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1. A Simulator Study on Drivers’ Response and Perception Towards Vehicle Cyberattacks

   https://doi.org/10.1177/1071181319631310  

   Zhang, Fangda; Petit, Jonathan; Roberts, Shannon C. (November 2019, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   The introduction of advanced technologies has made driving a more automated activity. However, most vehicles are not designed with cybersecurity considerations and hence, they are susceptible to cyberattacks. When such incidents happen, it is critical for drivers to respond properly. The goal of this study was to observe drivers’ responses to unexpected vehicle cyberattacks while driving in a simulated environment and to gain deeper insights into their perceptions of vehicle cybersecurity. Ten participants completed the experiment and the results showed that they perceived and responded differently to each vehicle cyberattack. Participants correctly identified the cybersecurity issue and took according action when the issue caused a noticeable visual and auditory response. Participants preferred to be clearly informed about what happened and what to do through a combination of visual, tactile, and auditory warnings. The lack of knowledge of vehicle cybersecurity was obvious among participants. 

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2. How do drivers respond to vehicle cyberattacks? A driving simulator study

   https://doi.org/10.1177/1071181322661506  

   Parker, Jah'inaya; Zhang, Fangda; Wang, Meng; Roberts, Shannon C (September 2022, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Modern vehicles are embedded with numerous electronic components, making them more advanced and automated, while also making them vulnerable to cyberattacks. This study investigated how drivers respond to unexpected, cyber-attack-induced situations through a driving simulator study. It also examined differences in driver responses if they were trained or received warning messages on how to mitigate the effect of a vehicle cyberattack. The findings suggest that drivers' responses to cyberattacks vary based on the severity of the event. Those who receive training are much more likely to drive cautiously when the vehicle behaves unexpectedly and those who receive warning messages are likely to view them, but not necessarily take action. These results have far reaching implications into the utility of training programs in improving driver behavior and leave future work in terms of optimizing warning message systems. 

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3. Learner Driver Experience and Teenagers’ Crash Risk During the First Year of Independent Driving

   https://doi.org/10.1001/jamapediatrics.2020.0208  

   Ehsani, Johnathon P.; Gershon, Pnina; Grant, Brydon J.; Zhu, Chunming; Klauer, Sheila G.; Dingus, Tom A.; Simons-Morton, Bruce G. (April 2020, JAMA pediatrics)

   null (Ed.)

   IMPORTANCE One mechanism for teenagers’ elevated crash risk during independent driving may be inadequate learner driving experience. OBJECTIVE To determine how learner driver experience was associated with crash risk during the first year of independent driving. DESIGN, SETTING, AND PARTICIPANTS Youth aged 15.5 to 16.1 years at recruitmentwere eligible to participate. Participants’ vehicles were instrumented with sensors, and driving was recorded during the learner period through 1 year of independent driving. Data were collected from January 2011 through August 2014 in southwestern Virginia. EXPOSURES The amount, consistency and variety of practice, driving errors, and kinematic risky driving (KRD) rates during the learner period were recorded. Surveys, including one on sensation-seeking personality traits, were assessed at baseline. MAIN OUTCOMES AND MEASURES Cox proportional hazard regressions examined associations between individual characteristics and learner driving experience with driving time to first crash and all crashes in the first year of independent driving. So that hazard ratios (HRs) can be directly comparable, units of measurement were standardized to the interquartile range. RESULTS Of 298 individuals who responded to recruitment, 90 fulfilled the criteria and 82 completed the study (of whom 75 were white [91%] and 44 were girls [54%]). Teenage participants drove a mean (SD) of 1259.2 (939.7) miles over 89 days during the learner period. There were 49 property-damage crashes and/or police-reportable crashes during independent driving. Factors associated with driving time to first crash included higher sensation-seeking personality scale scores (HR, 1.67 [95%CI, 1.08-2.57] per 0.75-unit increase), learner driving KRD rates (HR, 1.27 [95%CI, 1.12-1.43] per 9.24-unit increase), and learner driving errors (HR, 0.44 [95%CI, 0.22-0.86] per increase of 6.48 errors). Similar results were obtained for all crashes in the first year, with the addition of consistency of learner driving practice (HR, 0.61 [95%CI, 0.38-0.99] per 0.23-unit increase). CONCLUSIONS AND RELEVANCE Individual characteristics and learner driving experiences were associated with crash risk during independent driving. As expected, there was an association between sensation seeking and crashes. Elevated KRD rates during the learner period may reflect risky driving behavior among novices or tolerance to abrupt maneuvers by parents who supervise driving. Consistent practice throughout the learner period could reduce teenage crash risk, which is supported by learning theories indicating distributed practice is effective for developing expertise. Errors during practice may constitute learning events that reinforce safer driving. Physicians could encourage parents to provide opportunities for regular practice driving and monitor their teenager’s KRD rates during the learner period using in-vehicle or smartphone-based technology. 

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4. Investigating the Effects of Automated Driving Styles and Driver’s Driving Styles on Driver Trust, Acceptance, and Take Over Behaviors

   https://doi.org/10.1177/1071181320641484  

   Ma, Zheng; Zhang, Yiqi (December 2020, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   null (Ed.)

   Autonomous Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, driver could be reluctant to ride with AVs due to the lack of trust and acceptance of AV’s driving styles. The present study investigated the impact of driver’s driving style (aggressive/defensive) and the designed driving styles of AVs (aggressive/defensive) on driver’s trust, acceptance, and take-over behavior in fully autonomous vehicles. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve scenarios in either an aggressive AV or a defensive AV. Results revealed that drivers’ trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV’s driving style and driver’s driving style. The findings implied that driver’s individual differences should be considered in the design of AV’s driving styles to enhance driver’s trust and acceptance of AVs and reduce undesired take over behaviors. 

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5. Learning naturalistic driving environment with statistical realism

   https://doi.org/10.1038/s41467-023-37677-5  

   Yan, Xintao; Zou, Zhengxia; Feng, Shuo; Zhu, Haojie; Sun, Haowei; Liu, Henry X. (April 2023, Nature Communications)

   Abstract For simulation to be an effective tool for the development and testing of autonomous vehicles, the simulator must be able to produce realistic safety-critical scenarios with distribution-level accuracy. However, due to the high dimensionality of real-world driving environments and the rarity of long-tail safety-critical events, how to achieve statistical realism in simulation is a long-standing problem. In this paper, we develop NeuralNDE, a deep learning-based framework to learn multi-agent interaction behavior from vehicle trajectory data, and propose a conflict critic model and a safety mapping network to refine the generation process of safety-critical events, following real-world occurring frequencies and patterns. The results show that NeuralNDE can achieve both accurate safety-critical driving statistics (e.g., crash rate/type/severity and near-miss statistics, etc.) and normal driving statistics (e.g., vehicle speed/distance/yielding behavior distributions, etc.), as demonstrated in the simulation of urban driving environments. To the best of our knowledge, this is the first time that a simulation model can reproduce the real-world driving environment with statistical realism, particularly for safety-critical situations. 

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