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1. Semi-Automated Synthesis of Driving Rules

   Ortiz, Diego; Gilpin, Leilani; Cardenas, Alvaro A. (February 2024, ISOC NDSS)
2. Semi-Automated Synthesis of Driving Rules

   Ortiz, Diego; Gilpin, Leilani; Cardenas, Alvaro A (February 2024, VehicleSec 24)
3. The Effect of Driving Style on Responses to Unexpected Vehicle Cyberattacks

   https://doi.org/10.3390/safety9010005  

   Zhang, Fangda; Wang, Meng; Parker, Jah’inaya; Roberts, Shannon C (March 2023, Safety)

   Vehicle cybersecurity is a serious concern, as modern vehicles are vulnerable to cyberattacks. How drivers respond to situations induced by vehicle cyberattacks is safety critical. This paper sought to understand the effect of human drivers’ risky driving style on response behavior to unexpected vehicle cyberattacks. A driving simulator study was conducted wherein 32 participants experienced a series of simulated drives in which unexpected events caused by vehicle cyberattacks were presented. Participants’ response behavior was assessed by their change in velocity after the cybersecurity events occurred, their post-event acceleration, as well as time to first reaction. Risky driving style was portrayed by scores on the Driver Behavior Questionnaire (DBQ) and the Brief Sensation Seeking Scale (BSSS). Half of the participants also received training regarding vehicle cybersecurity before the experiment. Results suggest that when encountering certain cyberattack-induced unexpected events, whether one received training, driving scenario, participants’ gender, DBQ-Violation scores, together with their sensation seeking measured by disinhibition, had a significant impact on their response behavior. Although both the DBQ and sensation seeking have been constantly reported to be linked with risky and aberrant driving behavior, we found that drivers with higher sensation seeking tended to respond to unexpected driving situations induced by vehicle cyberattacks in a less risky and potentially safer manner. This study incorporates not only human factors into the safety research of vehicle cybersecurity, but also builds direct connections between drivers’ risky driving style, which may come from their inherent risk-taking tendency, to response behavior to vehicle cyberattacks. 

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4. Real‐time driving style classification based on short‐term observations

   https://doi.org/10.1049/cmu2.12405  

   Zheng, Xinhu; Yang, Pengtao; Duan, Dongliang; Cheng, Xiang; Yang, Liuqing (May 2022, IET Communications)

   Abstract Vehicle behaviour prediction provides important information for decision‐making in modern intelligent transportation systems. People with different driving styles have considerably different driving behaviours and hence exhibit different behaviour tendency. However, most existing prediction methods do not consider the different tendencies in driving styles and apply the same model to all vehicles. Furthermore, most of the existing driver classification methods rely on offline learning that requires a long observation of driving history and hence are not suitable for real‐time driving behaviour analysis. To facilitate personalised models that can potentially improve vehicle behaviour prediction, the authors propose an algorithm that classifies drivers into different driving styles. The algorithm only requires data from a short observation window and it is more applicable for real‐time online applications compared with existing methods that require a long term observation. Experiment results demonstrate that the proposed algorithm can achieve consistent classification results and provide intuitive interpretation and statistical characteristics of different driving styles, which can be further used for vehicle behaviour prediction. 

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5. A Study on Learning and Simulating Personalized Car-Following Driving Style

   https://doi.org/10.1109/ITSC55140.2022.9922548  

   Sheng, Shili; Pakdamanian, Erfan; Han, Kyungtae; Wang, Ziran; Feng, Lu (October 2022, 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC))