Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control
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Abstract (arXiv)
Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments. However, the car-following performance of DDPG is usually degraded by unreasonable reward function design, insufficient training, and low sampling efficiency. In order to solve this kind of problem, a hybrid car-following strategy based on DDPG and cooperative adaptive cruise control (CACC) is proposed. First, the car-following process is modeled as the Markov decision process to calculate CACC and DDPG simultaneously at each frame. Given a current state, two actions are obtained from CACC and DDPG, respectively. Then, an optimal action, corresponding to the one offering a larger reward, is chosen as the output of the hybrid strategy. Meanwhile, a rule is designed to ensure that the change rate of acceleration is smaller than the desired value. Therefore, the proposed strategy not only guarantees the basic performance of car-following through CACC but also makes full use of the advantages of exploration on complex environments via DDPG. Finally, simulation results show that the car-following performance of the proposed strategy is improved compared with that of DDPG and CACC.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 8 of its 25 references that are in the map. First posted on arXiv 24 Feb 2021.
Builds on
8 of 25 references.
References · 8
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- 2019A Survey of Deep Learning Applications to Autonomous Vehicle Controlcited by 101
- 2020Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehiclescited by 101
- 2019Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Drivingcited by 56
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- 2019Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Controlcited by 19
- 2019Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learningcited by 10
- 2018A Review on Cooperative Adaptive Cruise Control (CACC) Systems: Architectures, Controls, and Applicationscited by 4
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Citing papers per year: 2022: 3; 2023: 1; 2024: 1.
5 papers.
- 2022Deep Reinforcement Learning Aided Platoon Control Relying on V2X Informationcited by 5
- 2022Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programmingcited by 4
- 2023Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Controlcited by 2
- 2022AVDDPG: Federated reinforcement learning applied to autonomous platoon controlcited by 0
- 2024Edge Delayed Deep Deterministic Policy Gradient: efficient continuous control for edge scenarioscited by 0
- 2024Edge Delayed Deep Deterministic Policy Gradient: efficient continuous control for edge scenarios9 Dec 2024
- 2023Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control19 Nov 2023
- 2022AVDDPG: Federated reinforcement learning applied to autonomous platoon control5 Jul 2022
- 2022Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming15 Jun 2022
- 2022Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information28 Mar 2022