Efficient traffic safety management necessitates real-time crash risk prediction using expressway characteristics. With the emergence of autonomous vehicles (AVs), the development and evaluation of variable speed limit (VSL) strategies, a key active traffic management technique, become crucial for enhancing safety and mobility in mixed traffic flows. This underscores the need for optimized VSL strategies to accommodate both conventional and AVs. This paper presents a study on the development of VSL control algorithms using deep reinforcement learning in a microscopic traffic simulation. As the rewards function, time-to-collision and speed were considered. To enhance traffic safety, VSL strategies were refined across various market penetration of connected AVs. Analysis revealed that safety and traffic density are improved by 53% and 59%, respectively, in market penetration rate (MPR) 50, marking significant safety improvements in congested and low MPR scenarios. These findings present the importance of developing and evaluating VSL strategies for mixed traffic flow, particularly in the context of increasing the prevalence of connected and AVs.

中文翻译：

---

自动驾驶环境下基于深度 Q 网络学习的主动速度管理


高效的交通安全管理需要利用高速公路特性进行实时碰撞风险预测。随着自动驾驶汽车 （AV） 的出现，可变限速 （VSL） 策略的开发和评估（一种关键的主动交通管理技术）对于提高混合交通流中的安全性和流动性变得至关重要。这凸显了优化 VSL 策略以适应传统和 AV 的需求。本文介绍了在微观交通仿真中使用深度强化学习开发 VSL 控制算法的研究。作为奖励功能，考虑了碰撞时间和速度。为了提高交通安全，VSL 策略针对互联 AV 的各种市场渗透率进行了改进。分析显示，市场渗透率 （MPR） 50 的安全性和交通密度分别提高了 53% 和 59%，标志着在拥堵和低 MPR 情景下的安全有了显着改善。这些发现表明了为混合交通流开发和评估 VSL 策略的重要性，尤其是在互联和 AV 普及率增加的情况下。