Safe motion planner for autonomous driving based on LPV MPC and reachability analysis
This article presents an innovative optimization-based solution to the collision avoidance challenge for autonomous vehicles. The presented approach consists in an online motion planner designed to define feasible and efficient paths able to deal with dynamic surroundings while implicitly ensure saf...
| Autores: | , , |
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| Formato: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/362171 |
| Acesso em linha: | http://hdl.handle.net/10261/362171 https://api.elsevier.com/content/abstract/scopus_id/85189669737 |
| Access Level: | acceso abierto |
| Palavra-chave: | Autonomous vehicles Constrained zonotopes Coordination LPV Motion planning Robust planner Safety Tubes Zonotopes |
| Resumo: | This article presents an innovative optimization-based solution to the collision avoidance challenge for autonomous vehicles. The presented approach consists in an online motion planner designed to define feasible and efficient paths able to deal with dynamic surroundings while implicitly ensure safety in the proposed maneuvers. The fact of considering moving obstacles inside the motion planner increases the complexity of the problem while forces it to be executed more frequently as others. To reduce this computational complexity, the approach presented counts with a two stages translation of the commonly used non-linear optimization-based structure into a QP formulation which can be easily solved. The first stage is based on the use of LPV matrices in the dynamic constraints of the vehicle. The second stage consists in performing a reachability analysis based on set propagation to obtain linear expressions of the permitted inputs and reachable states which guarantee safety conditions. |
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