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The connectivity aspect of connected autonomous vehicles (CAV) is beneficialbecause it facilitates dissemination of traffic-related information to vehiclesthrough Vehicle-to-External (V2X) communication. Onboard sensing equipmentincluding LiDAR and camera can reasonably characterize the traffic environmentin the immediate locality of the CAV. However, their performance is limited bytheir sensor range (SR). On the other hand, longer-range information is helpfulfor characterizing imminent conditions downstream. By contemporaneouslycoalescing the short- and long-range information, the CAV can constructcomprehensively its surrounding environment and thereby facilitate informed,safe, and effective movement planning in the short-term (local decisionsincluding lane change) and long-term (route choice). In this paper, we describea Deep Reinforcement Learning based approach that integrates the data collectedthrough sensing and connectivity capabilities from other vehicles located inthe proximity of the CAV and from those located further downstream, and we usethe fused data to guide lane changing, a specific context of CAV operations. Inaddition, recognizing the importance of the connectivity range (CR) to theperformance of not only the algorithm but also of the vehicle in the actualdriving environment, the paper carried out a case study. The case studydemonstrates the application of the proposed algorithm and duly identifies theappropriate CR for each level of prevailing traffic density. It is expectedthat implementation of the algorithm in CAVs can enhance the safety andmobility associated with CAV driving operations. From a general perspective,its implementation can provide guidance to connectivity equipment manufacturersand CAV operators, regarding the default CR settings for CAVs or therecommended CR setting in a given traffic environment.

Facilitating Connected Autonomous Vehicle Operations Using Space-weighted Information Fusion and Deep Reinforcement Learning Based Control