面向自主超车的风险感知决策：一种基于世界模型的混合专家框架

中文摘要 AI 辅助

自主高速公路超车需要具备前瞻性的决策能力，以应对复杂交互、随机交通演化和时间风险累积。然而，标准的安全强化学习方法通常依赖隐式的基于价值的风险估计，而非显式的动力学建模，因此难以准确捕捉多步时域内的复杂风险传播，这种局限常导致局部安全但长期存在大量潜在风险的行为。为解决该问题，本文提出一种基于世界模型的风险感知混合专家（WM-RMoE）框架。首先，学习得到的隐式动力学模型支持并行多步滚动，通过累积风险评估将安全评估从动作层面提升至轨迹层面。其次，为增强不同交互强度下的鲁棒性，采用分层门控机制动态协调长时域、短时域和基于规则的安全模块中的专家。此外，集成高斯混合模型以保留多模态操纵分支，从而缓解行为模式平均化问题。实验结果表明，WM-RMoE在安全合规性、决策稳定性和泛化能力方面显著优于代表性基准方法，且得益于风险感知的公式化，该框架具备独特的能力，可在不同交通密度下生成具有前瞻性且语义清晰的超车操纵。

英文摘要

Autonomous highway overtaking demands foresighted decision-making to handle complex interactions, stochastic traffic evolution, and temporal risk accumulation. However, standard safe reinforcement learning approaches typically rely on implicit value-based risk estimations rather than explicit dynamics modeling, thereby struggling to accurately capture complex risk propagation over multi-step horizons. This limitation frequently results in behaviors that are locally safe but induce substantial latent risks in the long term. To address this, a World Model-based Risk-aware Mixture-of-Experts (WM-RMoE) framework is proposed. First, a learned latent dynamics model facilitates parallel multi-step rollouts, elevating safety assessment from the action level to the trajectory level via cumulative risk evaluation. Second, to enhance robustness under varying interaction intensities, a hierarchical gating mechanism dynamically coordinates experts across long-horizon, short-horizon, and rule-based safety modules. Furthermore, a Gaussian Mixture Model is integrated to preserve multimodal maneuvering branches, thereby mitigating the issue of behavioral mode averaging. Experimental results demonstrate that WM-RMoE significantly outperforms representative baselines in terms of safety compliance, decision stability, and generalization capability. Furthermore, benefiting from the risk-aware formulation, the proposed framework uniquely exhibits the ability to generate foresighted and semantically distinct overtaking maneuvers across diverse traffic densities.

发表机构

• School of Mechanical Engineering, Southeast University（东南大学机械工程学院）
• University of Michigan（密歇根大学）
• Nanyang Technological University（南洋理工大学）

机构由 AI 辅助整理，请以论文原文为准。