Cooperative adaptive cruise control (CACC) has important significance for the development of the connected and automated vehicle (CAV) industry. In this paper, a learning control method combined Deep Deterministic Policy Gradient and Proportional-Integral-Derivative (DDPG-PID) controller is proposed. The main contribution of this study is automating the PID weight tuning process by formulating this objective as a deep reinforcement learning (DRL) problem. Based on the Hardware-in-the-Loop (HIL) simulation platform, the DDPG-PID controller is compared with the conventional PID controller under the test condition. Experiment results indicate that on 38.95% stability time in vehicular platooning system is decreased by utilizing the proposed method. The performance of maximum distance error is also improved efficiently, which is reduced by 60.94%. The research in this paper is a further development of learning control method and provides a new idea for the practical application of DRL algorithm in industrial field.
Longitudinal Tracking Control of Vehicle Platooning Using DDPG-based PID
2020-12-18
1203923 byte
Conference paper
Electronic Resource
English
Vehicle speed tracking in bench test based on DDPG
IEEE | 2022
|Longitudinal Control for Truck Platooning
IEEE | 2020
|VEHICLE PLATOONING CONTROL SYSTEM, ON-VEHICLE UNIT AND VEHICLE PLATOONING CONTROL METHOD
European Patent Office | 2023
|