Abstract
The effect of driver's physiological and psychological characteristics on traffic safety is represented mainly as driver's propensity. Previous researches focus mostly on psychology test and its influence on traffic safety from relative static and macroscopic perspective. However, in the field of vehicle active safety, there are few studies on driver's affective measurement and computing from microcosmic and dynamic perspective, and previous researchers did not consider the influence of environment. The emphasis is about situation factors which directly influence driver's affection in all environment factors under two-lane condition. Various experiments are designed to collect driver's microdynamic information, and characteristics of driver's propensity toward different environments are extracted using genetic simulated annealing algorithm. Results show that the method can provide a basis to establish dynamic recognition model of driver's propensity further which is adapted to multilane environment.
Highlights
The effect of driver’s physiological and psychological characteristics on traffic safety is represented mainly as driver’s propensity
In the field of vehicle active safety, there are few studies on driver’s affective measurement computing from microcosmic and dynamic perspective, and previous researchers did not consider the influence of environment changes
Rowe et al [1] reported the construction of a self-report Violation Willingness Scale for predrivers, examination of the existing Attitudes to Driving Violations Scale in predrivers, and some preliminary data on the development of propensity to risky driving
Summary
The effect of driver’s physiological and psychological characteristics on traffic safety is represented mainly as driver’s propensity. In the field of vehicle active safety, there are few studies on driver’s affective measurement computing from microcosmic and dynamic perspective, and previous researchers did not consider the influence of environment changes. Di Stasi et al [4] aimed to uncover whether emotional auditory stimuli could affect risky behaviour in hazardous situations These results suggested that auditory warning systems for vehicles should avoid using emotionladen sounds, as their affective content might diminish their utility to increase driving alertness. Lu et al [10] explored how and why anger and fear influenced driving risk perception. Hu et al [12] explored how two states of affect, emotion and mood, would influence driver’s risky driving behavior through risk perception and risk attitude. Characteristics of driver’s propensity in different environments are extracted using genetic simulated annealing algorithm, and the characteristics provide a basis to further establish dynamic recognition model of driver’s propensity which is adapted to multi-lane environment
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