用深度强化学习控制自动驾驶车，能提升道路容量和燃油效率。
Macroscopic Characteristics of Mixed Traffic Flow with Deep Reinforcement Learning Based Automated and Human-Driven Vehicles
- 用TD3算法训练自动驾驶车，在NGSIM数据上模拟真实交互。
- 自动驾驶车比例越高，道路容量提升约7.52%，高速时燃油效率提高28.98%。
- 兼顾安全与效率，适合交通流建模与智能网联汽车研究者。
在自动驾驶车与人类驾驶车辆共存的混合交通中，如何平衡安全、效率、舒适性、燃油经济性及交通规则遵守，同时捕捉驾驶员行为差异，是重大挑战。传统跟车模型如智能驾驶员模型（IDM）难以泛化且忽略燃油效率，促使采用基于学习的方法。尽管深度强化学习（DRL）在微观跟车表现优异，其宏观交通流特性仍不明确。本研究聚焦于分析基于DRL的自动驾驶车在混合交通中的宏观交通流特性和燃油效率。采用双延迟深度确定性策略梯度（TD3）算法，并利用NGSIM高速公路数据集训练自动驾驶车，实现与人类驾驶车辆的真实交互。通过基本图（FD）评估不同驾驶员异质性、异质时间间隔渗透率及不同强化学习控制车辆占比下的交通性能。还对比了基于RL的自动驾驶车与IDM在宏观层面的燃油效率。结果表明，交通性能对安全时间间隔分布和强化学习车辆比例敏感。从全人类驾驶过渡到全强化学习控制，道路容量可提升约7.52%。在高速（>50 km/h）下，燃油效率平均提高28.98%；低速（<50 km/h）下提升1.86%。总体而言，该DRL框架在不牺牲安全的前提下提升了交通容量与燃油效率。
原文摘要 · Abstract (English)
Automated Vehicle (AV) control in mixed traffic, where AVs coexist with human-driven vehicles, poses significant challenges in balancing safety, efficiency, comfort, fuel efficiency, and compliance with traffic rules while capturing heterogeneous driver behavior. Traditional car-following models, such as the Intelligent Driver Model (IDM), often struggle to generalize across diverse traffic scenarios and typically do not account for fuel efficiency, motivating the use of learning-based approaches. Although Deep Reinforcement Learning (DRL) has shown strong microscopic performance in car-following conditions, its macroscopic traffic flow characteristics remain underexplored. This study focuses on analyzing the macroscopic traffic flow characteristics and fuel efficiency of DRL-based models in mixed traffic. A Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is implemented for AVs' control and trained using the NGSIM highway dataset, enabling realistic interaction with human-driven vehicles. Traffic performance is evaluated using the Fundamental Diagram (FD) under varying driver heterogeneity, heterogeneous time-gap penetration levels, and different shares of RL-controlled vehicles. A macroscopic level comparison of fuel efficiency between the RL-based AV model and the IDM is also conducted. Results show that traffic performance is sensitive to the distribution of safe time gaps and the proportion of RL vehicles. Transitioning from fully human-driven to fully RL-controlled traffic can increase road capacity by approximately 7.52%. Further, RL-based AVs also improve average fuel efficiency by about 28.98% at higher speeds (above 50 km/h), and by 1.86% at lower speeds (below 50 km/h) compared to the IDM. Overall, the DRL framework enhances traffic capacity and fuel efficiency without compromising safety.
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