South Park features a Japanese automated car that takes part in the Wacky Races-style car race.
• KITT and KARR, the Pontiac Firebird Trans-Ams in the 1982 TV series Knight Rider, were sentient and autonomous. The KITT and KARR based Ford Mustangs from Knight Rider were also sentient and autonomous, like their Firebird counterparts.
• 'Driven', series 4 episode 11 of the 2003 TV series NCIS features a robotic vehicle named 'Otto', part of a high-level project of the Department of Defense, which causes the death of a Navy Lieutenant, and then later almost kills Abby.
• The TV series Viper features a silver/grey armored assault vehicle, called The Defender, which masquerades as a flame-red 1992 Dodge Viper RT/10 and later as a 1998 cobalt blue Dodge Viper GTS. The vehicle's sophisticated computer systems allow it to be controlled via remote on some occasions.
• Black Mirror episode 'Hated in the Nation' briefly features a self-driving SUV with a touchscreen interface on the inside.
• Bull has a show discussing the effectiveness and safety of self-driving cars in an episode call E.J.^[294]

See also[edit]

• DARPA Grand Challenge: 2004, 2007
• DARPA Robotics Challenge (2012)

References[edit]

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Further reading[edit]

• O'Toole, Randal (18 January 2010). Gridlock: Why We're Stuck in Traffic and What To Do About It. Cato Institute. ISBN978-1-935308-24-9.
• Macdonald, Iain David Graham (2011). A Simulated Autonomous Car(PDF) (thesis). The University of Edinburgh. Retrieved 17 April 2013.
• Knight, Will (22 October 2013). 'The Future of Self-driving Cars'. MIT Technology Review. Retrieved 22 July 2016.
• Taiebat, Morteza; Brown, Austin; Safford, Hannah; Qu, Shen; Xu, Ming (2018). 'A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles'. Environmental Science & Technology. 52 (20): 11449–11465. arXiv:1901.10581. doi:10.1021/acs.est.8b00127. PMID30192527.
• Glancy, Dorothy (2016). A Look at the Legal Environment for Driverless Vehicles(PDF) (Report). National Cooperative Highway Research Program Legal Research Digest. 69. Washington, DC: Transportation Research Board. ISBN978-0-309-37501-6. Retrieved 22 July 2016.
• Newbold, Richard (17 June 2015). 'The driving forces behind what would be the next revolution in the haulage sector'. The Loadstar. Retrieved 22 July 2016.
• Bergen, Mark (27 October 2015). 'Meet the Companies Building Self-Driving Cars for Google and Tesla (And Maybe Apple)'. re/code.
• John A. Volpe National Transportation Systems Center (March 2016). 'Review of Federal Motor Vehicle Safety Standards (FMVSS) for Automated Vehicles: Identifying potential barriers and challenges for the certification of automated vehicles using existing FMVSS'(PDF). National Transportation Library. U.S. Department of Transportation.
• Slone, Sean (August 2016). 'State Laws on Autonomous Vehicles'(PDF). Capitol Research – Transportation Policy. Council of State Governments. Retrieved 28 September 2016.
• Steve Henn (31 July 2015). 'Remembering When Driverless Elevators Drew Skepticism'.
• James M. Anderson; et al. (2016). 'Autonomous Vehicle Technology: A Guide for Policymakers'(PDF). RAND Corporation.
• Gereon Meyer, Sven Beiker (Eds.), Road Vehicle Automation, Springer International Publishing 2014, ISBN978-3-319-05990-7, and following issues: Road Vehicle Automation 2 (2015), Road Vehicle Automation 3 (2016), Road Vehicle Automation 4 (2017), Road Vehicle Automation 5 (2018). These books are based on presentations and discussions at the Automated Vehicles Symposium organized annually by TRB and AUVSI.