Comprehensive Evaluation of Freeway Driving Risks Based on Fuzzy Logic
Lian Xie, Jiaxin Zhang, Rui Cheng
Abstract
The quantitative evaluation of driving risk is a crucial prerequisite for intelligent vehicle accident warning, and it is necessary to predict it comprehensively and accurately. Therefore, a simulated driving experiment was conducted with 16 experimental scenarios designed through an orthogonal design, and 44 subjects were recruited to explore the driving risks in different situations. A two-layer fuzzy integrated evaluation model was constructed, which considered the workload as an important element for balancing driving risk and driving behavior. Workload and road environment indicators were taken as the underlying input variables. The results show that the comprehensive evaluation model is well-suited to identify the risks of each scenario. The effectiveness of the proposed method is further confirmed by comparing the results with those of the technique for order preference by similarity to an ideal solution (TOPSIS) model. The proposed method could be used for real-time vehicle safety warning and provide a reference for accident prevention.