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Titel:

Data Association for Grid-Based Object Tracking Using Particle Labeling

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Steyer, Sascha; Tanzmeister, Georg; Lenk, Christian; Dallabetta, Vinzenz; Wollherr, Dirk
Abstract:
Estimating surrounding objects and obstacles by processing sensor data is essential for safe autonomous driving. Grid-based approaches discretize the environment into grid cells, which implicitly solves the data association between measurement data and the filtered state on this grid representation. Recent approaches estimate, in addition to occupancy probabilities, cell velocity distributions using a low-level particle filter. Measured occupancy can thus be classified as static or dynamic, wher...     »
Stichworte:
object detection; object tracking; particle filtering (numerical methods); target tracking; traffic engineering computing; data association; grid-based object tracking; particle labeling; surrounding objects; sensor data; safe autonomous driving; grid cells; measurement data; filtered state; grid representation; cell velocity distributions; measured occupancy; subsequent tracking; moving objects; dynamic cells; multiple predicted objects; high-level objects; object label; particle label distribu...     »
Kongress- / Buchtitel:
2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Ausrichter der Konferenz:
IEEE
Datum der Konferenz:
November 4-7, 2018
Jahr:
2018
Monat:
Nov
Seiten:
3036-3043
Volltext / DOI:
doi:10.1109/ITSC.2018.8569511
Semester:
WS 18-19
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