Cooperative Adaptive Cruise Control using Vehicle-to-Vehicle communication and Deep Learning

In this paper, a cooperative adaptive cruise control (CACC) system is presented with integrated lidar and vehicle-to-vehicle (V2V) communication. Firstly, an adaptive cruise control system (ACC) is designed for the Q-Car electrical vehicle, an autonomous car. Secondly, a CACC system and V2V communication are designed based on a new algorithm to improve the ACC system performance. Lastly, the CACC agent was trained by Deep Q learning (DQN) and tested. The proposed CACC system improved the stability of the vehicle. Experimental results demonstrate that the CACC system can decrease the average inter-vehicular distance of ACC by 44.74%, with an additional 40.19% when DQN was utilized. The vehicles communicate with each other through a WiFi module to transmit information with 1ms latency.