Free Energy-Inspired Cognitive Risk Integration for AV Navigation in Pedestrian-Rich Environments

--Recent advances in autonomous vehicle (A V) behavior planning have shown impressive social interaction capabilities when interacting with other road users. However, achieving human-like prediction and decision-making in interactions with vulnerable road users remains a key challenge in complex multi-agent interactive environments. Existing research focuses primarily on crowd navigation for small mobile robots, which cannot be directly applied to A Vs due to inherent differences in their decision-making strategies and dynamic boundaries. Moreover, pedestrians in these multi-agent simulations follow fixed behavior patterns that cannot dynamically respond to A V actions. T o overcome these limitations, this paper proposes a novel framework for modeling interactions between the A V and multiple pedestrians. In this framework, a cognitive process modeling approach inspired by the Free Energy Principle is integrated into both the A V and pedestrian models to simulate more realistic interaction dynamics. Specifically, the proposed pedestrian Cognitive-Risk Social Force Model adjusts goal-directed and repulsive forces using a fused measure of cognitive uncertainty and physical risk to produce human-like trajectories. Meanwhile, the A V leverages this fused risk to construct a dynamic, risk-aware adjacency matrix for a Graph Convolutional Network within a Soft Actor-Critic architecture, allowing it to make more reasonable and informed decisions. Simulation results indicate that our proposed framework effectively improves safety, efficiency, and smoothness of A V navigation compared to the state-of-the-art method. N recent years, rapid advancements in autonomous driving technology have enabled autonomous vehicles (A V) to expand beyond simple, structured highway environments and to be increasingly deployed in more complex urban environments [1]. They are expected to become a key component of future urban transportation systems [2]. Unlike structured roads with clear traffic rules and physical lane separations, shared spaces in cities such as squares, campuses, and residential areas usually lack right-of-way regulations and explicit physical boundaries [3] between vehicles and pedestrians. Y afei Wang is with the School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China (email: wyfjlu@sjtu.edu.cn) The high uncertainty and dynamic nature of human behavior [5], especially when multiple pedestrians are moving at the same time, significantly complicate the A V's decision-making process. To ensure both safety and efficiency, A Vs must make real-time decisions and continuously adapt their strategies in response to surrounding pedestrian behaviors.