Autonomous Drifting Based on Maximal Safety Probability Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909305953845248 |
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| author | Hoshino, Hikaru Li, Jiaxing Menon, Arnav Dolan, John M. Nakahira, Yorie |
| author_facet | Hoshino, Hikaru Li, Jiaxing Menon, Arnav Dolan, John M. Nakahira, Yorie |
| contents | This paper proposes a novel learning-based framework for autonomous driving based on the concept of maximal safety probability. Efficient learning requires rewards that are informative of desirable/undesirable states, but such rewards are challenging to design manually due to the difficulty of differentiating better states among many safe states. On the other hand, learning policies that maximize safety probability does not require laborious reward shaping but is numerically challenging because the algorithms must optimize policies based on binary rewards sparse in time. Here, we show that physics-informed reinforcement learning can efficiently learn this form of maximally safe policy. Unlike existing drift control methods, our approach does not require a specific reference trajectory or complex reward shaping, and can learn safe behaviors only from sparse binary rewards. This is enabled by the use of the physics loss that plays an analogous role to reward shaping. The effectiveness of the proposed approach is demonstrated through lane keeping in a normal cornering scenario and safe drifting in a high-speed racing scenario. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03160 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Autonomous Drifting Based on Maximal Safety Probability Learning Hoshino, Hikaru Li, Jiaxing Menon, Arnav Dolan, John M. Nakahira, Yorie Robotics Systems and Control This paper proposes a novel learning-based framework for autonomous driving based on the concept of maximal safety probability. Efficient learning requires rewards that are informative of desirable/undesirable states, but such rewards are challenging to design manually due to the difficulty of differentiating better states among many safe states. On the other hand, learning policies that maximize safety probability does not require laborious reward shaping but is numerically challenging because the algorithms must optimize policies based on binary rewards sparse in time. Here, we show that physics-informed reinforcement learning can efficiently learn this form of maximally safe policy. Unlike existing drift control methods, our approach does not require a specific reference trajectory or complex reward shaping, and can learn safe behaviors only from sparse binary rewards. This is enabled by the use of the physics loss that plays an analogous role to reward shaping. The effectiveness of the proposed approach is demonstrated through lane keeping in a normal cornering scenario and safe drifting in a high-speed racing scenario. |
| title | Autonomous Drifting Based on Maximal Safety Probability Learning |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2409.03160 |