UC Riverside

The thesis presents the design and implementation of connected vehicles using vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) and control system integrating wireless communication and sensor synchronization for the enhanced performance and functionality. Nowadays the repaid development in sensor technology and wireless communication protocols expand new possibilities for developing the intelligent transportation systems capable of improving the road safety and traffic efficiency. The proposed the system uses multi-sensor setup including the camera, LiDAR to gather real-time environmental data around the vehicle and infrastructure. These sensor data are trained by advanced model and algorithm to create a comprehensive perception of vehicle’s surrounding. Meanwhile the key to the system’s effectiveness is the synchronization of sensor data through the wireless communication. The synchronization mechanism ensure temporal alignment and coherence across sensors minimizing latency and improving the accuracy of perception. Overall the developed system offers the relevant recorded dataset from sensors and theoretical basis for model training, localization stuff meanwhile contributing how to control the vehicle ECU accordingly based on sensor data to improve traffic efficiency, and overall driving experience in the era of autonomous and connected vehicles.