Pedestrian tracking using Velodyne data-Stochastic optimization for extended object tracking
Paper i proceeding, 2017

Environment perception is a key enabling technology in autonomous vehicles, and multiple object tracking is an important part of this. High resolution sensors, such as automotive radar and lidar, leads to the so called extended target tracking problem, in which there are multiple detections per tracked object. For computationally feasible multiple extended target tracking, the data association problem must be handled. Previous work has relied on the use of clustering algorithms, together with assignment algorithms, to achieve this. In this paper we present a stochastic optimisation method that directly maximises the desired likelihood function, and solves the problem in a single step, rather than two steps (clustering+assignment). The proposed method is evaluated against previous work in an experiment where Velodyne data is used to track pedestrians, and the results clearly show that the proposed method achieves the best performance, especially in challenging scenarios.


Karl Granström

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

S. Renter

Universität Ulm

Maryam Fatemi

Zenuity AB

Lennart Svensson

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 11-14 June 2017


COPPLAR CampusShuttle cooperative perception & planning platform

VINNOVA (2015-04849), 2016-01-01 -- 2018-12-31.





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