A proposed reinforcement learning approach via discrete control reformulation and multi-step double DQN for adaptive cruise control in electric vehicles
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new AI approach for adaptive cruise control (ACC) in electric vehicles (EVs), improving training speed and reducing headway errors for safer driving.
Area Of Science
- Artificial Intelligence
- Automotive Engineering
- Control Systems
Background
- Adaptive Cruise Control (ACC) is vital for electric vehicle (EV) safety and efficiency.
- Traditional ACC struggles with complex driving environments.
- AI, specifically reinforcement learning (RL), offers solutions but faces challenges in stability, convergence, and robustness.
Purpose Of The Study
- To develop a more stable and efficient RL-based ACC system for EVs.
- To address limitations of current RL methods in real-world ACC applications.
- To enable smoother, human-like driving behavior in autonomous systems.
Main Methods
- Reformulated ACC control using simplified action abstraction (unified throttle/brake).
- Implemented a Double Deep Q-Network with multi-step returns (Double-MS DQN).
- Utilized a stochastic scenario generator for robust training and evaluation in the CARLA simulator.
Main Results
- Achieved significantly faster convergence, reducing training episodes by up to 73%.
- Reduced headway errors by over 40% compared to standard DQN, Dueling DQN, and Double DQN.
- Demonstrated enhanced policy stability and robustness in diverse driving scenarios.
Conclusions
- The proposed Double-MS DQN offers a high-performance, stable, and scalable RL solution for ACC in EVs.
- Simplified action formulation enables effective learning with lightweight algorithms.
- The approach leads to safer and smoother vehicle control in practical applications.
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