The Effect of Driving Style on Responses to Unexpected Vehicle Cyberattacks

Zhang, Fangda; Wang, Meng; Parker, Jah’inaya; Roberts, Shannon C. · 2023 · Crossref

DOI: 10.3390/safety9010005

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Summary

This study investigates how drivers’ risky driving styles influence their behavioral responses to unexpected vehicle cyberattacks, addressing a critical gap in vehicle cybersecurity research where human factors have been understudied compared to technological vulnerabilities. As modern vehicles become increasingly connected cyber-physical systems, they are susceptible to attacks that can cause sudden, unpredictable disruptions such as false warnings or loss of control. The authors aimed to determine whether self-reported risky driving behaviors and personality traits, specifically sensation seeking, correlate with how drivers react to these simulated cyberattack-induced scenarios. The researchers conducted a driving simulator experiment with 32 participants aged 18–26. Participants completed a baseline drive followed by three experimental drives featuring distinct cyberattack simulations: auditory sirens with no visible source, repeated dashboard warning signs, and involuntary lane weaving. Half of the participants received prior training on vehicle cybersecurity, and half received in-vehicle warnings after the events occurred. Driving style was assessed using the Driver Behavior Questionnaire (DBQ) and the Brief Sensation Seeking Scale (BSSS). Response behavior was quantified through three metrics: delta velocity (change in speed), post-event acceleration, and time to first reaction, which was measured via video analysis of actions like checking mirrors or gripping the wheel. Statistical analysis employed multiple linear regression to evaluate the impact of independent variables, including gender, age, training status, and DBQ/BSSS scores, on these response metrics. The results indicated that driving scenario, training status, gender, DBQ-Violation scores, and sensation seeking (specifically disinhibition) significantly impacted response behavior. Participants exhibited higher delta velocity during siren and dashboard sign events compared to lane changes, suggesting a tendency to increase speed immediately after these specific attacks. Notably, the study found a counterintuitive relationship regarding sensation seeking: drivers with higher sensation seeking scores tended to respond to cyberattack-induced situations in a less risky and potentially safer manner than those with lower scores. This contrasts with the typical association between sensation seeking and reckless driving, suggesting that high sensation seekers may possess better focused attention or adaptability when facing unexpected hazards. These findings highlight the complex role of human factors in vehicle cybersecurity, demonstrating that inherent risk-taking tendencies do not uniformly predict unsafe responses to cyber threats. The study suggests that drivers with higher sensation seeking may be better equipped to handle the unpredictability of cyberattacks, potentially due to enhanced attentional capabilities. This research underscores the importance of integrating human behavioral analysis into cybersecurity frameworks, as driver personality and training significantly influence safety outcomes during cyber incidents. The results imply that future safety interventions and vehicle designs should account for individual differences in driver psychology rather than relying solely on technological safeguards.

Key finding

Drivers with higher sensation-seeking scores tended to respond to unexpected driving situations induced by vehicle cyberattacks in a less risky and potentially safer manner.

Methodology

simulator

Sample size: 32

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• automation surprise
• sex gender
• behavioral adaptation risk compensation
• sensation seeking
• risk taking
• driver post crash behavior

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: behavioral performance data
• Theoretical Contribution: computational model, theory or model