Modeling Driver Mental Workload for Accident Causation and Prevention

抄録

Past road safety studies have mostly focused on the identification of scenarios involving high accident risk. However, risky scenarios can only describe accident outcomes rather than the real causality. Discussion of driver's cognitive interaction while driving is a necessity for deeper exploration about the nature of accident. To comprehend the entire structure of mental workload, this research proposes a research framework for studying mental models that incorporates task demand and motivated capability. Understanding the contributing factors of mental model and the individual difference in task demand and motivated capability can help evaluate the mental workload. In addition, integrating mental model with accident chain analysis enables exploring information net effect on mental workload. Thus, optimized information can hopefully be defined and provided to drivers in different scenarios without causing additional risk of accidents.