Optimization-based motion primitive automata for autonomous driving

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...

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Bibliographic Details
Published in:arXiv.org 2024-01
Main Authors: Pedrosa, Matheus V A, Scheffe, Patrick, Alrifaee, Bassam, Flaßkamp, Kathrin
Format: Article
Language:English
Subjects:
Autonomous cars
Multiple criterion
Multiple objective analysis
Pareto optimization
Trajectory optimization
Trajectory planning
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2401.14276