The integration of autonomous vehicles (AVs) into the existing transportation infrastructure offers a promising solution to alleviate congestion and enhance mobility. This research explores a novel approach to traffic optimization by employing a multi-agent rollout approach within a mixed autonomy environment. The study concentrates on coordinating the speed of human-driven vehicles by longitudinally controlling AVs, aiming to dynamically optimize traffic flow and alleviate congestion at highway bottlenecks in real-time. We model the problem as a decentralized partially observable Markov decision process (Dec-POMDP) and propose an improved multi-agent rollout algorithm. By employing agent-by-agent policy iterations, our approach implicitly considers cooperation among multiple agents and seamlessly adapts to complex scenarios where the number of agents dynamically varies. Validated in a real-world network with varying AV penetration rates and traffic flow, the simulations demonstrate that the multi-agent rollout algorithm significantly enhances performance, reducing average travel time on bottleneck segments by 9.42% with a 10% AV penetration rate.

翻译：将自动驾驶车辆（AV）融入现有交通基础设施，为缓解拥堵和提升出行效率提供了有前景的解决方案。本研究探索了一种新颖的交通优化方法，在混合自主环境中采用多智能体Rollout方法。该研究聚焦于通过纵向控制自动驾驶车辆来协调人类驾驶车辆的速度，旨在实时动态优化交通流并缓解高速公路瓶颈处的拥堵。我们将问题建模为分散式部分可观测马尔可夫决策过程（Dec-POMDP），并提出了一种改进的多智能体Rollout算法。通过逐智能体的策略迭代，我们的方法隐式考虑了多智能体间的协作，并能够无缝适应智能体数量动态变化的复杂场景。在真实道路网络中对不同自动驾驶车辆渗透率和交通流进行验证，仿真结果表明，多智能体Rollout算法显著提升了性能：当自动驾驶车辆渗透率为10%时，瓶颈路段的平均通行时间降低了9.42%。