Motion Planning for Autonomous Vehicles in Partially Observable Environments
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...
Gorde:
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| Formatua: | Online |
| Hizkuntza: | ingelesa |
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KIT Scientific Publishing
2023
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| Gaiak: | |
| Sarrera elektronikoa: | OCN: 1410104286 |
| Etiketak: |
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| Gaia: | 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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