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This paper presents a novel method for using full duplex (FD) communication across shared channels in cellular vehicle-to-everything (C-V2X) networks. Three modes A, B and C have been defined for communication in V2X networks where Mode C for FD communication has been introduced for the first time. We have derived mathematical model for success probability of FD for C-V2X network and formulated expressions for area spectral efficiency (ASE). Dual connectivity (DC) for simultaneous link of receiving vehicle with nearest transmitting vehicle and base station (BS) has also been analyzed for the first time for HD and FD in C-V2X network. Analytical and Monte Carlo simulations results have shown that utilization of FD in C-V2X network provides comparable success probability as compared to HD with improvement in ASE. Success probability of FD remains close to HD in terms of signal to interference noise ratio (SINR) in the range from -40 dBW to 60 dBW. Importance of achieving perfect self-interference cancellation (SIC) for different values of self-interference (SI) in FD network has also been evaluated. FD in C-V2X network has shown to significantly improve ASE with gain of 2.55 dB over Direct Short Range Communication (DSRC) and 2 dB over HD in C-V2X network under specific conditions. No degradation in ASE was observed in case of DC for HD and FD. ASE for FD has shown improvement as compared to HD for DSRC and C-V2X networks when evaluated against density of vehicles, BSs and roads.

Connected vehicles can benefit from sharing and merging their observations to develop a more complete understanding of the traffic scene and track traffic participants behind obstructions. Although vehicle-to-vehicle(V2V) communications provide a channel for point cloud data sharing, it is challenging to align point clouds from two vehicles with state-of-the-art techniques due to localization errors, visual obstructions, and differences in perspective. Therefore, we propose a two-phase point cloud registration mechanism to fuse point clouds which focuses on key objects in the scene where the point clouds are most similar and infer the transformation from those. Our system first identifies co-visible objects between vehicle views based on hyper-graph matching using multiple similarity metrics, and then refines the overlap region between co-visible objects across the views for point cloud registration. The system is evaluated based on both experimental and simulation data, which shows tremendous performance improvement when combing with state-of-art baselines.

The technical exploration of safe automated driving has evolved from single vehicle intelligence to vehicular networking. Therefore, to a certain extent, the safety requirements have been translated into the requirements for network performance. Although Vehicle-to-Everything (V2X) communication technologies have developed decades of years, from the Dedicated Short-Range Communication (DSRC) to the Long Term Evolution V2X (LTE-V2X), the introduction of millimeter wave (mmWave) in the 5G era will achieve an unprecedented leap in V2X performance by empowering multi-gigabit data transmission with ultra-low latency. Meanwhile, the emergence of Software Defined Networking (SDN) enables centralized network management and allows flexible scheduling of communication resources to match V2X capacities. Their benefits for vehicular networks have been revealed by plenty of studies respectively. Nevertheless, no researchers investigate their combination to improve the safety of automated driving, especially lack of scalable prototype systems for proof-of-concepts (PoCs), not to mention the field test with real traffic. In this paper, we firstly propose a novel architecture, i.e. software-defined dynamic mmWave V2X network, to support cooperative perception. And then, necessary hardware and software platforms are introduced to compose prototype systems. Step by step, we conduct PoCs from the indoors to the outdoors, which demonstrate the network functions, the prototype scalability and most importantly, the significant enhancement to the vehicle perception for safe automated driving.