How MuCAR won the convoy scenario at ELROB 2016

Fries, Carsten; Burger, Patrick; Kallwies, Jan; Naujoks, Benjamin; Luettel, Thorsten; Wuensche, Hans-Joachim

doi:10.1109/itsc.2017.8317711

Cited by 8 publications

(1 citation statement)

References 18 publications

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“…Furthermore, GDPF can deal with over segmentation of a previous clustering step. Figure 1 shows an exemplary use case of the GDPF in a suburban area, which was recorded with a roof-mounted Velodyne HDL64-S2 [7] of our institute's autonomous car MuCAR-3 [8].…”

Section: Introductionmentioning

confidence: 99%

The Greedy Dirichlet Process Filter -An Online Clustering Multi-Target Tracker

Naujoks

Burger

Wuensche

2018

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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Reliable collision avoidance is one of the main requirements for autonomous driving. Hence, it is important to correctly estimate the states of an unknown number of static and dynamic objects in real-time. Here, data association is a major challenge for every multi-target tracker. We propose a novel multi-target tracker called Greedy Dirichlet Process Filter (GDPF) based on the non-parametric Bayesian model called Dirichlet Processes and the fast posterior computation algorithm Sequential Updating and Greedy Search (SUGS). By adding a temporal dependence we get a real-time capable tracking framework without the need of a previous clustering or data association step. Real-world tests show that GDPF outperforms other multi-target tracker in terms of accuracy and stability.

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Section: Introductionmentioning

confidence: 99%