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This paper introduces an Active Inference framework for autonomous vehicles to handle occluded pedestrian scenarios by endowing the agent with a belief-driven mechanism. The framework uses a Rao-Blackwellized Particle Filter (RBPF) for hybrid state estimation and incorporates a Conditional Belief Reset mechanism and Hypothesis Injection to model beliefs about pedestrian intentions. Results from simulation experiments show a significant reduction in collision rates compared to reactive, rule-based, and RL baselines, while also demonstrating explainable and human-like driving behavior.
Mimicking human intuition, this autonomous driving system anticipates hidden pedestrian behavior by actively maintaining and updating beliefs about their intentions, leading to safer and more explainable navigation.
The sudden appearance of occluded pedestrians presents a critical safety challenge in autonomous driving. Conventional rule-based or purely data-driven approaches struggle with the inherent high uncertainty of these long-tail scenarios. To tackle this challenge, we propose a novel framework grounded in Active Inference, which endows the agent with a human-like, belief-driven mechanism. Our framework leverages a Rao-Blackwellized Particle Filter (RBPF) to efficiently estimate the pedestrian's hybrid state. To emulate human-like cognitive processes under uncertainty, we introduce a Conditional Belief Reset mechanism and a Hypothesis Injection technique to explicitly model beliefs about the pedestrian's multiple latent intentions. Planning is achieved via a Cross-Entropy Method (CEM) enhanced Model Predictive Path Integral (MPPI) controller, which synergizes the efficient, iterative search of CEM with the inherent robustness of MPPI. Simulation experiments demonstrate that our approach significantly reduces the collision rate compared to reactive, rule-based, and reinforcement learning (RL) baselines, while also exhibiting explainable and human-like driving behavior that reflects the agent's internal belief state.
