Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Stefan Zernetsch"'
Publikováno v:
2022 IEEE Intelligent Vehicles Symposium (IV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d3584979dc8b999d892a7ea0119e45a
https://doi.org/10.1109/iv51971.2022.9827220
https://doi.org/10.1109/iv51971.2022.9827220
Autor:
Konrad Doll, Stefan Zernetsch, Michael Goldhammer, Bernhard Sick, Sebastian Köhler, Klaus Dietmayer
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 21:3035-3045
Avoiding collisions with vulnerable road users (VRUs) using sensor-based early recognition of critical situations is one of the manifold opportunities provided by the current development in the field of intelligent vehicles. As, especially, pedestria
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec36c7db720c9fcdae5c2f497b735423
https://doi.org/10.1109/tits.2019.2923319
https://doi.org/10.1109/tits.2019.2923319
Publikováno v:
2021 IEEE Symposium Series on Computational Intelligence (SSCI).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c9f14b67a5c9308857df313b5430632b
https://doi.org/10.1109/ssci50451.2021.9660151
https://doi.org/10.1109/ssci50451.2021.9660151
Autor:
Jan Schneegans, Jan Eilbrecht, Stefan Zernetsch, Maarten Bieshaar, Konrad Doll, Olaf Stursberg, Bernhard Sick
Publikováno v:
2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb1e783a52c74d5ae5648ed5e819120c
https://doi.org/10.1109/ivworkshops54471.2021.9669208
https://doi.org/10.1109/ivworkshops54471.2021.9669208
In this article, an approach for probabilistic trajectory forecasting of vulnerable road users (VRUs) is presented, which considers past movements and the surrounding scene. Past movements are represented by 3D poses reflecting the posture and moveme
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bfeb651945c7ea95d930dcbe50b0133f
http://arxiv.org/abs/2106.02598
http://arxiv.org/abs/2106.02598
Publikováno v:
ICPR
In this article, we present an approach to detect basic movements of cyclists in real world traffic situations based on image sequences, optical flow (OF) sequences, and past positions using a multi-stream 3D convolutional neural network (3D-ConvNet)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8cefc7b77ca3d2b992381afa2d565551
https://doi.org/10.1109/icpr48806.2021.9413233
https://doi.org/10.1109/icpr48806.2021.9413233
Publikováno v:
Pattern Recognition. ICPR International Workshops and Challenges ISBN: 9783030687625
ICPR Workshops (1)
ICPR Workshops (1)
In this work, we use Recurrent Neural Networks (RNNs) in form of Gated Recurrent Unit (GRU) networks to forecast trajectories of vulnerable road users (VRUs), such as pedestrians and cyclists, in road traffic utilizing the past trajectory and 3D pose
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c627702e5d090ef27e5ff4ef5f2e5c80
https://doi.org/10.1007/978-3-030-68763-2_5
https://doi.org/10.1007/978-3-030-68763-2_5
Publikováno v:
SSCI
This work investigates the use of knowledge about three dimensional (3D) poses and Recurrent Neural Networks (RNNs) for detection of basic movements, such as wait, start, move, stop, turn left, turn right, and no turn, of pedestrians and cyclists in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20569eec56b55eb1dd7fe18ca2b49649
https://doi.org/10.1109/ssci47803.2020.9308462
https://doi.org/10.1109/ssci47803.2020.9308462
Publikováno v:
SSCI
In this article, we investigate the use of 3D human poses for trajectory forecasting of vulnerable road users (VRUs), such as pedestrians and cyclists, in road traffic. The forecast is based on past movements of the respective VRU and an important as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8af2ff163672ece8e1620e5a2654469a
https://doi.org/10.1109/ssci44817.2019.9003023
https://doi.org/10.1109/ssci44817.2019.9003023
Publikováno v:
2019 IEEE Intelligent Vehicles Symposium (IV).
In this article, we present an approach to forecast trajectories of vulnerable road users (VRUs) including a numerical quantification of the uncertainty of the forecast. The uncertainty estimates are modeled as normal distributions by means of neural
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd24077aec1c3978c7f8c2f16c594b75
https://doi.org/10.1109/ivs.2019.8814258
https://doi.org/10.1109/ivs.2019.8814258