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

被引：2

作者：

Ke, Haoyang ^{[1
]}

Mozaffari, Saeed ^{[2
]}

Alirezaee, Shahpour ^{[3
]}

Saif, Mehrdad ^{[3
]}

机构：

[1] Univ Windsor, Elect & Comp Engn, Windsor, ON N9B 3P4, Canada

[2] Univ Windsor, Mechatron Res Lab, Windsor, ON, Canada

[3] Univ Windsor, Elect & Comp Engn Dept, Windsor, ON, Canada

来源：

2022 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) | 2022年

关键词：

COLLISION-AVOIDANCE; PERFORMANCE; POLICY;

D O I：

10.1109/IV51971.2022.9827148

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：435 / 440

页数：6

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