Belief State Planning For Autonomous Driving Planning With Interaction Uncertain Prediction And Uncertain Perception
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Belief State Planning for Autonomous Driving: Planning with Interaction, Uncertain Prediction and Uncertain Perception
Author: Hubmann, Constantin
language: en
Publisher: KIT Scientific Publishing
Release Date: 2021-09-13
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.
Motion Planning for Autonomous Vehicles in Partially Observable Environments
Author: Taş, Ömer Şahin
language: en
Publisher: KIT Scientific Publishing
Release Date: 2023-10-23
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.
Probabilistic Motion Planning for Automated Vehicles
Author: Naumann, Maximilian
language: en
Publisher: KIT Scientific Publishing
Release Date: 2021-02-25
In motion planning for automated vehicles, a thorough uncertainty consideration is crucial to facilitate safe and convenient driving behavior. This work presents three motion planning approaches which are targeted towards the predominant uncertainties in different scenarios, along with an extended safety verification framework. The approaches consider uncertainties from imperfect perception, occlusions and limited sensor range, and also those in the behavior of other traffic participants.