用强化学习优化车辆间距控制，提升高速公路拥堵缓解效果。
Highway Congestion Reduction through Reinforcement Learning Based Eulerian Headway Control
- 通过强化学习动态调整车距，而非速度，实现快速响应与高合规性。
- 在多车道模拟中，交通流量最高提升10.6%，优于人工驾驶和传统可变限速。
- 设计更贴近实际的评估指标，适合关注智能交通系统落地的研究者。
配备自适应巡航控制（ACC）的联网自动驾驶汽车为缓解高速公路拥堵提供了新可能。传统欧拉型可变限速（VSL）依赖路侧标志调节，更新频率低且驾驶员遵守度有限。近期研究探索了拉格朗日策略，直接控制单车，虽反应快、合规性高，但在多车道场景中依赖驾驶员隐含变道意图，难以保证稳定决策。为此，我们提出一种基于强化学习优化的欧拉型控制系统，（i）利用ACC实现快速响应与高合规性，（ii）通过调节瓶颈区平均密度而非依赖驾驶意图，关键在于下达车距指令而非速度指令。我们在大规模仿真中评估两种变体：时间车距控制与距离车距控制，在多种交通条件下均优于基线方法，相较人类驾驶交通流提升最高达10.6%，较传统VSL提升6.7%。为增强评估可靠性，我们提出一种新型边界感知速度指标，解决动态车辆进出场景下仿真研究中的公认缺陷。实证结果结合可部署系统设计，为实现安全、可扩展的高速公路拥堵缓解提供了可行路径。
原文摘要 · Abstract (English)
Connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC) create new opportunities for highway congestion mitigation. Traditional practice relies on Eulerian variable speed limits (VSL) which regulate traffic through roadside signs, but suffer from infrequent updates and limited driver compliance. Recent research explored Lagrangian strategies that directly control individual vehicles, offering high reactivity and compliance, yet in realistic multi-lane settings they depend on drivers' latent lane-change intentions, making robust vehicle-level decisions difficult. Hence, we propose an Eulerian control system optimized through reinforcement learning, that (i) leverages ACC for reactivity and compliance, and (ii) obviates dependence on latent driver intentions by regulating aggregate density near bottlenecks, crucially via headway commands rather than speed commands. We evaluate two variants of our system, time-headway and distance-headway control, in large-scale simulations across a range of traffic conditions. Both variants outperform baselines, improving traffic flow by up to 10.6% over human traffic and 6.7% over traditional VSL. To strengthen evaluation, we propose a novel boundary-aware speed metric addressing a recognized flaw in simulation studies with dynamic vehicle entry and exit. The empirical results, together with our emphasis on deployable system design, suggest a path towards practical, safe, and scalable highway congestion mitigation.
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