As the commercialisation of Intelligent Connected Vehicles (ICVs) accelerates, Vehicle-to-Everything (V2X)-based general testing and assessment systems have emerged at the forefront of the research. Current field testing schemes mostly follow the norms of traditional vehicle tests. In contrast, Original Equipment Manufacturers (OEMs) have increasingly focused on the potential influence of V2X communication performance on the application response characteristics. Our previous work resulted in a C-V2X (Cellular-V2X) large-scale testing system (LSTS) for communication performance testing. However, when addressing the need to combine application and communication, the system software faces confronts heightened technical challenges. This paper proposes a layered software architecture for the automated C-V2X LSTS, which is tailored to combined scenarios. This architecture integrates scenario encapsulation technology with a large-scale node array deployment strategy, enabling communication and application testing under diversified scenarios. The experimental results demonstrate the scalability of the system, and a case study of Forward Collision Warning (FCW) validates the effectiveness and reliability of the system.

Multi-access Edge Computing (MEC), which could provide real-time computing ability, is considered as an effective approach to improve performance of Vehicular Ad Hoc Network (VANET). MEC could process regional vehicles information and generate real-time road hazard features, which could be used to realize trajectory planning progress of vehicles. In this paper, an MEC-oriented VANET infrastructure is presented, and a road hazard feature-based trajectory planning method is proposed. Back Propagation (BP) neural network is employed to predict road hazard feature changing, while a hazard-based cost function is defined. Then, an improved Rapidly Exploring Random Tree (RRT) algorithm is proposed for novel regional trajectory planning. A joint simulation is done based on SUMO and NS3 platforms. Simulation results verify the effectiveness and stability of the proposed algorithm.