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Objective The objective of this study was to assess the effects of different warning messages for an Intersection Movement Assist (IMA) based on drivers' ability to avoid a potential safety hazard. Background An IMA system can detect hazards and warn drivers when it is unsafe to enter an intersection. The effects of different warning information conveyed by these systems are still unknown. Method A driving simulator study with 80 participants was conducted with a red light running (RLR) scenario using a 5 (warnings) x 2 (training) between-subject design. IMA warnings included the messages "Danger," "Brake now," "Vehicle on your left," a beep, and no IMA warning. Training was provided to half of the participants. Analysis of variance and logistic regression models were used to examine differences in drivers' avoidance behavior. Results The analyses showed that all tested warning messages can significantly enhance drivers' avoidance performance. Significant differences were observed in crash occurrence, avoidance behavior (i.e., reaction time and speed change), and eye movements (i.e., fixation pattern and time to first fixation). The effects of training also differed given the warning message provided. Conclusion The "Brake now" message performed best in reducing crash involvement and prompted better avoidance performance. The "Danger" and "Vehicle on your left" messages improved drivers' hazard detection ability. The training showed a potential to enhance the effectiveness of nonspeech warning messages. Application The findings of this study can help designers and engineers better design IMA warning messages for RLR scenarios.

Objective This paper examines drivers' allocation of attention using response time to a tactile detection response task (TDRT) while interacting with an in-vehicle information system (IVIS) over time. Background Longer TDRT response time is associated with higher cognitive workload. However, it is not clear what role is assumed by the human and system in response to varying in-vehicle environments over time. Method A driving simulator study with 24 participants was conducted with a restaurant selection task of two difficulty levels (easy and hard) presented in three modalities (audio only, visual only, hybrid). A linear mixed-effects model was applied to identify factors that affect TDRT response time. A nonparametric time-series model was also used to explore the visual attention allocation under the hybrid mode over time. Results The visual-only mode significantly increased participants' response time compared with the audio-only mode. Females took longer to respond to the TDRT when engaged with an IVIS. The study showed that participants tend to use the visual component more toward the end of the easy tasks, whereas the visual mode was used more at the beginning of the harder tasks. Conclusion The visual-only mode of the IVIS increased drivers' cognitive workload when compared with the auditory-only mode. Drivers showed different visual attention allocation during the easy and hard restaurant selection tasks in the hybrid mode. Application The findings can help guide the design of automotive user interfaces and help manage cognitive workload.

Objective: A driving simulator study was conducted to evaluate the longitudinal effects of an intervention and withdrawal of a lane keeping system on driving performance and cognitive workload. Background: Autonomous vehicle systems are being implemented into the vehicle fleet. However, limited research exists in understanding the carryover effects of long-term exposure. Methods: Forty-eight participants (30 treatment, 18 control) completed eight drives across three separate days in a driving simulator. The treatment group had an intervention and withdrawal of a lane keeping system. Changes in driving performance (standard deviation of lateral position [SDLP] and mean time to collision [TTC]) and cognitive workload (response time and miss rate to a detection response task) were modeled using mixed effects linear and negative binomial regression. Results: Drivers exposed to the lane keeping system had an increase in SDLP after the system was withdrawn relative to their baseline. Drivers with lane keeping had decreased mean TTC during and after system withdrawal compared with manual drivers. There was an increase in cognitive workload when the lane keeping system was withdrawn relative to when the system was engaged. Conclusion: Behavioral adaptations in driving performance and cognitive workload were present during automation and persisted after the automation was withdrawn. Application: The findings of this research emphasize the importance to consider the effects of skill atrophy and misplaced trust due to semi-autonomous vehicle systems. Designers and policymakers can utilize this for system alerts and training.

Cognitive distraction represents an important and growing traffic safety issue, particularly with the increasing computerization of cars. The target paper in this special section describes a protocol for assessing the distraction potential of information and entertainment systems. Cognitive distraction has specific relevance to the challenges facing driving safety but also reflects the more pervasive challenge of generalizing findings in the face of complex contextual and compensatory influences. Peer commentaries from five driving safety experts sketch paths forward in assessing the distraction potential of in-vehicle information technology. A simple, definitive statement regarding the risk of talking to your car is appealing, but the complexity of driver behavior may make such a statement unachievable.

Objectives: An experiment was conducted to assess the effects of distraction mitigation strategies on drivers? performance and productivity while engaged in an in-vehicle information system task. Background: Previous studies show that in-vehicle tasks undermine driver safety and there is a need to mitigate driver distraction. Method: An advising strategy that alerts drivers to potential dangers and a locking strategy that prevents the driver from continuing the distracting task were presented to 16 middle-aged and 12 older drivers in a driving simulator in two modes (auditory, visual) and two road conditions (curves, braking events). Results: Distraction was a problem for both age groups. Visual distractions were more detrimental than auditory ones for curve negotiation, as depicted by more erratic steering, F(6, 155) = 26.76, p < .05. Drivers did brake more abruptly under auditory distractions, but this effect was mitigated by both the advising, t(155) = 8.37, p < .05, and locking strategies, t(155) = 8.49, p < .05. The locking strategy also resulted in longer minimum time to collision for middle-aged drivers engaged in visual distractions, F(6, 138) = 2.43, p < .05. Conclusions: Adaptive interfaces can reduce abrupt braking on curve entries resulting from auditory distractions and can also improve the braking response for distracted drivers. Application: These strategies can be incorporated into existing in-vehicle systems, thus mitigating the effects of distraction and improving driver performance.

Objective: The aim of this study was to evaluate the effects of secondary tasks on the driving performance of individuals with mild traumatic brain injuries (TBIs). Background: Studies suggest detrimental impacts of driving with TBI or while distracted but the impact of driver distraction on TBI drivers is not well documented. Method: Bayesian regression models were used to estimate the effect of relatively simple secondary tasks on driving performance of TBI and healthy control (HC) drivers. A driving simulator was used to develop prior distribution of task effects on driving performance for HCs. An on-road study was conducted with TBI and HC drivers to generate effect estimates for the posterior distributions. The Bayesian models were also compared to frequentist models. Results: During a coin-sorting task, all drivers exhibited larger maximum lateral acceleration and larger standard deviation of speed than in a baseline driving segment. There were no significant driving performance differences between the TBI and the HC drivers during the tasks. Across all tasks, TBI drivers spent more time looking at the tasks and made more frequent glances toward the tasks. Conclusions: The findings show that even drivers with mild TBI have significantly longer and more glances toward the tasks compared to the HCs. Application: This study demonstrates a Bayesian approach and how the results differ from frequentist statistics. Using prior distributions in a Bayesian model helps account for the probabilities associated with otherwise unknown parameters. This method strengthens the Bayesian parameter estimates compared to that of a frequentist model.

Objective: The aim of this study was to examine variations in drivers' foot behavior and identify factors associated with pedal misapplications. Background: Few studies have focused on the foot behavior while in the vehicle and the mishaps that a driver can encounter during a potentially hazardous situation. Method: A driving simulation study was used to understand how drivers move their right foot toward the pedals. The study included data from 43 drivers as they responded to a series of rapid traffic signal phase changes. Pedal application types were classified as (a) direct hit, (b) hesitated, (c) corrected trajectory, and (d) pedal errors (incorrect trajectories, misses, slips, or pressed both pedals). A mixed-effects multinomial logit model was used to predict the likelihood of one of these pedal applications, and linear mixed models with repeated measures were used to examine the response time and pedal duration given the various experimental conditions (stimuli color and location). Results: Younger drivers had higher probabilities of direct hits when compared to other age groups. Participants tended to have more pedal errors when responding to a red signal or when the signal appeared to be closer. Traffic signal phases and locations were associated with pedal response time and duration. The response time and pedal duration affected the likelihood of being in one of the four pedal application types. Conclusion and Application: Findings from this study suggest that age-related and situational factors may play a role in pedal errors, and the stimuli locations could affect the type of pedal application.

Objective: The objective of this study was to investigate use patterns among early adopters of adaptive cruise control (ACC). Background: Extended use of ACC may influence a driver's behavior in the long term, which can have unintended safety consequences. Method: The authors examined the use of a motion-based simulator by 24 participants (15 males and 9 females). Cluster analysis was performed on drivers' use of ACC and was based on their gap settings, speed settings, number of warnings issued, and ACC disengaged. The data were then examined on the basis of driving performance measures and drivers' subjective responses to trust in ACC, understanding of system operations, and driving styles. Driving performance measures included minimum time headway, adjusted minimum time to collision, and drivers' reaction time to critical events. Results: Three groups of drivers were observed on the basis of risky behavior, moderately risky behavior, and conservative behavior. Drivers in the conservative group stayed farther behind the lead vehicle than did drivers in the other two groups. Risky drivers responded later to critical events and had more ACC warnings issued. Conclusion: Safety consequences with ACC may be more prevalent in some driver groups than others. The findings suggest that these safety implications are related to trust in automation, driving styles, understanding of system operations, and personalities. Application: Potential applications of this research include enhanced design for next-generation ACC systems and countermeasures to improve safe driving with ACC.