9783731510390.pdf
This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive be...
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KIT Scientific Publishing
2021
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oapen-20.500.12657-51091 |
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oapen-20.500.12657-510912021-10-20T02:48:26Z Belief State Planning for Autonomous Driving Hubmann, Constantin Autonomes Fahren Entscheidungsfindung Verhaltensgenerierung Trajektorienplanung Interaktion Autonomous Driving Decision Making Behavior Planning Trajectory Planning Interactive Planning bic Book Industry Communication::T Technology, engineering, agriculture::TG Mechanical engineering & materials This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive behavior of the other agents explicitly. Simulating the most likely future scenarios allows to find an optimal policy online that enables non-conservative planning under uncertainty. 2021-10-19T08:21:35Z 2021-10-19T08:21:35Z 2021 book ONIX_20211019_9783731510390_2 1613-4214 9783731510390 https://library.oapen.org/handle/20.500.12657/51091 eng Schriftenreihe / Institut für Mess- und Regelungstechnik, Karlsruher Institut für Technologie application/pdf n/a 9783731510390.pdf KIT Scientific Publishing KIT Scientific Publishing 10.5445/KSP/1000122855 10.5445/KSP/1000122855 44e29711-8d53-496b-85cc-3d10c9469be9 9783731510390 KIT Scientific Publishing 47 180 Karlsruhe open access |
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OAPEN |
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English |
| description |
This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive behavior of the other agents explicitly. Simulating the most likely future scenarios allows to find an optimal policy online that enables non-conservative planning under uncertainty. |
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9783731510390.pdf |
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9783731510390.pdf |
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KIT Scientific Publishing |
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2021 |
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