Abstract
The advances in distributed intervehicle communication networks have stimulated a fruitful line of research in cooperative Connected Autonomous Vehicles (CAVs). When grouped into platoons, the individual CAVs must automatically adjust their speeds using the feedback of on-board sensors and communication with the neighboring vehicles to maintain strong stability. The gap between the communication network and vehicular traffic research are one of the most significant hurdles towards the realization of this CAV technology. This research in the dissertation is to develop a Joint Network-Traffic vehicle-to-vehicle (V2V) approach to improve the safety and stability of CAVs by optimizing the information flow. Our studies on 3rd Generation Partnership Project releases and simulation of 5G New Radio (NR) framework show that the channel loss at this high frequency is reduced by using flexible numerology, advanced physical layer techniques and orthogonal resources in 5G NR. In addition, our studies show that the channel quality is sensitive to the mobility of neighboring vehicles and road topology. To improve the platoon stability, we proposed a new approach that considers vehicle dynamics including non-linear acceleration into the mmWave communication system design for improving both the channel capacity and safety for CAVs. Both the simulations and experiments using the hardware demonstrate that the proposed approaches can improve beamforming and channel performance in vehicular mobility environments. Specifically, the proposed adaptive platooning scheme overcomes the routing assumption by merging vehicles for different sources and destinations through mobile communication. Further, we investigated a sidelink based network model for resource sharing which is suitable for close-range platooning applications. The outcome of the research in this dissertation can potentially foster many cooperative CAV applications by allowing communications among vehicles.