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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Published in:arXiv.org 2024-01
Main Authors: Pedrosa, Matheus V A, Scheffe, Patrick, Alrifaee, Bassam, Flaßkamp, Kathrin
Format: Article
Language:English
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Autonomous cars
Multiple criterion
Multiple objective analysis
Pareto optimization
Trajectory optimization
Trajectory planning
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Scheffe, Patrick
Alrifaee, Bassam
Flaßkamp, Kathrin
description 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.
doi_str_mv 10.48550/arxiv.2401.14276
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subjects Autonomous cars
Multiple criterion
Multiple objective analysis
Pareto optimization
Trajectory optimization
Trajectory planning
title Optimization-based motion primitive automata for autonomous driving
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