Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, 2023-May, pp. 5631-5637
- Issue Date:
- 2023-07-04
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Self-Improving_Safety_Performance_of_Reinforcement_Learning_Based_Driving_with_Black-Box_Verification_Algorithms.pdf | Published version | 4.56 MB |
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In this work we propose a self improving artificial intelligence system to enhance the safety performance of reinforcement learning RL based autonomous driving AD agents using black box verification methods RL algorithms have become popular in AD applications in recent years However the performance of existing RL algorithms heavily depends on the diversity of training scenarios A lack of safety critical scenarios during the training phase could result in poor generalization performance in real world driving applications We propose a novel framework in which the weaknesses of the training set are explored through black box verification methods After discovering AD failure scenarios the RL agent s training is re initiated via transfer learning to improve the performance of previously unsafe scenarios Simulation results demonstrate that our approach efficiently discovers safety failures of action decisions in RL based adaptive cruise control ACC applications and significantly reduces the number of vehicle collisions through iterative applications of our method The source code is publicly available at https github com data and decision lab self improving RL
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