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Motion Planning for Autonomous Vehicles in Partially Observable Environments
Ömer Şahin Taş
2023
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This work develops a motion planner that compensates the deficiencies from perception modules by exploiting the reaction capabilities of a vehicle. The work analyzes present uncertainties and defines driving objectives together with constraints that ensure safety. The resulting problem is solved in real-time, in two distinct ways: first, with nonlinear optimization, and secondly, by framing it as a partially observable Markov decision process and approximating the solution with sampling.
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Keywords
- Automatisiertes Fahren
- Bewegungsplanung
- Building construction & materials
- Civil engineering, surveying & building
- Decision making
- Entscheidungsfindung
- Information Gathering
- Motion Planning
- Planning under Uncertainty
- Planung unter Unsicherheiten
- robotics
- Robotik
- Technology, engineering, agriculture
Links
DOI: 10.5445/KSP/1000158509Editions