Abstract
Cooperative adaptive cruise control (CACC) has important significance for the development of the connected and automated vehicle (CAV) industry. The traditional proportional integral derivative (PID) platoon controller adjustment is not only time-consuming and laborious, but also unable to adapt to different working conditions. This paper proposes a learning control method for a vehicle platooning system using a deep deterministic policy gradient (DDPG)-based PID. The main contribution of this study is automating the PID weight tuning process by formulating this objective as a deep reinforcement learning (DRL) problem. The longitudinal control of the vehicle platooning is divided into upper and lower control structures. The upper-level controller based on the DDPG algorithm can adjust the current PID controller parameters. Through offline training and learning in a SUMO simulation software environment, the PID controller can adapt to different road and vehicular platooning acceleration and deceleration conditions. The lower-level controller controls the gas/brake pedal to accurately track the desired acceleration and speed. Based on the hardware-in-the-loop (HIL) simulation platform, the results show that in terms of the maximum speed error, for the DDPG-based PID controller this is 0.02–0.08 m/s less than for the conventional PID controller, with a maximum reduction of 5.48%. In addition, the maximum distance error of the DDPG-based PID controller is 0.77 m, which is 14.44% less than that of the conventional PID controller.
Highlights
Connected and automated vehicles (CAVs) are an important development direction for the automobile industry
Scenario1 1isisthe theexperimental experimentalcondition conditionofofthe thevehicle vehicleplatoon platoonaccelerating acceleratinguphill uphilland and Scenario is the experimental condition of the vehicle platoon decelerating downhill
We have proposed a deep deterministic policy gradient (DDPG)-based proportional integral derivative (PID) learning control method, which uses a DDPG algorithm to automatically tune the PID weights for a vehicle platooning system
Summary
Connected and automated vehicles (CAVs) are an important development direction for the automobile industry. They are an important way to solve the problems of traffic safety, resource consumption, environmental pollution, etc., but are the core element of establishing an intelligent transportation system. Through vehicle-to-everything (V2X) communication, this mode can receive the dynamic information of the surrounding environment in real-time and improve driving safety [3,4]. By sharing information among vehicles, a CACC system allows automated vehicles to form platoons and be driven at harmonized speed with smaller constant time gaps between vehicles [8]. CACC plays a positive role in improving the performance of the vehicular platooning system and ensuring the safety of vehicles, so it has attracted wide attention from researchers.
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