提出双交互感知协同控制策略，提升混合交通中自动驾驶车辆的通行效率。
Dual-Interaction-Aware Cooperative Control Strategy for Alleviating Mixed Traffic Congestion
- 区分自动驾驶车间协作与与人类驾驶车辆的观测交互，增强局部感知。
- 通过交互感知价值估计提升全局交通理解，优化策略更新方向。
- 适用于智能交通系统中复杂瓶颈场景，适合研究自动驾驶协同控制者。
随着智能交通系统的发展，联网与自动化车辆（CAVs）有望通过协同策略显著降低交通拥堵，尤其在瓶颈区域。然而，混合交通环境中人类驾驶车辆（HDVs）行为的不确定性与多样性给CAVs的协作带来重大挑战。本文提出一种双交互感知协同控制（DIACC）策略，在多智能体强化学习框架下增强CAVs在混合交通瓶颈场景中的局部与全局交互感知能力。DIACC包含三项创新：1）去中心化的交互自适应决策模块（D-IADM），通过区分CAV-CAV协作交互与CAV-HDV观测交互，提升智能体的局部交互感知；2）中心化的交互增强评价器（C-IEC），通过交互感知的价值估计提升评分子的全局交通理解，为策略更新提供更精准指导；3）采用软最小值聚合与温度退火的奖励设计，优先关注交互密集型场景。此外，轻量级的主动安全动作精化（PSAR）模块引入基于规则的修正，加速训练收敛。实验结果表明，相比规则基模型和基准MARL模型，DIACC在交通效率和适应性方面均有显著提升。
原文摘要 · Abstract (English)
As Intelligent Transportation System (ITS) develops, Connected and Automated Vehicles (CAVs) are expected to significantly reduce traffic congestion through cooperative strategies, such as in bottleneck areas. However, the uncertainty and diversity in the behaviors of Human-Driven Vehicles (HDVs) in mixed traffic environments present major challenges for CAV cooperation. This paper proposes a Dual-Interaction-Aware Cooperative Control (DIACC) strategy that enhances both local and global interaction perception within the Multi-Agent Reinforcement Learning (MARL) framework for Connected and Automated Vehicles (CAVs) in mixed traffic bottleneck scenarios. The DIACC strategy consists of three key innovations: 1) A Decentralized Interaction-Adaptive Decision-Making (D-IADM) module that enhances actor's local interaction perception by distinguishing CAV-CAV cooperative interactions from CAV-HDV observational interactions. 2) A Centralized Interaction-Enhanced Critic (C-IEC) that improves critic's global traffic understanding through interaction-aware value estimation, providing more accurate guidance for policy updates. 3) A reward design that employs softmin aggregation with temperature annealing to prioritize interaction-intensive scenarios in mixed traffic. Additionally, a lightweight Proactive Safety-based Action Refinement (PSAR) module applies rule-based corrections to accelerate training convergence. Experimental results demonstrate that DIACC significantly improves traffic efficiency and adaptability compared to rule-based and benchmark MARL models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。