Influence of adverse weather on drivers’ perceived risk during car following based on driving simulations

Chen, Chen; Zhao, Xiaohua; Liu, Hao; Ren, Guichao; Liu, Xiaoming · 2019 · OpenAlex-citations

DOI: 10.1007/s40534-019-00197-4

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Summary

This study investigates how adverse weather conditions influence drivers’ perceived risk during car-following maneuvers, addressing a gap in understanding subjective risk perception under controlled environmental variables. While previous research relied on field data that often lacked extreme weather samples or failed to isolate weather effects from traffic volume, this research utilizes driving simulation to precisely control visibility and road friction. The study is motivated by the need to quantify how drivers dynamically adjust their behavior based on risk homeostasis theory, which posits that drivers adjust operations to maintain a target risk level. The methodology employed a fixed-base driving simulator with 31 professional drivers. The experimental design included 11 weather conditions (clear sky, four levels of fog, four levels of rain, and two levels of snow) and three road types (basic segment, upslope, downslope). Three car-following scenarios were simulated based on lead vehicle motion: cruising, accelerating, and decelerating. Seven vehicle motion indicators, such as headway distance and acceleration, were selected to represent perceived risk. These indicators were normalized and integrated into a single Perceived Risk Index (PRI) using the entropy weight method. Multiple linear regression and repeated measures analysis of variance were then used to analyze the impact of weather and road type on the PRI and individual indicators. The results demonstrate that both weather conditions and road type significantly affect car-following behavior. Generally, drivers’ perceived risk increased as weather conditions worsened, with extremely heavy snow eliciting the highest risk levels. However, under conditions of extremely poor visibility, such as heavy dense fog, the measured perceived risk was lower; this counterintuitive finding is attributed to the difficulty of vehicle operation and limited visibility, which caused drivers to maintain larger headways or follow the lead car more passively rather than actively managing risk through speed adjustments. Additionally, drivers exhibited higher perceived risk on downslopes compared to upslopes. The analysis also revealed that drivers were more cautious regarding the deceleration of lead vehicles than their acceleration, showing greater sensitivity to speed reduction in adverse conditions. The significance of this research lies in its ability to quantify perceived risk using objective vehicle motion parameters under controlled conditions, overcoming the limitations of field data. The findings highlight that the relationship between weather severity and perceived risk is not strictly linear, particularly in extreme visibility conditions where operational constraints alter driver behavior. This provides valuable insights for traffic safety modeling and the development of adaptive cruise control systems that account for human risk perception in varying weather and road geometries.

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• weather rain fog snow
• traffic density

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• Theoretical Contribution: theory or model, computational model