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
Motion Planning for Autonomous Vehicles in Partially Observable Environments
2023
1 Online-Ressource (222 p.)
Book
Electronic Resource
Unknown
Motion planning for autonomous vehicles in partially observable environments
TIBKAT | 2023
|Motion Planning for Autonomous Vehicles in Partially Observable Environments
TIBKAT | 2023
|Motion planning for autonomous vehicles in partially observable environments
TIBKAT | 2022
|Decision-Making for Autonomous Systems in Partially Observable Environments
BASE | 2013
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