面向异构混合自动驾驶中一致性认知决策的多层图强化学习
Multilevel Graph Reinforcement Learning for Consistent Cognitive Decision-making in Heterogeneous Mixed Autonomy
AI总结：
针对异构混合自动驾驶中复杂时空交互带来的决策难题，提出融合多层图表示、多头注意力的异步并行分层强化学习框架，实现CAVs类人一致性决策，经高速公路复杂场景验证有效，推动相关决策机制发展。
摘要：
在异构混合自动驾驶领域，车辆在复杂的非欧空间中存在动态空间关联与非线性时间交互，这些复杂性给传统决策框架带来了严峻挑战。针对该问题，我们提出一种融合多层图表示的分层强化学习框架，该框架可有效理解并建模在不确定交通条件下、搭载不同决策系统的行驶车辆之间的时空交互关系。本方法以多层图表示理论为基础，封装了非欧空间中固有的时空关系；通过加权图表征节点间的时空特征，解决了动态图中固有的度不平衡问题。我们将异步并行分层强化学习与多层图表示、多头注意力机制相结合，使网联自动驾驶车辆（CAVs）具备类人类认知的能力，可在多个关键维度上实现一致性决策。我们在高密度、高随机性、高动态性的高速公路复杂环境中验证了所提决策策略的有效性，并通过消融实验、对比分析及时空轨迹评估对框架性能进行了测评。本研究对异构混合自动驾驶领域中类人类认知功能的决策机制进行了定量分析，推动了多维度决策策略与精细化注意力资源分配的发展。
英文摘要：
In the realm of heterogeneous mixed autonomy, vehicles experience dynamic spatial correlations and nonlinear temporal interactions in a complex, non-Euclidean space. These complexities pose significant challenges to traditional decision-making frameworks. Addressing this, we propose a hierarchical reinforcement learning framework integrated with multilevel graph representations, which effectively comprehends and models the spatiotemporal interactions among vehicles navigating through uncertain traffic conditions with varying decision-making systems. Rooted in multilevel graph representation theory, our approach encapsulates spatiotemporal relationships inherent in non-Euclidean spaces. A weighted graph represents spatiotemporal features between nodes, addressing the degree imbalance inherent in dynamic graphs. We integrate asynchronous parallel hierarchical reinforcement learning with a multilevel graph representation and a multi-head attention mechanism, which enables connected autonomous vehicles (CAVs) to exhibit capabilities akin to human cognition, facilitating consistent decision-making across various critical dimensions. The proposed decision-making strategy is validated in challenging environments characterized by high density, randomness, and dynamism on highway roads. We assess the performance of our framework through ablation studies, comparative analyses, and spatiotemporal trajectory evaluations. This study presents a quantitative analysis of decision-making mechanisms mirroring human cognitive functions in the realm of heterogeneous mixed autonomy, promoting the development of multi-dimensional decision-making strategies and a sophisticated distribution of attentional resources.
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