Cognitive Risk-Aware Hierarchical Trajectory Planning for Adaptive Regulation of Driving Caution

Dongjian Song

Abstract: Trajectory planning is crucial for ensuring the safety of intelligent vehicles (IVs) in autonomous driving, especially in complex traffic environments where perception and control uncertainties increase collision risks. Existing risk-aware planning methods often fail to capture the full scope of uncertainties through risk assessment and struggle to consistently integrate risk information throughout the trajectory generation process. Risk factors are often overshadowed by smoothness and dynamic feasibility considerations, leading to excessively high-risk trajectories that fail to meet human driving expectations for caution. To address these challenges, we propose a cognitive risk-aware hierarchical trajectory planning method that adaptively regulates driving caution based on cognitive risk. We introduce cognitive risk assessment using two fields: the anisotropic objective risk field, which accounts for the size and comprehensive motion uncertainty of surrounding obstacles, and the driving cognition field, which considers the IV motion trends and reflects its proactive cognition of objective risks. By fusing these fields, we calculate cognitive risk and incorporate it into a planning framework that combines trajectory search and optimization. By explicitly considering cognitive risk constraints in both stages, the method achieves complete cognitive risk awareness, generating adaptive, safe, and dynamically feasible smooth trajectories. Experimental results in various scenarios demonstrate the effectiveness and superiority of the proposed method. By incorporating cognitive risk assessment, the IV exhibits more cautious driving behavior, aligning with human expectations. Compared to three state-of-the-art methods, our method improves the minimum time-to-collision by over 20%, reduces lane-crossing time by more than 10%, and decreases the average yaw rate by over 12%. In summary, the proposed method ensures higher safety, improves lane-changing efficiency and smoothness.