Complex but solvable: towards a cognitive human-like risk-identification model for AV-HV mixed traffic

Human-driven vehicles (HVs) in mixed traffic environments exhibit significant behavioral uncertainty and heterogeneous driving patterns, posing substantial challenges for autonomous vehicles (AVs) in risk identification. This challenge is particularly pronounced in interweaving areas with high traffic dynamics, dense interactions, and complex operational conditions. Existing risk identification methods mainly rely on predefined scenarios or static decision rules, which often lead to limited generalization capability, slow response to extreme risks, and insufficient interpretability in complex mixed traffic environments. To address these limitations, this study draws inspiration from the adaptive learning and consultation behaviors of humans and constructs a cognitive optimization method based on the human behavior-based optimization (HBBO) algorithm. Based on this, a cognitive human-like risk-identification model (CHRIM) is proposed for autonomous vehicle-human-driven vehicle (AV-HV) interactions in interweaving areas. The model simulates key cognitive abilities of human drivers, including abstract understanding of driving features, retrospective reasoning of risk levels, and cognitive optimization of risk identification strategies, enabling adaptive evolution and dynamic updating of risk identification mechanisms. Experiments based on real trajectory data collected from urban expressway interweaving area in Chongqing demonstrate that the proposed method achieves superior performance in risk identification accuracy and robustness. The accuracy (ACC), matthews correlation coefficient (MCC), and kappa (KAP) reach 0.9543, 0.8906, and 0.8782, respectively, significantly outperforming several mainstream methods, including the fine tree model (FTM), random subspace model (RSM), efficient logistic regression model (ELRM), and neural networks (NN). In addition, an explainable analysis framework is developed to provide intuitive insights into human-like risk identification of AVs and to reveal the risk evolution process in interweaving area.