Fu, Tingzhong ; Nguyen, Hoang Hai ; Findeisen, Rolf (2024)
Guaranteed Collision Avoidance for Autonomous Vehicles Fusing Model Predictive Control and Data Driven Reachability Analysis.
22nd European Control Conference. Stockholm, Sweden (25.06.2024 - 28.06.2024)
doi: 10.23919/ECC64448.2024.10590821
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Ensuring collision avoidance is a critical challenge for autonomous vehicles, particularly when faced with uncertain moving obstacles. This work presents a robust collision avoidance framework, integrating data-driven reachability analysis with Model Predictive Control (MPC). The framework is specifically designed to address scenarios where detailed information about the moving obstacles that should be avoided is unavailable. A data-driven approach is employed, which utilizes uncertain measurements corrupted by bounded noise of the obstacle. Based on the measurements, an over-approximation of the reachable sets by moving obstacles represented as zonotopes is constructed. To guarantee security, a safety margin is added to the approximation. The resulting set is employed as a polytopic collision avoidance constraint within the robust MPC scheme, enabling effective control of the autonomous vehicle while guaranteeing avoidance of impacts. The effectiveness of the data-driven collision avoidance scheme is demonstrated through extensive simulations. The presented results outline a promising advancement in collision avoidance for autonomous vehicles operating in uncertain environments.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Fu, Tingzhong ; Nguyen, Hoang Hai ; Findeisen, Rolf |
| Art des Eintrags: | Bibliographie |
| Titel: | Guaranteed Collision Avoidance for Autonomous Vehicles Fusing Model Predictive Control and Data Driven Reachability Analysis |
| Sprache: | Englisch |
| Publikationsjahr: | 24 Juli 2024 |
| Verlag: | IEEE |
| Buchtitel: | 2024 European Control Conference (ECC) |
| Veranstaltungstitel: | 22nd European Control Conference |
| Veranstaltungsort: | Stockholm, Sweden |
| Veranstaltungsdatum: | 25.06.2024 - 28.06.2024 |
| DOI: | 10.23919/ECC64448.2024.10590821 |
| Kurzbeschreibung (Abstract): | Ensuring collision avoidance is a critical challenge for autonomous vehicles, particularly when faced with uncertain moving obstacles. This work presents a robust collision avoidance framework, integrating data-driven reachability analysis with Model Predictive Control (MPC). The framework is specifically designed to address scenarios where detailed information about the moving obstacles that should be avoided is unavailable. A data-driven approach is employed, which utilizes uncertain measurements corrupted by bounded noise of the obstacle. Based on the measurements, an over-approximation of the reachable sets by moving obstacles represented as zonotopes is constructed. To guarantee security, a safety margin is added to the approximation. The resulting set is employed as a polytopic collision avoidance constraint within the robust MPC scheme, enabling effective control of the autonomous vehicle while guaranteeing avoidance of impacts. The effectiveness of the data-driven collision avoidance scheme is demonstrated through extensive simulations. The presented results outline a promising advancement in collision avoidance for autonomous vehicles operating in uncertain environments. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS) |
| Hinterlegungsdatum: | 06 Nov 2024 15:18 |
| Letzte Änderung: | 14 Nov 2024 16:14 |
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