“Pay attention to that aggressive vehicle”: The effect of Aggressive Vehicle Warning Systems on driving behavior and perceived workload

Wang, Yi; Zhang, Wei · 2024 · Crossref

DOI: 10.54941/ahfe1005228

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Summary

This study investigates the impact of Aggressive Vehicle Warning Systems (AVWS) on driving behavior and perceived workload, addressing the safety risks posed by aggressive driving. Motivated by the prevalence of aggressive behaviors and the potential of connected vehicle (CV) technology to facilitate vehicle-to-vehicle communication, the researchers aimed to determine if warning drivers about nearby aggressive vehicles could improve safety performance and reduce cognitive load. The study specifically examined how AVWS, which provides visual and auditory alerts based on detected driving styles, influences driver responses during hazard events compared to a baseline condition with no additional information. The experiment utilized a driving simulator with 18 licensed participants randomly assigned to either an AVWS group or a baseline group. Participants drove an 11 km urban route encountering seven specific hazard events: sudden braking, lane changes, traffic rule violations at intersections, and two scenarios involving system errors (false alarms and miss alarms). The AVWS group received visual cues on a head-up display and auditory beeps when an aggressive vehicle was nearby. Data collected included driving performance metrics such as collision count, response time, minimum time-to-collision (minTTC), speed standard deviation, and brake pedal depth, as well as perceived workload measured via the NASA-TLX questionnaire. Results indicated that the AVWS significantly improved safety in unpredictable intersection events, where the AVWS group exhibited a significantly larger minTTC, indicating a lower collision risk. In emergency braking scenarios, the AVWS group showed shorter response times, particularly when the aggressive vehicle was further away, though this was accompanied by a smaller minTTC, suggesting less cautious driving. The AVWS group also experienced zero collisions compared to three in the baseline group. Regarding workload, the AVWS group reported lower levels of mental workload and frustration, although these differences did not reach statistical significance. However, the system introduced risks during error conditions; in false alarm scenarios, drivers reacted with greater speed variability and brake pressure, while in miss alarm scenarios, they faced higher collision risks due to reduced caution. Additionally, the AVWS group tended to change lanes more frequently, potentially to avoid aggressive vehicles. The findings suggest that AVWS can effectively orient driver attention and reduce collision risks in specific high-risk scenarios like intersections, while also alleviating mental workload. However, the study highlights critical design implications: warnings must be reliable, as discrepancies between system alerts and actual vehicle behavior can lead to erratic driving and increased risk. The authors conclude that while AVWS offers benefits for intelligent vehicle design, careful consideration of privacy, data security, and the potential for negative traffic flow impacts from increased lane changing is necessary. Future research should expand sample sizes and compare AVWS with existing driver assistance systems to further validate its efficacy.

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Topics

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• aggressive driving
• traffic density
• automation surprise
• behavioral adaptation risk compensation

Information type

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