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|Titel DE=Best Dissertation Award - IEEE ITS Society | |Titel DE=Best Dissertation Award - IEEE ITS Society | ||
|Titel EN=Best Dissertation Award - IEEE ITS Society | |Titel EN=Best Dissertation Award - IEEE ITS Society | ||
− | |Beschreibung DE='''Holger Banzhaf wurde für seine Dissertation ''Nonholonomic Motion Planning for Automated Vehicles in Dense Scenarios''mit dem Best Dissertation Award ausgezeichnet.'''<br><br>'''Abstract'''<br> | + | |Beschreibung DE='''Holger Banzhaf wurde für seine Dissertation ''Nonholonomic Motion Planning for Automated Vehicles in Dense Scenarios'' mit dem Best Dissertation Award ausgezeichnet.'''<br><br>'''Abstract'''<br> |
− | |Beschreibung EN='''Holger Banzhaf was awarded with the Best Dissertation Award for his dissertation named ''Nonholonomic Motion Planning for Automated Vehicles in Dense Scenarios'''''<br><br>'''Abstract'''<br>Motion planning | + | |
+ | |Beschreibung EN='''Holger Banzhaf was awarded with the Best Dissertation Award for his dissertation named ''Nonholonomic Motion Planning for Automated Vehicles in Dense Scenarios'''''<br><br>'''Abstract'''<br> Motion planning is one of the crucial components in the software stack of | ||
+ | an automated vehicle. It is responsible for the computation of a safe and | ||
+ | preferably optimal trajectory from a given start state to a desired goal. While | ||
+ | a local solution to this problem is sufficient for highway driving, this thesis | ||
+ | focuses on the computation of a global solution, which is typically required to | ||
+ | handle unstructured environments or complex maneuvers. Relevant scenar�ios include dead ends, blocked lanes, or various parking problems that have | ||
+ | proven difficult for automated vehicles to solve, particularly when space is | ||
+ | tight. | ||
+ | The contributions of this thesis can be grouped into three parts. The first | ||
+ | part focuses on steering functions for car-like robots, which play a major | ||
+ | role in both search-based and sampling-based motion planning. Within this | ||
+ | context, the novel steering function hybrid curvature (HC) steer is introduced | ||
+ | that computes smoother paths than the well-known Reeds-Shepp steering | ||
+ | function [152] and shorter paths than continuous curvature (CC) steer [53]. | ||
+ | Especially in tight environments, HC steer proves to be a powerful tool | ||
+ | for the computation of directly executable motion plans with continuous | ||
+ | curvature between direction switches. In addition to that, the two novel | ||
+ | steering functions continuous curvature rate (CCR) and hybrid curvature | ||
+ | rate (HCR) steer are presented that extend the smoothness of both CC and | ||
+ | HC steer from curvature to curvature rate continuity. This allows to increase | ||
+ | the comfort for passengers as well as the tracking performance of the low�level motion controller. | ||
+ | The second part of this thesis focuses on motion planning under uncer�tainty aiming to improve the robustness of the motion plans by explicitly | ||
+ | considering the localization and control errors of the system. For this pur�pose, the previously mentioned steering functions are extended to belief | ||
+ | space in which every vehicle state is associated with its respective uncer�tainty. Furthermore, two novel algorithms for probabilistic collision checking | ||
+ | are introduced in order to bound the collision probability of the computed | ||
+ | vehicle motion. The third part addresses the problem of slow convergence in sampling�based motion planning if samples are only drawn from a uniform distribution. | ||
+ | To overcome this problem, a data-driven approach is presented that utilizes a | ||
+ | convolutional neural network to predict a distribution over future vehicle | ||
+ | poses given the current environment and the boundary conditions of the | ||
+ | planning problem. Samples from this distribution can then be used to bias | ||
+ | the motion planner towards promising regions in the state space allowing to | ||
+ | improve the planning performance in complex scenarios. | ||
+ | Finally, the proposed methods from all three parts are integrated into the | ||
+ | sampling-based motion planner RRT* [93] and its bidirectional extension | ||
+ | BiRRT* [86] to demonstrate their benefits in a broad set of challenging en�vironments. The motion planner is not only tested in simulation, but also | ||
+ | integrated into a research vehicle proving its effectiveness in real-world | ||
+ | applications. | ||
|Datum=2020/09/21 | |Datum=2020/09/21 | ||
}} | }} |
Version vom 6. August 2021, 11:54 Uhr
Neuigkeit vom 21. September 2020
Best Dissertation Award - IEEE ITS Society
Holger Banzhaf wurde für seine Dissertation Nonholonomic Motion Planning for Automated Vehicles in Dense Scenarios mit dem Best Dissertation Award ausgezeichnet.
Abstract