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A DDPG-based Path Following Control Strategy for Autonomous Vehicles by Integrated Imitation Learning and Feedforward Exploration

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The proposed method enhances path tracking performance of autonomous vehicles through integrated learning techniques.
Results indicate that the designed DDPG-based strategy improves learning efficiency and generalization in path following.
Incorporating imitation learning and feedforward exploration optimizes decision-making during the path tracking process.
The approach utilizes a hierarchical reward system to refine the actor-critic network for better control outcomes.

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This method integrates imitation learning and feedforward exploration for improved path tracking performance in autonomous vehicles.

Liu et al. (2025) studied this question.

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