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An Adaptive Cruise Control Algorithm Based on DDPG Algorithm Based on Deep Reinforcement Learning Under Variable Acceleration Conditions
- 1 College of Automotive Sciences, Tongji University, Shanghai, China
* Author to whom correspondence should be addressed.
Abstract
Adaptive cruise control (ACC) dynamically regulates a vehicle's speed to preserve a secure gap from the preceding vehicle, enhancing road safety. In this study, ACC is examined through the lens of deep reinforcement learning, with a focus on the Deep Deterministic Policy Gradient (DDPG) technique. The reward function takes into account the speed error, and two modes—speed control and distance control—are implemented. The proposed ACC strategy is trained and validated through simulations on the MATLAB/Simulink platform. The experimental results indicate that the reward function converges rapidly, confirming the suitability of the DDPG algorithm for automotive ACC research.
Keywords
Reinforcement Learning, DDPG, Adaptive Cruise, Intelligent Vehicle.
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Cite this article
Lan,Y. (2024). An Adaptive Cruise Control Algorithm Based on DDPG Algorithm Based on Deep Reinforcement Learning Under Variable Acceleration Conditions. Applied and Computational Engineering,106,37-43.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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