Paper ID: 2401.14276

Optimization-based motion primitive automata for autonomous driving

Matheus V. A. Pedrosa, Patrick Scheffe, Bassam Alrifaee, Kathrin Flaßkamp

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.

Submitted: Jan 25, 2024