Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Kowol, Kamil"'
In this study, we propose a novel approach to enrich the training data for automated driving by using a self-designed driving simulator and two human drivers to generate safety-critical corner cases in a short period of time, as already presented in~
Externí odkaz:
http://arxiv.org/abs/2305.18222
Deep neural networks (DNN) which are employed in perception systems for autonomous driving require a huge amount of data to train on, as they must reliably achieve high performance in all kinds of situations. However, these DNN are usually restricted
Externí odkaz:
http://arxiv.org/abs/2302.02790
Autor:
Rösch, Kevin, Heidecker, Florian, Truetsch, Julian, Kowol, Kamil, Schicktanz, Clemens, Bieshaar, Maarten, Sick, Bernhard, Stiller, Christoph
Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns. Based on these predictions - and additional contextual information suc
Externí odkaz:
http://arxiv.org/abs/2210.08885
In this work we present two video test data sets for the novel computer vision (CV) task of out of distribution tracking (OOD tracking). Here, OOD objects are understood as objects with a semantic class outside the semantic space of an underlying ima
Externí odkaz:
http://arxiv.org/abs/2210.02074
The overall goal of this work is to enrich training data for automated driving with so called corner cases. In road traffic, corner cases are critical, rare and unusual situations that challenge the perception by AI algorithms. For this purpose, we p
Externí odkaz:
http://arxiv.org/abs/2202.10803
In this work, we present an uncertainty-based method for sensor fusion with camera and radar data. The outputs of two neural networks, one processing camera and the other one radar data, are combined in an uncertainty aware manner. To this end, we ga
Externí odkaz:
http://arxiv.org/abs/2010.03320
Autor:
Maag, Kira, Chan, Robin Kien-Wei, Uhlemeyer, Svenja, Kowol, Kamil, Gottschalk, Hanno, Wang, Lei, Gall, Juergen, Chin, Tat-Jun, Sato, Imari, Chellappa, Rama
Publikováno v:
Computer Vision – ACCV 2022 ISBN: 9783031263477
In this work we present two video test data sets for the novel computer vision (CV) task of out of distribution tracking (OOD tracking). Here, OOD objects are understood as objects with a semantic class outside the semantic space of an underlying ima
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a181c0e4be62b547f8266df02bb8a8b4
https://doi.org/10.1007/978-3-031-26348-4_28
https://doi.org/10.1007/978-3-031-26348-4_28