Frequency and Quality of Exposure to Adaptive Cruise Control and Impact on Trust, Workload, and Mental Models.

Advanced Driver Assistance Systems (ADAS) support drivers with some driving tasks. However, drivers may lack appropriate knowledge about ADAS resulting in inadequate mental models. This may result in drivers misusing ADAS, or mistrusting the technologies, especially after encountering edge-case events (situations beyond the capability of an ADAS where the system may malfunction or fail) and may also adversely affect driver workload. Literature suggests mental models could be improved through exposure to ADAS-related driving situations, especially those related to ADAS capabilities and limitations. The objective of this study was to examine the impact of frequency and quality of exposure on drivers' understanding of Adaptive Cruise Control (ACC), their trust, and their workload after driving with ACC. Sixteen novice ACC users were recruited for this longitudinal driving simulator study. Drivers were randomly assigned to one of two groups - the 'Regular Exposure' group encountering 'routine' edge-case events, and the 'Enhanced Exposure' group encountering 'routine' and 'rare' events. Each participant undertook four different simulator sessions, each separated by about a week. Each session comprised a simulator drive featuring five edge-case scenarios. The study followed a mixed-subject design, with exposure frequency as the within-subject variable, and quality of exposure (defined by two groups) as the between-subject variable. Surveys measured drivers' trust, workload, and mental models. The results from the analyses using linear regression models revealed that drivers' mental models about ACC improve with frequency of exposure to ACC and associated edge-case driving situations. This was more the case for drivers who experienced 'rare' ACC edge cases. The findings also indicate that for those who encountered 'rare' edge cases, workload was higher and trust was lower than those who did not. These findings are significant since they underline the importance of experience and familiarity with ADAS for safe operation. While these findings indicate that drivers benefit from increased exposure to ACC and edge cases in terms of appropriate use of ADAS, and ultimately promise crash reductions and injury prevention, a challenge remains regarding how to provide drivers with appropriate exposure in a safe manner.

Simulator evaluation of an intersection maneuver assist system with connected and automated vehicle technologies.

Intersection crashes can be potentially mitigated through vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) safety management systems. It is important, however, to consider some of the human factors related aspects of such systems to maximise potential safety benefits. In this study, Intersection Manoeuvre Assistance Systems were conceptualised and evaluated in a driving simulator. The systems were designed to assist drivers with intersection manoeuvres by making use of connected infrastructure and providing real-time feedback, guidance, and active vehicle controls. The study compared drivers' confidence, workload, glances at the instrument panel, and hazard anticipation when driving using three systems-System A (no alert or assist); System B (alert only); and System C (alert and assist). Study results show differences in drivers' confidence in such systems and potentially degraded visual gaze behaviours.Practitioner summary: Connected infrastructure-based intersection management assistance systems can potentially reduce crashes. This experimental driving simulation study evaluated drivers' perceptions and reactions to intersection management systems. Results indicate reduced confidence in automated systems, reduced visual scanning for external hazards at intersections, and increased off-road glances towards the instrument panel.

Modeling drivers' reaction when being tailgated: A Random Forests Method.

BACKGROUND: Tailgating is a common aggressive driving behavior that has been identified as one of the leading causes of rear-end crashes. Previous studies have explored the behavior of tailgating drivers and have reported effective solutions to decrease the amount or prevalence of tailgating. This paper tries to fill the research gap by focusing on understanding highway tailgating scenarios and examining the leading vehicles' reaction using existing naturalistic driving data. METHOD: A total of 1,255 tailgating events were identified by using the one-second time headway threshold criterion. Four types of reactions from the leading vehicles were identified, including changing lanes, slowing down, speeding up, and making no response. A Random Forests algorithm was employed in this study to predict the leading vehicle's reaction based on corresponding factors including driver, vehicle, and environmental variables. RESULTS: The analysis of the tailgating scenarios and associated factors showed that male drivers were more frequently involved in tailgating events than female drivers and that tailgating was more prevalent under sunny weather and in daytime conditions. Changing lanes was the most prevalent reaction from the leading vehicle during tailgating, which accounted for more than half of the total events. The results of Random Forests showed that mean time headway, duration of tailgating, and minimum time headway were three main factors, which had the greatest impact on the leading vehicle drivers' reaction. It was found that in 95% of the events, leading vehicles would change lanes when being tailgated for two minutes or longer. Practical Applications: Results of this study can help to better understand the behavior and decision making of drivers. This understanding can be used in designing countermeasures or assistance systems to reduce tailgating behavior and related negative safety consequences.

Predicting driver takeover performance in conditionally automated driving.

In conditionally automated driving, drivers have difficulty taking over control when requested. To address this challenge, we aimed to predict drivers' takeover performance before the issue of a takeover request (TOR) by analyzing drivers' physiological data and external environment data. We used data sets from two human-in-the-loop experiments, wherein drivers engaged in non-driving-related tasks (NDRTs) were requested to take over control from automated driving in various situations. Drivers' physiological data included heart rate indices, galvanic skin response indices, and eye-tracking metrics. Driving environment data included scenario type, traffic density, and TOR lead time. Drivers' takeover performance was categorized as good or bad according to their driving behaviors during the transition period and was treated as the ground truth. Using six machine learning methods, we found that the random forest classifier performed the best and was able to predict drivers' takeover performance when they were engaged in NDRTs with different levels of cognitive load. We recommended 3 s as the optimal time window to predict takeover performance using the random forest classifier, with an accuracy of 84.3% and an F1-score of 64.0%. Our findings have implications for the algorithm development of driver state detection and the design of adaptive in-vehicle alert systems in conditionally automated driving.

A Wizard-of-Oz experimental approach to study the human factors of automated vehicles: Platform and methods evaluation.

OBJECTIVES: Driving simulation is an important platform for studying vehicle automation. There are different approaches to using this platform - with most using scripting or programmatic tools to simulate vehicle automation. A less frequently used approach, the Wizard-of-Oz method, has potential for increased flexibility and efficiency in designing and conducting experiments. This study designed and evaluated an experimental setup to examine the feasibility of this approach as an alternative for conducting automation studies. METHODS: Twenty-four participants experienced simulated vehicle automation in two platforms, one where the automation was controlled by algorithms, and the other where the automation was simulated by an external operator. Surveys were administered after each drive and the drivers' takeover performance after the automation disengaged was measured. RESULTS: Results indicate that while the kinematic parameters of the driving differed significantly for the two platforms, there were no significant differences in the perceptions of participants and in their takeover performance between the two platforms. CONCLUSION: These results provide evidence for the use of alternative approaches for the conduct of human factors studies on vehicle automation, potentially lowering barriers to undertaking such experiments while increasing flexibility in designing more complex studies.

Pedestrian Trust in Automated Vehicles: Role of Traffic Signal and AV Driving Behavior.

Pedestrians' acceptance of automated vehicles (AVs) depends on their trust in the AVs. We developed a model of pedestrians' trust in AVs based on AV driving behavior and traffic signal presence. To empirically verify this model, we conducted a human-subject study with 30 participants in a virtual reality environment. The study manipulated two factors: AV driving behavior (defensive, normal, and aggressive) and the crosswalk type (signalized and unsignalized crossing). Results indicate that pedestrians' trust in AVs was influenced by AV driving behavior as well as the presence of a signal light. In addition, the impact of the AV's driving behavior on trust in the AV depended on the presence of a signal light. There were also strong correlations between trust in AVs and certain observable trusting behaviors such as pedestrian gaze at certain areas/objects, pedestrian distance to collision, and pedestrian jaywalking time. We also present implications for design and future research.

Psychosocial factors associated with intended use of automated vehicles: A simulated driving study.

This study applied the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to assess drivers' intended use of automated vehicles (AVs) after undertaking a simulated driving task. In addition, this study explored the potential for trust to account for additional variance to the psychosocial factors in TPB and TAM. Seventy-four participants (51% female) aged between 25 and 64 years (M = 42.8, SD = 12.9) undertook a 20 min simulated experimental drive in which participants experienced periods of automated driving and manual control. A survey task followed. A hierarchical regression analysis revealed that TPB constructs; attitude toward the behavior, subjective norms, and perceived behavioral control, were significant predictors of intentions to use AV. In addition, there was partial support for the test of TAM, with ease of use (but not usefulness) predicting intended use of AV (SAE Level 3). Trust contributed variance to both models beyond TPB or TAM constructs. The findings provide an important insight into factors that might reflect intended use of vehicles that are primarily automated (longitudinal, lateral, and manoeuvre controls) but require and allow drivers to have periods of manual control.

Perceived safety benefits, concerns, and utility of advanced driver assistance systems among owners of ADAS-equipped vehicles.

OBJECTIVE: There are many unknowns regarding drivers' use and acceptance of advanced vehicle technologies. This research aimed to examine drivers' perceptions of advanced driver assistance systems (ADAS). METHODS: This research was conducted using structured interviews and focus groups of owners of vehicles with advanced technologies. RESULTS: Drivers' perceptions about ADAS were mixed, but generally safety was considered to be the greatest value of the systems. There was recognition that the systems may result in overreliance and thus encourage distraction behaviors or other bad driving habits, and participants generally expressed that they were ultimately responsible for the vehicle's operation and needed to be ready to override the system if it failed. CONCLUSIONS: The findings indicate that driver characteristics and individual factors may influence perceptions, behaviors, and interactions with safety technology, and this research is a first step toward understanding any influences. Human factors issues related to automated vehicle technologies are critical for design and deployment, including those of trust, acceptance, and understanding of systems.

Peer Passenger Norms and Pressure: Experimental Effects on Simulated Driving Among Teenage Males.

OBJECTIVE: Serious crashes are more likely when teenage drivers have teenage passengers. One likely source of this increased risk is social influences on driving performance. This driving simulator study experimentally tested the effects of peer influence (i.e., risk-accepting compared to risk-averse peer norms reinforced by pressure) on the driving risk behavior (i.e., risky driving behavior and inattention to hazards) of male teenagers. It was hypothesized that peer presence would result in greater driving risk behavior (i.e., increased driving risk and reduced latent hazard anticipation), and that the effect would be greater when the peer was risk-accepting. METHODS: Fifty-three 16- and 17-year-old male participants holding a provisional U.S., State of Michigan driver license were randomized to either a risk-accepting or risk-averse condition. Each participant operated a driving simulator while alone and separately with a confederate peer passenger. The simulator world included scenarios designed to elicit variation in driving risk behavior with a teen passenger present in the vehicle. RESULTS: Significant interactions of passenger presence (passenger present vs. alone) by risk condition (risk-accepting vs. risk-averse) were observed for variables measuring: failure to stop at yellow light intersections (Incident Rate Ratio (IRR)=2.16; 95% Confidence Interval [95CI]=1.06, 4.43); higher probability of overtaking (IRR=10.17; 95CI=1.43, 73.35); shorter left turn latency (IRR=0.43; 95CI=0.31,0.60); and, failure to stop at an intersection with an occluded stop sign (IRR=7.90; 95CI=2.06,30.35). In all cases, greater risky driving by participants was more likely with a risk-accepting passenger versus a risk-averse passenger present and a risk-accepting passenger present versus driving alone. CONCLUSIONS: Exposure of male teenagers to a risk-accepting confederate peer passenger who applied peer influence increased simulated risky driving behavior compared with exposure to a risk-averse confederate peer passenger or driving alone. These results are consistent with the contention that variability in teenage risky driving is in part explained by social influences.

Naturalistic teenage driving study: Findings and lessons learned.

INTRODUCTION: This paper summarizes the findings on novice teenage driving outcomes (e.g., crashes and risky driving behaviors) from the Naturalistic Teenage Driving Study. METHOD: Survey and driving data from a data acquisition system (global positioning system, accelerometers, cameras) were collected from 42 newly licensed teenage drivers and their parents during the first 18 months of teenage licensure; stress responsivity was also measured in teenagers. RESULT: Overall teenage crash and near-crash (CNC) rates declined over time, but were >4 times higher among teenagers than adults. Contributing factors to teenage CNC rates included secondary task engagement (e.g., distraction), kinematic risky driving, low stress responsivity, and risky social norms. CONCLUSIONS: The data support the contention that the high novice teenage CNC risk is due both to inexperience and risky driving behavior, particularly kinematic risky driving and secondary task engagement. PRACTICAL APPLICATIONS: Graduated driver licensing policy and other prevention efforts should focus on kinematic risky driving, secondary task engagement, and risky social norms.

A Review of Hazard Anticipation Training Programs for Young Drivers.

PURPOSE: Poor hazard anticipation skills are a risk factor associated with high motor vehicle crash rates of young drivers. A number of programs have been developed to improve these skills. The purpose of this review was to assess the empirical literature on hazard anticipation training for young drivers. METHODS: Studies were included if they (1) included an assessment of hazard anticipation training outcomes; (2) were published between January 1, 1980 and December 31, 2013 in an English language peer-reviewed journal or conference proceeding; and (3) included at least one group that uniquely comprised a cohort of participants aged <21 years. Nineteen studies met inclusion criteria. RESULTS: Studies used a variety of training methods including interactive computer programs, videos, simulation, commentary driving, or a combination of approaches. Training effects were predominantly measured through computer-based testing and driving simulation with eye tracking. Four studies included an on-road evaluation. Most studies evaluated short-term outcomes (immediate or few days). In all studies, young drivers showed improvement in selected hazard anticipation outcomes but none investigated crash effects. CONCLUSIONS: Although there is promise in existing programs, future research should include long-term follow-up, evaluate crash outcomes, and assess the optimal timing of hazard anticipation training taking into account the age and experience level of young drivers.

Young Drivers and Their Passengers: A Systematic Review of Epidemiological Studies on Crash Risk.

PURPOSE: A systematic review of the literature was conducted to appraise the evidence from epidemiological studies of crash risk in young drivers accompanied by passengers, compared with solo driving. METHODS: Databases searched were the Cochrane Library, Embase, Scopus, Transportation Research Information Services, and Web of Science for studies published between January 1, 1989 and August 1, 2013. Epidemiological studies were selected for review if they focused on crashes of young drivers (≤24 years old) and included both a no-passenger comparison group and some measure of exposure to enable calculation of estimates. RESULTS: Fifteen articles (17 studies) were selected; seven studies reported on fatal crashes and 10 on nonfatal or combined fatal/nonfatal crashes. Studies on fatal crashes showed increased risk, compared with solo driving, for young drivers with at least one passenger (significant estimates ranging from 1.24 to 1.89) and two or more passengers versus solo driving (1.70-2.92). Increased risk was also found for fatal crashes and for combined or nonfatal crashes with male versus female passengers (1.53-2.66) and for younger versus older drivers (1.42-3.14). CONCLUSIONS: Results more clearly indicated an increased risk for passenger presence in fatal crashes than that in nonfatal or combined fatal/nonfatal crashes. Findings of this review, based on correlational studies, support licensing policies that limit the presence and number of young passengers for young drivers.

Assessing Risk-Taking in a Driving Simulator Study: Modeling Longitudinal Semi-Continuous Driving Data Using a Two-Part Regression Model with Correlated Random Effects.

Signalized intersection management is a common measure of risky driving in simulator studies. In a recent randomized trial, investigators were interested in whether teenage males exposed to a risk-accepting passenger took more intersection risks in a driving simulator compared with those exposed to a risk-averse peer passenger. Analyses in this trial are complicated by the longitudinal or repeated measures that are semi-continuous with clumping at zero. Specifically, the dependent variable in a randomized trial looking at the effect of risk-accepting versus risk-averse peer passengers on teenage simulator driving is comprised of two components. The discrete component measures whether the teen driver stops for a yellow light, and the continuous component measures the time the teen driver, who does not stop, spends in the intersection during a red light. To convey both components of this measure, we apply a two-part regression with correlated random effects model (CREM), consisting of a logistic regression to model whether the driver stops for a yellow light and a linear regression to model the time spent in the intersection during a red light. These two components are related through the correlation of their random effects. Using this novel analysis, we found that those exposed to a risk-averse passenger have a higher proportion of stopping at yellow lights and a longer mean time in the intersection during a red light when they did not stop at the light compared to those exposed to a risk-accepting passenger, consistent with the study hypotheses and previous analyses. Examining the statistical properties of the CREM approach through simulations, we found that in most situations, the CREM achieves greater power than competing approaches. We also examined whether the treatment effect changes across the length of the drive and provided a sample size recommendation for detecting such phenomenon in subsequent trials. Our findings suggest that CREM provides an efficient method for analyzing the complex longitudinal data encountered in driving simulation studies.

Buffering social influence: neural correlates of response inhibition predict driving safety in the presence of a peer.

Adolescence is a period characterized by increased sensitivity to social cues, as well as increased risk-taking in the presence of peers. For example, automobile crashes are the leading cause of death for adolescents, and driving with peers increases the risk of a fatal crash. Growing evidence points to an interaction between neural systems implicated in cognitive control and social and emotional context in predicting adolescent risk. We tested such a relationship in recently licensed teen drivers. Participants completed an fMRI session in which neural activity was measured during a response inhibition task, followed by a separate driving simulator session 1 week later. Participants drove alone and with a peer who was randomly assigned to express risk-promoting or risk-averse social norms. The experimentally manipulated social context during the simulated drive moderated the relationship between individual differences in neural activity in the hypothesized cognitive control network (right inferior frontal gyrus, BG) and risk-taking in the driving context a week later. Increased activity in the response inhibition network was not associated with risk-taking in the presence of a risky peer but was significantly predictive of safer driving in the presence of a cautious peer, above and beyond self-reported susceptibility to peer pressure. Individual differences in recruitment of the response inhibition network may allow those with stronger inhibitory control to override risky tendencies when in the presence of cautious peers. This relationship between social context and individual differences in brain function expands our understanding of neural systems involved in top-down cognitive control during adolescent development.

Neural responses to exclusion predict susceptibility to social influence.

PURPOSE: Social influence is prominent across the lifespan, but sensitivity to influence is especially high during adolescence and is often associated with increased risk taking. Such risk taking can have dire consequences. For example, in American adolescents, traffic-related crashes are leading causes of nonfatal injury and death. Neural measures may be especially useful in understanding the basic mechanisms of adolescents' vulnerability to peer influence. METHODS: We examined neural responses to social exclusion as potential predictors of risk taking in the presence of peers in recently licensed adolescent drivers. Risk taking was assessed in a driving simulator session occurring approximately 1 week after the neuroimaging session. RESULTS: Increased activity in neural systems associated with the distress of social exclusion and mentalizing during an exclusion episode predicted increased risk taking in the presence of a peer (controlling for solo risk behavior) during a driving simulator session outside the neuroimaging laboratory 1 week later. These neural measures predicted risky driving behavior above and beyond self-reports of susceptibility to peer pressure and distress during exclusion. CONCLUSIONS: These results address the neural bases of social influence and risk taking; contribute to our understanding of social and emotional function in the adolescent brain; and link neural activity in specific, hypothesized, regions to risk-relevant outcomes beyond the neuroimaging laboratory. Results of this investigation are discussed in terms of the mechanisms underlying risk taking in adolescents and the public health implications for adolescent driving.

Peer passenger influences on male adolescent drivers' visual scanning behavior during simulated driving.

PURPOSE: There is a higher likelihood of crashes and fatalities when an adolescent drives with peer passengers, especially for male drivers and male passengers. Simulated driving of male adolescent drivers with male peer passengers was studied to examine passenger influences on distraction and inattention. METHODS: Male adolescents drove in a high-fidelity driving simulator with a male confederate who posed either as a risk-accepting passenger or as a risk-averse passenger. Drivers' eye movements were recorded. The visual scanning behavior of the drivers was compared when driving alone with when driving with a passenger and when driving with a risk-accepting passenger with a risk-averse passenger. RESULTS: The visual scanning of a driver significantly narrowed horizontally and vertically when driving with a peer passenger. There were no significant differences in the times the drivers' eyes were off the forward roadway when driving with a passenger versus when driving alone. Some significant correlations were found between personality characteristics and the outcome measures. CONCLUSIONS: The presence of a male peer passenger was associated with a reduction in the visual scanning range of male adolescent drivers. This reduction could be a result of potential cognitive load imposed on the driver due to the presence of a passenger and the real or perceived normative influences or expectations from the passenger.

Keep your eyes on the road: young driver crash risk increases according to duration of distraction.

PURPOSE: Secondary task engagement that distracts the driver is a contributing factor to motor vehicle crashes among adults. However, the association between eye glance duration and crash risk with novice teenage drivers has not been determined. METHODS: Vehicles of 42 newly licensed teenage drivers were instrumented with cameras, accelerometers, Global Positioning System(s) (GPS), and other devices. Data were collected continuously for 18 months. Crashes and near crashes (CNCs) were identified by examining highly elevated gravitational force events. Video footage of the 6 seconds prior to each CNC and randomly sampled non-CNC road segments were coded for the duration of eye glances off the forward roadway and the presence of secondary task engagement. The likelihood (odds ratios) of CNC due to eye glance behavior was calculated by comparing the prevalence of secondary task engagement and duration of eyes off road prior to CNC with the prevalence and duration of eyes off road during non-CNC road segments. RESULTS: Crash risk increased with the duration of single longest glance during all secondary tasks (OR=3.8 for >2 s) and wireless secondary task engagement (OR=5.5 for >2 s). Single longest glance provided a more consistent estimate of crash risk than total time eyes off the forward roadway. CONCLUSIONS: Those eye glances away from the forward roadway involving secondary tasks increased the likelihood of CNC. The longer the duration of eye glance away from the road the greater the risk, regardless of type of secondary task. Education and policy discouraging secondary task engagement, particularly for prolonged periods, is warranted.

Experimental effects of injunctive norms on simulated risky driving among teenage males.

OBJECTIVE: Teenage passengers affect teenage driving performance, possibly by social influence. To examine the effect of social norms on driving behavior, male teenagers were randomly assigned to drive in a simulator with a peer-aged confederate to whom participants were primed to attribute either risk-accepting or risk-averse social norms. It was hypothesized that teenage drivers would engage in more risky driving behavior in the presence of peer passengers than no passengers, and with a risk-accepting compared with a risk-averse passenger. METHOD: 66 male participants aged 16 to 18 years holding a provisional driver license were randomized to drive with a risk-accepting or risk-averse passenger in a simulator. Failure to Stop at a red light and percent Time in Red (light) were measured as primary risk-relevant outcomes of interest at 18 intersections, while driving once alone and once with their assigned passenger. RESULTS: The effect of passenger presence on risky driving was moderated by passenger type for Failed to Stop in a generalized linear mixed model (OR = 1.84, 95% CI [1.19, 2.86], p < .001), and percent Time in Red in a mixed model (B = 7.71, 95% CI [1.54, 13.87], p < .05). CONCLUSIONS: Exposure of teenage males to a risk-accepting confederate peer increased teenage males' risky simulated driving behavior compared with exposure to a risk-averse confederate peer. These results indicate that variability in teenage risky driving could be partially explained by social norms.

The effect of male teenage passengers on male teenage drivers: findings from a driving simulator study.

Studies have shown that teenage drivers are less attentive, more frequently exhibit risky driving behavior, and have a higher fatal crash risk in the presence of peers. The effects of direct peer pressure and conversation on young drivers have been examined. Little is known about the impact on driving performance of the presence of a non-interacting passenger and subtle modes of peer influence, such as perceived social norms. The goal of this study was to examine if teenagers would engage in more risky driving practices and be less attentive in the presence of a passenger (vs. driving alone) as well as with a risk-accepting (vs. risk-averse) passenger. A confederate portrayed the passenger's characteristics mainly by his non-verbal attitude. The relationship between driver characteristics and driving behavior in the presence of a passenger was also examined. Thirty-six male participants aged 16-17 years old were randomly assigned to drive with a risk-accepting or risk-averse passenger. Main outcomes included speed, headway, gap acceptance, eye glances at hazards, and horizontal eye movement. Driver characteristics such as tolerance of deviance, susceptibility to peer pressure, and self-esteem were measured. Compared to solo driving, the presence of a passenger was associated with significantly fewer eye glances at hazards and a trend for fewer horizontal eye movements. Contrary to the hypothesis, however, Passenger Presence was associated with waiting for a greater number of vehicles to pass before initiating a left turn. Results also showed, contrary to the hypothesis, that participants with the risk-accepting passenger maintained significantly longer headway with the lead vehicle and engaged in more eye glances at hazards than participants with the risk-averse passenger. Finally, when driving with the passenger, earlier initiation of a left turn in a steady stream of oncoming vehicles was significantly associated with higher tolerance of deviance and susceptibility to peer pressure, while fewer eye glances at hazards was linked to lower self-esteem. While the results of this study were mixed, they suggest that the presence of a teenage passenger can affect some aspects of teenage driver behavior even in the absence of overt pressure and distraction. Results are discussed in relation to theoretical concepts of social influence and social facilitation models.