The connectivity aspect of connected autonomous vehicles (CAV) is beneficial because it facilitates dissemination of traffic-related information to vehicles through Vehicle-to-External (V2X) communication. Onboard sensing equipment including LiDAR and camera can reasonably characterize the traffic environment in the immediate locality of the CAV. However, their performance is limited by their sensor range (SR). On the other hand, longer-range information is helpful for characterizing imminent conditions downstream. By contemporaneously coalescing the short- and long-range information, the CAV can construct comprehensively its surrounding environment and thereby facilitate informed, safe, and effective movement planning in the short-term (local decisions including lane change) and long-term (route choice). Current literature provides useful information on CAV control approaches that use only local information sensed from the proximate traffic environment but relatively little guidance on how to fuse this information with that obtained from downstream sources and from different time stamps, and how to use the fused information to enhance CAV movements. In this paper, we describe a Deep Reinforcement Learning based approach that integrates the data collected through sensing and connectivity capabilities from other vehicles located in the proximity of the CAV and from those located further downstream, and we use the fused data to guide lane changing, a specific context of CAV operations. In addition, recognizing the importance of the connectivity range (CR) to the performance of not only the algorithm but also of the vehicle in the actual driving environment, the study carried out a case study. The case study demonstrates the application of the proposed algorithm and duly identifies the appropriate CR for each level of prevailing traffic density. It is expected that implementation of the algorithm in CAVs can enhance the safety and mobility associated with CAV driving operations. From a general perspective, its implementation can provide guidance to connectivity equipment manufacturers and CAV operators, regarding the default CR settings for CAVs or the recommended CR setting in a given traffic environment.

互联自动驾驶汽车（CAV）的连通性方面是有益的，因为它有助于通过车辆到外部（V2X）通信向车辆传播与交通相关的信息。包括LiDAR和摄像头在内的车载传感设备可以合理地刻画CAV所在地的交通环境。但是，它们的性能受到其传感器范围（SR）的限制。另一方面，更长距离的信息有助于表征下游即将发生的情况。通过同时合并短距离和远程信息，CAV可以全面构建其周围环境，从而在短期（包括车道变更在内的本地决策）和长期（路线选择）方面促进知情，安全和有效的移动计划）。当前的文献提供了有关CAV控制方法的有用信息，这些方法仅使用从临近交通环境中感测到的本地信息，但是关于如何将这些信息与从下游来源和不同时间戳获得的信息进行融合以及如何使用融合信息来进行指导的信息很少。增强CAV的运动。在本文中，我们描述了一种基于深度强化学习的方法，该方法将通过感应和连接功能从位于CAV附近的其他车辆以及从更下游的车辆收集的数据进行集成，并且我们使用融合后的数据来指导车道变更， CAV操作的特定上下文。此外，认识到连通性范围（CR）不仅对于算法的有效性，而且对于实际驾驶环境中车辆的性能，都进行了案例研究。案例研究演示了所提出算法的应用，并针对当前流行的交通密度的每个级别适当地确定了适当的CR。期望在CAV中实施该算法可以增强与CAV驾驶操作相关的安全性和机动性。从一般的角度来看，其实现可以为连接设备制造商和CAV运营商提供有关CAV的默认CR设置或给定交通环境中建议的CR设置的指南。案例研究演示了所提出算法的应用，并针对当前流行的交通密度的每个级别适当地确定了适当的CR。期望在CAV中实施该算法可以增强与CAV驾驶操作相关的安全性和机动性。从一般的角度来看，其实现可以为连接设备制造商和CAV运营商提供有关CAV的默认CR设置或给定交通环境中建议的CR设置的指南。案例研究演示了所提出算法的应用，并针对当前流行的交通密度的每个级别适当地确定了适当的CR。期望在CAV中实施该算法可以增强与CAV驾驶操作相关的安全性和机动性。从一般的角度来看，其实现可以为连接设备制造商和CAV运营商提供有关CAV的默认CR设置或给定交通环境中建议的CR设置的指南。