MPC trajectory planner for autonomous driving solved by genetic algorithm technique
Focusing on autonomous driving algorithm development, this paper proposes a novel real-time trajectory planner formulated as a Nonlinear Model Predictive Control (NMPC) algorithm. The mathematical formulation of the problem is deeply reported and discussed. The numerical solution of the NMPC problem is the result of a novel genetic algorithm strategy that represents the innovative aspect of the work proposed. The aim of this paper is also to show how genetic algorithm can be a valid approach for motion planning strategies. Numerical results are discussed through simulations that show a reasonable behaviour of the proposed strategy in the presence of moving obstacles as well as in a wide range of road friction conditions. Moreover, a real-time implementation for research purposes is assumed as possible by considering computational time analysis reported.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00423114
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Supplemental Notes:
- © 2021 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Arrigoni, S
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0000-0002-5316-7387
- Braghin, F
- Cheli, F
- Publication Date: 2022-12
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 4118-4143
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Serial:
- Vehicle System Dynamics
- Volume: 60
- Issue Number: 12
- Publisher: Taylor & Francis
- ISSN: 0042-3114
- EISSN: 1744-5159
- Serial URL: https://www.tandfonline.com/toc/nvsd20/current
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Crash avoidance systems; Genetic algorithms; Highway safety; Trajectory control
- Identifier Terms: Model Predictive Control
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01867355
- Record Type: Publication
- Files: TRIS
- Created Date: Dec 15 2022 2:15PM