Optimization-based motion primitive automata for autonomous driving
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929224248459264 |
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| author | Pedrosa, Matheus V. A. Scheffe, Patrick Alrifaee, Bassam Flaßkamp, Kathrin |
| author_facet | Pedrosa, Matheus V. A. Scheffe, Patrick Alrifaee, Bassam Flaßkamp, Kathrin |
| contents | Trajectory planning for autonomous cars can be addressed by primitive-based methods, which encode nonlinear dynamical system behavior into automata. In this paper, we focus on optimal trajectory planning. Since, typically, multiple criteria have to be taken into account, multiobjective optimization problems have to be solved. For the resulting Pareto-optimal motion primitives, we introduce a universal automaton, which can be reduced or reconfigured according to prioritized criteria during planning. We evaluate a corresponding multi-vehicle planning scenario with both simulations and laboratory experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14276 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimization-based motion primitive automata for autonomous driving Pedrosa, Matheus V. A. Scheffe, Patrick Alrifaee, Bassam Flaßkamp, Kathrin Systems and Control Robotics Trajectory planning for autonomous cars can be addressed by primitive-based methods, which encode nonlinear dynamical system behavior into automata. In this paper, we focus on optimal trajectory planning. Since, typically, multiple criteria have to be taken into account, multiobjective optimization problems have to be solved. For the resulting Pareto-optimal motion primitives, we introduce a universal automaton, which can be reduced or reconfigured according to prioritized criteria during planning. We evaluate a corresponding multi-vehicle planning scenario with both simulations and laboratory experiments. |
| title | Optimization-based motion primitive automata for autonomous driving |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2401.14276 |