Quantifying perceived risk in driving: A Monte Carlo approach for obstacle avoidance.

Objective: This study aims to develop a model for quantifying perceived risk in obstacle avoidance, emphasizing how drivers' perceived risk characteristics influence their driving decisions. The research addresses the lack of attention given to modeling risk from the perspective of drivers' risk perceptions.

Methods: Monte Carlo methods are employed to account for the uncertainties and complexities of driving behavior, restoring the probabilistic nature of risk. The proposed method quantifies perceived risk by incorporating drivers' fuzzy perceptions, enabling a quantitative evaluation during obstacle avoidance. A logit model is used to link perceived risk with driving decisions, identifying key factors influencing driver behavior in obstacle avoidance scenarios.

Results: Experimental data revealed significant variations in vehicle trajectories and speed distributions due to differences in drivers' experience and proficiency. The perceived risk indicator (PRI) values for leftward bypasses were higher compared to rightward bypasses, and the receiver operating characteristic (ROC) curve confirmed the PRI's strong predictive ability with an area under the curve (AUC) of 0.820. The logit model showed that both PRI and speed significantly influenced the probability of choosing a rightward bypass, achieving 90% accuracy. Building on the model, the study predicted and visualized the probability of vehicles turning right to avoid obstacles at different positions and speeds within 200 m of the obstacle.

Conclusions: The research offers a framework for traffic professionals to understand driver-perceived risk and decision-making mechanisms. This understanding is beneficial for improving traffic safety and highlights the importance of considering drivers' risk perceptions in modeling driving behavior.