Motion Planning for Autonomous Vehicles in Partially Observable Environments - Taş, Ömer Şahin (Schriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie, 2023)
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.