Optimization-based motion primitive automata for autonomous driving-Reference-Cited by-同舟云学术

Optimization-based motion primitive automata for autonomous driving

Published:2023-04-01 Issue:4 Volume:71 Page:294-300
ISSN:0178-2312
Container-title:at - Automatisierungstechnik
language:en
Short-container-title:

Author:

Pedrosa Matheus V. A.¹^ORCID,Scheffe Patrick²,Alrifaee Bassam²,Flaßkamp Kathrin¹

Affiliation:

1. Chair of Systems Modeling and Simulation, Systems Engineering , Saarland University , Saarbrücken , Germany

2. Chair for Embedded Software , RWTH Aachen University , Aachen , Germany

Abstract

Abstract 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.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

https://www.degruyter.com/document/doi/10.1515/auto-2022-0158/pdf

Reference12 articles.

1. K. Flaßkamp, “On the optimal control of mechanical systems – hybrid control strategies and hybrid dynamics,” Ph.D. thesis, University of Paderborn, 2013.

2. M. Kloock, P. Scheffe, J. Maczijewski, et al.., “Cyber-Physical Mobility Lab: an open-source platform for networked and autonomous vehicles,” in 2021 European Control Conference (ECC), pp. 1937–1944, 2021.

3. E. Frazzoli, M. A. Dahleh, and E. Feron, “Maneuver-based motion planning for nonlinear systems with symmetries,” IEEE Trans. Robot., vol. 21, no. 6, pp. 1077–1091, 2005. https://doi.org/10.1109/tro.2005.852260.

4. M. Althoff, M. Koschi, and S. Manzinger, “Commonroad: composable benchmarks for motion planning on roads,” in Proc. of the IEEE Intelligent Vehicles Symposium, 2017.

5. P. Scheffe, M. V. A. Pedrosa, K. Flaßkamp, and B. Alrifaee, “Receding horizon control using graph search for multi-agent trajectory planning,” IEEE Trans. Control Syst. Technol., pp. 1–14, 2022, https://doi.org/10.1109/tcst.2022.3214718.