Vehicle Lateral Control Based on Augmented Lagrangian DDPG Algorithm
This paper studies the safe trajectory tracking control of intelligent vehicles, which is still an open and challenging problem. A deep reinforcement learning algorithm based on augmented Lagrangian safety constraints is proposed to the lateral control of vehicle trajectory tracking. First, the trac...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/10/5463 |
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| Summary: | This paper studies the safe trajectory tracking control of intelligent vehicles, which is still an open and challenging problem. A deep reinforcement learning algorithm based on augmented Lagrangian safety constraints is proposed to the lateral control of vehicle trajectory tracking. First, the tracking control of intelligent vehicles is described as a reinforcement learning process based on the Constrained Markov Decision Process (CMDP). The actor-critic neural network based reinforcement learning framework is established and the environment of reinforcement learning is designed to include the vehicle model, tracking model, road model and reward function. Secondly, the augmented Lagrangian Deep Deterministic Policy Gradient (DDPG) method is proposed for updating, in which a replay separation buffer method is used to solve the problem of sample correlation, and a neural network with the same structure is copied to solve the update divergence problem. Finally, a vehicle lateral control approach is obtained, whose effectiveness and advantages over existing results are verified through simulation results. |
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| ISSN: | 2076-3417 |