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.

翻译：遮挡行人的突然出现是自动驾驶领域面临的关键安全挑战。传统的基于规则或纯数据驱动的方法难以应对这类长尾场景固有的高度不确定性。为应对这一挑战，我们提出了一种基于主动推理的新型框架，该框架赋予智能体一种类人的、信念驱动的机制。我们的框架利用Rao-Blackwellized粒子滤波器来高效估计行人的混合状态。为了模拟人类在不确定性下的认知过程，我们引入了条件信念重置机制和假设注入技术，以显式建模关于行人多种潜在意图的信念。规划通过一种经交叉熵方法增强的模型预测路径积分控制器实现，该控制器将交叉熵方法的高效迭代搜索与模型预测路径积分固有的鲁棒性相结合。仿真实验表明，与反应式、基于规则以及强化学习的基线方法相比，我们的方法显著降低了碰撞率，同时展现出可解释且类人的驾驶行为，反映了智能体内部的信念状态。