用注意力机制提升自动驾驶车辆在高速汇入时的协作效率。
Resolve Highway Conflict in Multi-Autonomous Vehicle Controls with Local State Attention
- 引入局部状态注意力模块，压缩邻近车辆关键信息
- 在高密度交通中汇入效率显著优于主流基线
- 适合研究多车协同控制与交通场景泛化问题者
在混合交通环境中，自动驾驶车辆需适应人类驾驶车辆及其他异常驾驶情况。该场景可建模为自主车辆间具有完全合作奖励的多智能体强化学习（MARL）环境。尽管多智能体近端策略优化等方法在训练MARL任务时有效，但常无法解决智能体间的局部冲突，且难以泛化至随机事件。本文提出局部状态注意力模块，通过自注意力操作压缩附近车辆的关键信息，以缓解交通中的冲突。在模拟高速公路汇入场景中，以优先通行车辆作为意外事件，本方法能优先处理其他车辆信息，有效管理汇入过程。结果表明，在高密度交通条件下，相比主流基线，汇入效率显著提升。
原文摘要 · Abstract (English)
In mixed-traffic environments, autonomous vehicles must adapt to human-controlled vehicles and other unusual driving situations. This setting can be framed as a multi-agent reinforcement learning (MARL) environment with full cooperative reward among the autonomous vehicles. While methods such as Multi-agent Proximal Policy Optimization can be effective in training MARL tasks, they often fail to resolve local conflict between agents and are unable to generalize to stochastic events. In this paper, we propose a Local State Attention module to assist the input state representation. By relying on the self-attention operator, the module is expected to compress the essential information of nearby agents to resolve the conflict in traffic situations. Utilizing a simulated highway merging scenario with the priority vehicle as the unexpected event, our approach is able to prioritize other vehicles' information to manage the merging process. The results demonstrate significant improvements in merging efficiency compared to popular baselines, especially in high-density traffic settings.
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