  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). 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 paper 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.
