Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Timo Rehfeld"'
Autor:
Li Zhang, Faezeh Tafazzoli, Gunther Krehl, Runsheng Xu, Timo Rehfeld, Manuel Schier, Arunava Seal
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fa96b9b1ea7448ea291467c1bd8939ea
https://doi.org/10.1109/iros47612.2022.9981820
https://doi.org/10.1109/iros47612.2022.9981820
Publikováno v:
2020 IEEE Intelligent Vehicles Symposium (IV).
High-Definition (HD) Maps are indispensable components of an autonomous vehicle software stack, containing a precise representation of the static surroundings. Prediction, motion planning and vehicle behavior heavily rely on the accuracy of the HD Ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4bca2c60da380f6a896a2eacadfced0f
https://doi.org/10.1109/iv47402.2020.9304535
https://doi.org/10.1109/iv47402.2020.9304535
Publikováno v:
ICPR
Intersection scenarios provide the most complex traffic situations in Autonomous Driving and Driving Assistance Systems. Knowing where to stop in advance in an intersection is an essential parameter in controlling the longitudinal velocity of the veh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aff25f7a30c2ccfbc8ff892153847a38
http://arxiv.org/abs/2009.09093
http://arxiv.org/abs/2009.09093
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 39:1444-1454
We propose a novel approach to semantic scene labeling in urban scenarios, which aims to combine excellent recognition performance with highest levels of computational efficiency. To that end, we exploit efficient tree-structured models on two levels
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae2cae104092e4b3dc470fa75fb03fcf
https://doi.org/10.1109/tpami.2016.2592911
https://doi.org/10.1109/tpami.2016.2592911
Publikováno v:
SMC
In this paper, we present a method to estimate abstract parameters of high definition (HD) maps from sensor data. Parameters we estimate include the distance from ego-vehicle to road boundary, orientation of the ego-vehicle with respect to lanes, num
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::660f2b78a3ae6996293743efe5915908
https://doi.org/10.1109/smc.2017.8122577
https://doi.org/10.1109/smc.2017.8122577
Publikováno v:
Intelligent Vehicles Symposium
Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications. In this work, we present a methods that uses a deep convoluti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37635646f1ea146125448d5a573b0c1e
https://doi.org/10.1109/ivs.2017.7995750
https://doi.org/10.1109/ivs.2017.7995750
Autor:
Uwe Franke, Lukas Schneider, David Pfeiffer, Stefan Roth, Marius Cordts, Markus Enzweiler, Timo Rehfeld, Marc Pollefeys
Recent progress in advanced driver assistance systems and the race towards autonomous vehicles is mainly driven by two factors: (1) increasingly sophisticated algorithms that interpret the environment around the vehicle and react accordingly, and (2)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60f85076a947b62e49a7023cf7ec1df6
Autor:
Marius Cordts, Markus Enzweiler, Bernt Schiele, Timo Rehfeld, Mohamed Omran, Uwe Franke, Sebastian Ramos, Stefan Roth, Rodrigo Benenson
Publikováno v:
CVPR
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene unde
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c21a918c06175e4022563b11d30dbdea
https://doi.org/10.1109/cvpr.2016.350
https://doi.org/10.1109/cvpr.2016.350
Autor:
Stefan Roth, Uwe Franke, Lukas Schneider, Marius Cordts, Markus Enzweiler, Marc Pollefeys, Timo Rehfeld, David Pfeiffer
Publikováno v:
Intelligent Vehicles Symposium
In this paper we present Semantic Stixels, a novel vision-based scene model geared towards automated driving. Our model jointly infers the geometric and semantic layout of a scene and provides a compact yet rich abstraction of both cues using Stixels
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::382fdd2c6041a8851198b1f9a4c13c31
https://doi.org/10.1109/ivs.2016.7535373
https://doi.org/10.1109/ivs.2016.7535373