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Title:

Non-Conservative Trajectory Planning for Automated Vehicles by Estimating Intentions of Dynamic Obstacles

Document type:
Zeitschriftenaufsatz
Author(s):
Benciolini, Tommaso; Wollherr, Dirk; Leibold, Marion
Abstract:
Motion planning algorithms for urban automated driving must handle uncertainty due to unknown intention and future motion of Dynamic Obstacles (DOs). Considering a single future trajectory for each DO is not adequate, especially in urban frameworks where traffic participants exhibit very different behaviors. However, including multiple candidate trajectories representing different behaviors results in an excess of conservatism. We present a novel combination of the Interactive Multiple Model (IM...     »
Journal title:
IEEE Transactions on Intelligent Vehicles
Year:
2023
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1109/TIV.2023.3234163
Publisher:
IEEE
Semester:
WS 22-23
 BibTeX