The effect of human-machine interface modality, specificity, and timing on driver performance and behavior while using vehicle automation.

The effectiveness of the human-machine interface (HMI) in a driving automation system during takeover situations is based, in part, on its design. Past research has indicated that modality, specificity, and timing of the HMI have an impact on driver behavior. The objective of this study was to examine the effectiveness of two HMIs, which vary by modality, specificity, and timing, on drivers' takeover time, performance, and eye glance behavior. Drivers' behavior was examined in a driving simulator study with different levels of automation, varying traffic conditions, and while completing a non-driving related task. Results indicated that HMI type had a statistically significant effect on velocity and off-road eye glances such that those who were exposed to an HMI that gave multimodal warnings with greater specificity exhibited better performance. There were no effects of HMI on acceleration, lane position, or other eye glance metrics (e.g., on road glance duration). Future work should disentangle HMI design further to determine exactly which aspects of design yield between safety critical behavior.

Preface to the Special Issue on Assessment and Effectiveness of Driver Monitoring Systems.

With vehicle automation becoming more commonplace, the role of the human driver is shifting from that of system operator to that of system supervisor. With this shift comes the risk of drivers becoming more disengaged from the task of supervising the system functioning, thus increasing the need for technology to keep drivers alert. This special issue includes the most up-to-date research on how drivers use vehicle automation, and the safety risks it may pose. It also investigates the accuracy that driver monitoring systems have in detecting conditions like driver distraction and drowsiness, and explores ways future drivers may respond to the broader introduction of this technology on passenger vehicles.

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.

Feeling sleepy? stop driving-awareness of fall asleep crashes.

STUDY OBJECTIVES: To examine whether drivers are aware of sleepiness and associated symptoms, and how subjective reports predict driving impairment and physiological drowsiness. METHODS: Sixteen shift workers (19-65 years; 9 women) drove an instrumented vehicle for 2 hours on a closed-loop track after a night of sleep and a night of work. Subjective sleepiness/symptoms were rated every 15 minutes. Severe and moderate driving impairment was defined by emergency brake maneuvers and lane deviations, respectively. Physiological drowsiness was defined by eye closures (Johns drowsiness scores) and EEG-based microsleep events. RESULTS: All subjective ratings increased post night-shift (p < 0.001). No severe drive events occurred without noticeable symptoms beforehand. All subjective sleepiness ratings, and specific symptoms, predicted a severe (emergency brake) driving event occurring in the next 15 minutes (OR: 1.76-2.4, AUC > 0.81, p < 0.009), except "head dropping down". Karolinska Sleepiness Scale (KSS), ocular symptoms, difficulty keeping to center of the road, and nodding off to sleep, were associated with a lane deviation in the next 15 minutes (OR: 1.17-1.24, p<0.029), although accuracy was only "fair" (AUC 0.59-0.65). All sleepiness ratings predicted severe ocular-based drowsiness (OR: 1.30-2.81, p < 0.001), with very good-to-excellent accuracy (AUC > 0.8), while moderate ocular-based drowsiness was predicted with fair-to-good accuracy (AUC > 0.62). KSS, likelihood of falling asleep, ocular symptoms, and "nodding off" predicted microsleep events, with fair-to-good accuracy (AUC 0.65-0.73). CONCLUSIONS: Drivers are aware of sleepiness, and many self-reported sleepiness symptoms predicted subsequent driving impairment/physiological drowsiness. Drivers should self-assess a wide range of sleepiness symptoms and stop driving when these occur to reduce the escalating risk of road crashes due to drowsiness.

Driver Expectations of a Partial Driving Automation System in Relation to Branding and Training.

OBJECTIVE: The current study examined whether differences in the branding and description or mode of training materials influence drivers' understanding and expectations of a partial driving automation system. BACKGROUND: How technology is described might influence consumers' understanding and expectations, even if all information is accurate. METHOD: Ninety drivers received training about a real partial driving automation system with a fictitious name. Participants were randomly assigned to a branding condition (system named AutonoDrive, training emphasized capabilities; or system named DriveAssist, training emphasized limitations) and training mode (quick-start brochure; video; or in-person demonstration). No safety-critical information was withheld nor deliberately misleading information provided. After training, participants drove a vehicle equipped with the system. Associations of drivers' expectations with branding condition and training mode were assessed using between-subjects comparisons of questionnaire responses obtained pre- and post-drive. RESULTS: Immediately after training, those who received information emphasizing the system's capabilities had greater expectations of the system's function and crash avoidance capability in a variety of driving scenarios, including many in which the system would not work, as well as greater willingness to utilize the system's workload reduction benefits to take more risks. Most but not all differences persisted after driving the vehicle. Expectations about collision avoidance differed by training mode pre-drive but not post-drive. CONCLUSION: Training that emphasizes a partial driving automation system's capabilities and downplays its limitations can foster overconfidence. APPLICATION: Accuracy of technical information does not guarantee understanding; training should provide a balanced view of a system's limitations as well as capabilities.

Minding the Gap: Effects of an Attention Maintenance Training Program on Driver Calibration.

OBJECTIVE: The present study examines the effect of an existing driver training program, FOrward Concentration and Attention Learning (FOCAL) on young drivers' calibration, drivers' ability to estimate the length of their in-vehicle glances while driving, using two different measures, normalized difference scores and Brier Scores. BACKGROUND: Young drivers are poor at maintaining attention to the forward roadway while driving a vehicle. Additionally, drivers may overestimate their attention maintenance abilities. Driver training programs such as FOCAL may train target skills such as attention maintenance but also might serve as a promising way to reduce errors in drivers' calibration of their self-perceived attention maintenance behaviors in comparison to their actual performance. METHOD: Thirty-six participants completed either FOCAL or a Placebo training program, immediately followed by driving simulator evaluations of their attention maintenance performance. In the evaluation drive, participants navigated four driving simulator scenarios during which their eyes were tracked. In each scenario, participants performed a map task on a tablet simulating an in-vehicle infotainment system. RESULTS: FOCAL-trained drivers maintained their attention to the forward roadway more and reported better calibration using the normalized difference measure than Placebo-trained drivers. However, the Brier scores did not distinguish the two groups on their calibration. CONCLUSION: The study implies that FOCAL has the potential to improve not only attention maintenance skills but also calibration of the skills for young drivers. APPLICATION: Driver training programs may be designed to train not only targeted higher cognitive skills but also driver calibration-both critical for driving safety in young drivers.

Fatal hit-and-run crashes: Factors associated with leaving the scene.

INTRODUCTION: Hit-and-run crashes are a criminal offense that leave the victim without prompt medical care or the ability to receive financial compensation. METHOD: The purpose of the current study was to quantify the factors associated with the probability that a driver leaves the scene of a fatal crash, using multiple imputation to incorporate information from drivers who were never apprehended and thus whose characteristics were unknown. RESULTS: The results of this study show that in addition to driver, vehicle, and environmental factors having significant impacts on the likelihood of a driver fleeing the scene, economic and demographic factors are important as well. Practical Applications: This analysis allows for a more holistic understanding of hit-and-run crashes and informs potential countermeasures and future research.

Investigating the impact of driving automation systems on distracted driving behaviors.

Driving automation systems (e.g., SAE Level 2) ultimately aim to enhance the comfort and safety of drivers. At present, these systems are able to control some portions of the driving task (e.g., braking, steering) for extended time periods, giving drivers the opportunity to disengage from the responsibilities associated with driving. In this study, data derived from two naturalistic driving studies involving automation-equipped vehicles were analyzed to evaluate driver behaviors with respect to driving automation system use, specifically distraction-related factors (i.e., secondary task engagement, eye-glance behavior, and drowsiness). The results indicate that when drivers had prior experience using driving automation systems, they were almost two times as likely to participate in distracted driving behaviors when the systems were active than during manual driving. Drivers with less experience and familiarity with driving automation systems were less likely to drive distracted when the systems were active; however, these drivers tended to be somewhat drowsy when driving with systems activated. The results provide important insights into different operational phases of driving automation system use (i.e., learning/unfamiliar vs experienced users), whereby experience results in overtrust and overreliance on the advanced technologies, which subsequently may negate some of the safety benefits of these systems. Thus, while the safety benefits of driving automation systems are evident, it is imperative to better understand the impact these advanced technologies may have on driver behavior and performance in order to evaluate and address any unintended consequences associated with system use.

Age-Related Differences in the Cognitive, Visual, and Temporal Demands of In-Vehicle Information Systems.

In-vehicle information systems (IVIS) refer to a collection of features in vehicles that allow motorists to complete tasks (often unrelated to driving) while operating the vehicle. These systems may interfere, to a greater extent, with older drivers' ability to attend to the visual and cognitive demands of the driving environment. The current study sought to examine age-related differences in the visual, cognitive and temporal demands associated with IVIS interactions. Older and younger drivers completed a set of common tasks using the IVIS of a representative sample of six different vehicles while they drove along a low-density residential street. Evaluation measures included a Detection Response Task (DRT), to assess both cognitive and visual attention, and subjective measures following each condition using the NASA Task Load Index (TLX). Two age cohorts were evaluated: younger drivers between 21 and 36 years of age, and older drivers between 55 and 75 years of age. Participants completed experimental tasks involving interactions with the IVIS to achieve a specific goal (i.e., using the touch screen to tune the radio to a station; using voice commands to find a specified navigation destination, etc.). Performance of tasks varied according to different modes of interaction available in the vehicles. Older drivers took longer to complete tasks, were slower to react to stimuli, and reported higher task demand when interacting with IVIS. Older drivers stand to benefit the most from advancements in-vehicle technology, but ironically may struggle the most to use them. The results document significant age-related costs in the potential for distraction from IVIS interactions on the road.

The Relationship between in-Vehicle Technologies and Self-Regulation among Older Drivers.

The study sought to understand the relationship between in-vehicle technologies (IVTs) and self-regulatory behaviors among older drivers. In a large multi-site study of 2990 older drivers, self-reported data on the presence of IVTs and avoidance of various driving behaviors (talking on a mobile phone while driving, driving at night, driving in bad weather, and making left turns when there is no left turn arrow) were recorded. Self-reports were used to identify whether avoidance was due to self-regulation. Hierarchical logistic regressions were used to determine whether the presence of a particular IVT predicted the likelihood of a given self-regulatory behavior after controlling for other factors. Results suggest that the presence of Integrated Bluetooth/Voice Control systems are related to a reduced likelihood of avoiding talking on a mobile phone while driving due to self-regulation (OR= 0.37, 95% CI= 0.29-0.47). The presence of a Navigation Assistance system was related to a reduced likelihood of avoiding talking on a mobile phone while driving (OR= 0.65, 95% CI= 0.50-0.84) and avoiding driving at night due to self-regulation (OR= 0.80, 95% CI = 0.64-1.00). Present findings suggest in-vehicle technologies may differently influence the self-regulatory behaviors of older drivers.

Preface to the Special Issue on Human Factors and Advanced Vehicle Automation: Of Benefits, Barriers, and Bridges to Safe and Effective Implementation.

OBJECTIVE: The aim of this special issue is to bring together the latest research related to driver interaction with various types of vehicle automation. BACKGROUND: Vehicle technology has undergone significant progress over the past decade, bringing new support features that can assist the driver and take on more and more of the driving responsibilities. METHOD: This issue is comprised of eight articles from international research teams, focusing on different types of automation and different user populations, including driver support features through to highly automated driving systems. RESULTS: The papers comprising this special issue are clustered into three categories: (a) experimental studies of driver interactions with advanced vehicle technologies; (b) analysis of existing data sources; and (c) emerging human factors issues. Studies of currently available and pending systems highlight some of the human factors challenges associated with the driver-system interaction that are likely to become more prominent in the near future. Moreover, studies of more nascent concepts (i.e., those that are still a long way from production vehicles) underscore many attitudes, perceptions, and concerns that will need to be considered as these technologies progress. CONCLUSIONS: Collectively, the papers comprising this special issue help fill some gaps in our knowledge. More importantly, they continue to help us identify and articulate some of the important and potential human factors barriers, design considerations, and research needs as these technologies become more ubiquitous.

Detection of driver manual distraction via image-based hand and ear recognition.

Driving distraction is a leading cause of fatal car accidents, and almost nine people are killed in the US each day because of distracting activities. Therefore, reducing the number of distraction-affected traffic accidents remains an imperative issue. A novel algorithm for detection of drivers' manual distraction was proposed in this manuscript. The detection algorithm consists of two modules. The first module predicts the bounding boxes of the driver's right hand and right ear from RGB images. The second module takes the bounding boxes as input and predicts the type of distraction. 106,677 frames extracted from videos, which were collected from twenty participants in a driving simulator, were used for training (50%) and testing (50%). For distraction classification, the results indicated that the proposed framework could detect normal driving, using the touchscreen, and talking with a phone with F1-score 0.84, 0.69, 0.82, respectively. For overall distraction detection, it achieved F1-score of 0.74. The whole framework ran at 28 frames per second. The algorithm achieved comparable overall accuracy with similar research, and was more efficient than other methods. A demo video for the algorithm can be found at https://youtu.be/NKclK1bHRd4.

Vehicle and Highway Adaptations to Compensate for Sleepy Drivers.

Sleepiness remains a major contributor to road crashes. Driver monitoring systems identify early signs of sleepiness and alert drivers, using real-time analysis of eyelid movements, EEG activity, and steering control. Other vehicle adaptations warn drivers of lane departures or collision hazards, with higher vehicle automation actively taking over vehicle control to prevent run off the road incidents and institute emergency braking. Similarly, road adaptations warn drivers (rumble strips) or mitigate crash severity (barriers). Infrastructure to encourage drivers to use countermeasures, such as rest stops for napping, is also important. The effectiveness of adaptations varies for different road users.

Visual and Cognitive Demands of CarPlay, Android Auto, and Five Native Infotainment Systems.

OBJECTIVE: The present research compared and contrasted the workload associated with using in-vehicle information systems commonly available in five different automotive original equipment manufacturers (OEMs) with that of CarPlay and Android Auto when used in the same vehicles. BACKGROUND: A growing trend is to provide access to portable smartphone-based systems (e.g., CarPlay and Android Auto) that support an expansion of various in-vehicle infotainment system features and functions. METHOD/RESULTS: The study involved on-road testing of 24 participants in each configuration of five vehicles crossed with the three different infotainment systems: the embedded portion of the native OEM systems, CarPlay, and Android Auto. Our analysis found that workload was significantly greater for the embedded portion of the native OEM systems than for CarPlay and Android Auto. The strengths and weaknesses of each CarPlay and Android Auto traded off in such a way that the overall demand associated with using the two systems did not differ. CONCLUSION: CarPlay and Android Auto provided more functionality and resulted in lower levels of workload than the embedded portion of the native OEM infotainment systems. APPLICATION: Potential applications of this research include refinements to CarPlay and Android Auto to address variations in workload as a function of task type, the modality of interaction, and OEM implementation of the system.

Prediction of drowsiness events in night shift workers during morning driving.

The morning commute home is an especially vulnerable time for workers engaged in night shift work due to the heightened risk of experiencing drowsy driving. One strategy to manage this risk is to monitor the driver's state in real time using an in vehicle monitoring system and to alert drivers when they are becoming sleepy. The primary objective of this study is to build and evaluate predictive models for drowsiness events occurring in morning drives using a variety of physiological and performance data gathered under a real driving scenario. We used data collected from 16 night shift workers who drove an instrumented vehicle for approximately two hours on a test track on two occasions: after a night shift and after a night of rest. Drowsiness was defined by two outcome events: performance degradation (Lane-Crossing models) and electroencephalogram (EEG) characterized sleep episodes (Microsleep Models). For each outcome, we assessed the accuracy of sets of predictors, including or not including a driver factor, eyelid measures, and driving performance measures. We also compared the predictions using different time intervals relative to the events (e.g., 1-min prior to the event through 10-min prior). By examining the Area Under the receiver operating characteristic Curve (AUC), accuracy, sensitivity, and specificity of the predictive models, the results showed that the inclusion of an individual driver factor improved AUC and prediction accuracy for both outcomes. Eyelid measures improved the prediction for the Lane-Crossing models, but not for Microsleep models. Prediction performance was not changed by adding driving performance predictors or by increasing the time to the event for either outcome. The best models for both measures of drowsiness were those considering driver individual differences and eyelid measures, suggesting that these indicators should be strongly considered when predicting drowsiness events. The results of this paper can benefit the development of real-time drowsiness detection and help to manage drowsiness to avoid related motor-vehicle crashes and loss.

Modelling driver acceptance of driver support systems.

Driver support systems are intended to enhance driver performance and improve transportation safety. Even though these systems afford safety advantages, they challenge the traditional role of drivers in operating vehicles. Driver acceptance, therefore, is essential for the adoption of new in-vehicle technologies into the transportation system. In this study, a model of driver acceptance of driver support systems was developed. A conceptual driver acceptance model, including several components, was proposed based on a review of current literature. An empirical study was subsequently carried out using an online survey approach. The study collected data on participants' perceptions of two driver support systems (a fatigue monitoring system and an adaptive cruise control system combined with a lane-keeping system) in terms of attitude, perceived usefulness, and other components of driver acceptance. Results identified five components of driver acceptance (attitude, perceived usefulness, endorsement, compatibility, and affordability). The results also confirmed several mediating effects. The developed model was able to explain 85% of the variability in driver acceptance. The model provides an improved understanding how driver acceptance is formed, including which factors affect driver acceptance and how they affect it. The model can also help automakers and researchers to assess the design and estimate the potential use of a driver support system. The model could also be highly beneficial in developing a questionnaire to assess driver acceptance.

Does Talking on a Cell Phone, With a Passenger, or Dialing Affect Driving Performance? An Updated Systematic Review and Meta-Analysis of Experimental Studies.

Objective An up-to-date meta-analysis of experimental research on talking and driving is needed to provide a comprehensive, empirical, and credible basis for policy, legislation, countermeasures, and future research. Background The effects of cell, mobile, and smart phone use on driving safety continues to be a contentious societal issue. Method All available studies that measured the effects of cell phone use on driving were identified through a variety of search methods and databases. A total of 93 studies containing 106 experiments met the inclusion criteria. Coded independent variables included conversation target (handheld, hands-free, and passenger), setting (laboratory, simulation, or on road), and conversation type (natural, cognitive task, and dialing). Coded dependent variables included reaction time, stimulus detection, lane positioning, speed, headway, eye movements, and collisions. Results The overall sample had 4,382 participants, with driver ages ranging from 14 to 84 years ( M = 25.5, SD = 5.2). Conversation on a handheld or hands-free phone resulted in performance costs when compared with baseline driving for reaction time, stimulus detection, and collisions. Passenger conversation had a similar pattern of effect sizes. Dialing while driving had large performance costs for many variables. Conclusion This meta-analysis found that cell phone and passenger conversation produced moderate performance costs. Drivers minimally compensated while conversing on a cell phone by increasing headway or reducing speed. A number of additional meta-analytic questions are discussed. Application The results can be used to guide legislation, policy, countermeasures, and future research.

Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems.

Advanced Driver Assistance Systems (ADAS) are intended to enhance driver performance and improve transportation safety. The potential benefits of these technologies, such as reduction in number of crashes, enhancing driver comfort or convenience, decreasing environmental impact, etc., have been acknowledged by transportation safety researchers and federal transportation agencies. Although these systems afford safety advantages, they may also challenge the traditional role of drivers in operating vehicles. Driver acceptance, therefore, is essential for the implementation of these systems into the transportation system. Recognizing the need for research into the factors affecting driver acceptance, this study assessed the utility of the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Use of Technology (UTAUT) for modelling driver acceptance in terms of Behavioral Intention to use an ADAS. Each of these models propose a set of factors that influence acceptance of a technology. Data collection was done using two approaches: a driving simulator approach and an online survey approach. In both approaches, participants interacted with either a fatigue monitoring system or an adaptive cruise control system combined with a lane-keeping system. Based on their experience, participants responded to several survey questions to indicate their attitude toward using the ADAS and their perception of its usefulness, usability, etc. A sample of 430 surveys were collected for this study. Results found that all the models (TAM, TPB, and UTAUT) can explain driver acceptance with their proposed sets of factors, each explaining 71% or more of the variability in Behavioral Intention. Among the models, TAM was found to perform the best in modelling driver acceptance followed by TPB. The findings of this study confirm that these models can be applied to ADAS technologies and that they provide a basis for understanding driver acceptance.

Age-related differences in fatal intersection crashes in the United States.

OBJECTIVE: Given the aging U.S. population and resulting number of older drivers in the coming years, it is important to understand the factors leading to their involvement in vehicle crashes and develop counter-measures to reduce their frequency and severity. This is also useful for helping older adults "age in place" in terms of accessibility, mobility, quality of life and safety. Thus, the objective of this study was to provide up-to-date data on differences in age-related risks and rates for involvement in fatal intersection motor-vehicle crashes in the US. METHODS: Pooled data for the years 2011-2014 from the FARS, a census of fatal traffic crashes within the 50 States, the District of Columbia, and Puerto Rico, created by the US National Highway Traffic Safety Administration (NHTSA) were used to calculate summary statistics including annualized crash rates. Multivariate logistic regression models were used to evaluate age and gender-related differences in fatal intersection crash risk, controlling for covariates. An induced exposure analysis was conducted to calculate crash involvement ratios (CIRs) for all two-vehicle fatal intersection crashes. Older and younger drivers were compared with respect to the presence of factors related to intersection crashes using a multivariate Poisson regression model. RESULTS: During the period of 2011-2014, among the reported 120,809 fatal accidents in the US involving 178,489 drivers of vehicles, 48,733 (28%) were drivers involved in fatal intersection crashes. Age-adjusted annualized fatal intersection crash rates per 100,000 licensed drivers were highest for drivers aged 85 or older (9.89/100,000), followed by 20 years of age (8.93/100,000). Teen and older drivers (55+ years of age) were over-involved in fatal intersection crashes, drivers from 20 to 54 years old were under-involved. Male and female drivers, 70-74 years of age, were 20% and 21%, respectively, more likely to be involved in a fatal intersection crash than 20-24year olds (of same gender). By age 85, fatal intersection crash risk for all drivers was almost doubled. Significant differences in factors related to crashes involving younger (<65) and older (65+ years) drivers were time of day, lighting and weather conditions, day of week, roadway type and number of lanes, presence of visible traffic controls, speed limit and estimated driving speed, and whether the driver was deemed at fault for the crash CONCLUSION: The results provide the most up-to-date analysis of aging and fatal intersection crash risk in the US, and underscore several trends worthy of further investigation. Older adults face a number of challenges associated with natural aging, including sensory, perceptual, cognitive and motor declines that may impact their driving. As with younger drivers, expanded or renewed approaches to driver training at licensing renewals, as well as safety-based technological advances are viable avenues toward improving the safety outlook for older adults.