Title
Cooperative Adaptive Cruise Control using Vehicle-to-Vehicle communication and Deep Learning
Document Type
Conference Proceeding
Publication Date
1-1-2022
Publication Title
IEEE Intelligent Vehicles Symposium, Proceedings
Volume
2022-June
First Page
435
Last Page
440
Abstract
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.
DOI
10.1109/IV51971.2022.9827148
ISBN
9781665488211
Recommended Citation
Ke, Haoyang; Mozaffari, Saeed; Alirezaee, Shahpour; and Saif, Mehrdad. (2022). Cooperative Adaptive Cruise Control using Vehicle-to-Vehicle communication and Deep Learning. IEEE Intelligent Vehicles Symposium, Proceedings, 2022-June, 435-440.
https://scholar.uwindsor.ca/electricalengpub/221