Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
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| _version_ | 1866913235334070272 |
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| author | Wang, Yubin Li, Yulin Peng, Zengqi Ghazzai, Hakim Ma, Jun |
| author_facet | Wang, Yubin Li, Yulin Peng, Zengqi Ghazzai, Hakim Ma, Jun |
| contents | Lane change in dense traffic typically requires the recognition of an appropriate opportunity for maneuvers, which remains a challenging problem in self-driving. In this work, we propose a chance-aware lane-change strategy with high-level model predictive control (MPC) through curriculum reinforcement learning (CRL). In our proposed framework, full-state references and regulatory factors concerning the relative importance of each cost term in the embodied MPC are generated by a neural policy. Furthermore, effective curricula are designed and integrated into an episodic reinforcement learning (RL) framework with policy transfer and enhancement, to improve the convergence speed and ensure a high-quality policy. The proposed framework is deployed and evaluated in numerical simulations of dense and dynamic traffic. It is noteworthy that, given a narrow chance, the proposed approach generates high-quality lane-change maneuvers such that the vehicle merges into the traffic flow with a high success rate of 96%. Finally, our framework is validated in the high-fidelity simulator under dense traffic, demonstrating satisfactory practicality and generalizability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_03723 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning Wang, Yubin Li, Yulin Peng, Zengqi Ghazzai, Hakim Ma, Jun Robotics Lane change in dense traffic typically requires the recognition of an appropriate opportunity for maneuvers, which remains a challenging problem in self-driving. In this work, we propose a chance-aware lane-change strategy with high-level model predictive control (MPC) through curriculum reinforcement learning (CRL). In our proposed framework, full-state references and regulatory factors concerning the relative importance of each cost term in the embodied MPC are generated by a neural policy. Furthermore, effective curricula are designed and integrated into an episodic reinforcement learning (RL) framework with policy transfer and enhancement, to improve the convergence speed and ensure a high-quality policy. The proposed framework is deployed and evaluated in numerical simulations of dense and dynamic traffic. It is noteworthy that, given a narrow chance, the proposed approach generates high-quality lane-change maneuvers such that the vehicle merges into the traffic flow with a high success rate of 96%. Finally, our framework is validated in the high-fidelity simulator under dense traffic, demonstrating satisfactory practicality and generalizability. |
| title | Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2303.03723 |