Communication networks for connected vehicle: simulating, validating, and testing techniques

Autonomous driving represents one of the most significant challenges in the auto- motive industry as it promises to revolutionize how people travel and interact with vehicles. The 5G technology offers advanced connectivity that can support and enhance autonomous driving systems, enabling fast and reliable communication between vehicles, road infrastructures, and external devices. 5G and C-V2X are Key harnessing pillar’s of AI capabilities with cloud computing in autonomous and connected driving. The V2X in it’s all version (V2V, V2I, V2N..etc) could be seen as the third eye of the vehicle, which give access to the information of other vehicles and their surroundings that are not possible otherwise. This thesis work is part of an EU project called AI@EDGE, from which one of the test cases is being considered for developing this thesis work. The report first discusses the key technological enabler’s of AI@EDGE platform based on AI exploitation for various connected vehicle services and functions. In order to assess its functionality in a virtual setting, a traffic simulator called SUMO is being utilized, specifically in the construction of a simulated scenario. The simulation data is subscribed and communicated to the edge server through the utilization of SUMO’s TraCI library. To facilitate the transmission and reception of CAM/DENM messages, an AMQP client has been integrated into the TraCI-based program. In order to deploy the program, along with the AMQP client, into a TBM (on-board device), communication-specific testing is conducted on a 5G emulation bench. Various test cases pertaining to cellular networks LTE/5G, such as cell handover, dual-connectivity, carrier aggregation, and MIMO, are performed in a network-based emulation HIL environment. Additionally, network throughput testing is carried out on the Device Under Test, which is equipped with a C-V2X wave stack. Ultimately, the feasibility of a virtual validation platform for vehicular perception is assessed based on the developed capabilities and results obtained from the HIL testing.