Скачать или смотреть 【自動運転】死角の歩行者を"予測する脳"が事故率を激減させた件

論文情報
・url: http://arxiv.org/html/2602.23109v1
・title: Towards Intelligible Human-Robot Interaction: An Active Inference Approach to Occluded Pedestrian Scenarios
・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.
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見えない歩行者にどう対処するか。自動運転最大の難関に、脳科学から生まれた「能動的推論」という理論で挑んだ研究が登場しました。従来の強化学習では衝突率27%だったのに対し、この手法はわずか5.3%。人間のように「もしかしたら」と予測し、状況に応じて柔軟に判断する。説明可能で信頼できる自動運転の未来が、すぐそこまで来ています。