Towards Intelligible Human-Robot Interaction: An Active Inference Approach to Occluded Pedestrian Scenarios

TL;DR

This paper introduces a novel active inference framework for autonomous vehicles to better predict and react to occluded pedestrians, improving safety and human-like decision-making in uncertain scenarios.

Contribution

It presents a belief-driven approach using active inference, RBPF, and advanced planning techniques to handle occlusion uncertainty in autonomous driving.

Findings

Significantly reduces collision rates in simulations.

Produces explainable, human-like driving behavior.

Outperforms reactive, rule-based, and RL baselines.

Abstract

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.