Risk Factors Identification During Driving Based On Behavioral And Physiological Measures

Traffic accidents and related fatalities/injuries areglobal safety issues. Within the Driver-Vehicle-Environment(DVE) system, traffic accidents are mainly caused by drivers‘ risk factors.However, theseinternal risk factors are also determined by the externalfactors from the vehicle and road environments, as proposed in the Theory of Risk Homeostasis(TRH). Thus, it becomes imperative to reveal the risk factors from humans in varied road environments and thesubsequent accident-caused mechinanism. This dissertation conducted a series ofoff-line and on-line experimental studies with the advanced approaches of behavioral and physiological measures,to anlysis the underlying mechinanism of traffic accidents from the perspective of drivers‘―perception-cognition-repsonse‖ process.The off-line studies indicated individual differences between risky and other drivers during perception and cognition process.The naturalistic video-based study showed that the risky driving behaviors(i.e., violations and agrresions) werethe major problems on the urban roads of China, which were caused by the competivitve relations between drivers and subsequent interpersonal confilicts. The video-based risk perception test demonstrated the negative correlation between the freuqnency of risky driving and the risk perception level. The decision-making tasks showed that the drivers withhigher riskness traithad higher preferences touncertain and risky choices during decision-making. The feedback-loaced Event Related Potential(ERP) during decision-making further revealed that the neural mechinanism of these risky drivers was less error-revised and more reward-motivated. Thus, it is promsing to improve safety driving behaviors by implementing approprate interventions to these risky factors in drivers‘ perception and cogitivtion levels.The on-line studies were conducted in driving simulator contexts.During the driving task, drivers demonstrated variedbehaviors to compensate thedifferentrisks from road environments. In the low level-risk contexts, drivers decided their dirivng behaviors freely. In themedium-level risk contexts, drivers decided their driving behaviors from alternative choices―go‖ or ―not go‖. In the high-level risk contexts, drivers had to adopt emergent responses to avoid crashes. In medium-level riskcontextswith decision-making, risky drivers made more―go‖decisions than other drivers. In the low-leveland medium-level risk contexts, risky drivers demonstrated different behavioral patterns compared with other drivers, as distinguished by longitudinal velocity and horizontal angular velocity. In the high-level risk context, crashed drivers showed low level of anticipatory hazard, which was distinguished by therising rate of the skin conductance. The above results indicated the key factors from human and accident-caused mechinanism in different road enviorments.In order to predict the accident involvements, this dissertationfinally adopted the pattern recognition method with Discriminant Analysis(DA) based on the behavioral and physiological measures. The results showed that the pattern recognition methods with moremeasures could increase the accuracy and specificity. In this light, it is possible to develop high performance systems to predict the impending accidents and conduct forward interventions accordingly.In summary, this dissertationoffers comprehensive guidelines to measure the drivers‘ risk factors through the behavioral and physiological measures. The findingsprovide more precise descriptions in perception and cognition level to explain the accident-caused mechinanism based on the TRH framework.DA and other pattern recognition methodsprovidepracticable approaches to accurately predict impending accidents based on thesemultiplemeasures. In addition, it is feasible to extend the behavioral and physiological measures in other areas involving human-machine systems.