Zobrazeno 1 - 10
of 2 714
pro vyhledávání: '"Fent A"'
Autor:
Wolters, Philipp, Gilg, Johannes, Teepe, Torben, Herzog, Fabian, Fent, Felix, Rigoll, Gerhard
In this work, we present SpaRC, a novel Sparse fusion transformer for 3D perception that integrates multi-view image semantics with Radar and Camera point features. The fusion of radar and camera modalities has emerged as an efficient perception para
Externí odkaz:
http://arxiv.org/abs/2411.19860
Autor:
Fent, Felix, Kuttenreich, Fabian, Ruch, Florian, Rizwin, Farija, Juergens, Stefan, Lechermann, Lorenz, Nissler, Christian, Perl, Andrea, Voll, Ulrich, Yan, Min, Lienkamp, Markus
Autonomous trucking is a promising technology that can greatly impact modern logistics and the environment. Ensuring its safety on public roads is one of the main duties that requires an accurate perception of the environment. To achieve this, machin
Externí odkaz:
http://arxiv.org/abs/2407.07462
State-of-the-art LiDAR calibration frameworks mainly use non-probabilistic registration methods such as Iterative Closest Point (ICP) and its variants. These methods suffer from biased results due to their pair-wise registration procedure as well as
Externí odkaz:
http://arxiv.org/abs/2404.03427
The perception of autonomous vehicles has to be efficient, robust, and cost-effective. However, cameras are not robust against severe weather conditions, lidar sensors are expensive, and the performance of radar-based perception is still inferior to
Externí odkaz:
http://arxiv.org/abs/2404.03015
Publikováno v:
IEEE Transactions on Intelligent Vehicles, vol. 8, no. 7, pp. 3871-3883, July 2023
Reliable detection and tracking of surrounding objects are indispensable for comprehensive motion prediction and planning of autonomous vehicles. Due to the limitations of individual sensors, the fusion of multiple sensor modalities is required to im
Externí odkaz:
http://arxiv.org/abs/2310.08114
EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application
Autor:
Karle, Phillip, Betz, Tobias, Bosk, Marcin, Fent, Felix, Gehrke, Nils, Geisslinger, Maximilian, Gressenbuch, Luis, Hafemann, Philipp, Huber, Sebastian, Hübner, Maximilian, Huch, Sebastian, Kaljavesi, Gemb, Kerbl, Tobias, Kulmer, Dominik, Mascetta, Tobias, Maierhofer, Sebastian, Pfab, Florian, Rezabek, Filip, Rivera, Esteban, Sagmeister, Simon, Seidlitz, Leander, Sauerbeck, Florian, Tahiraj, Ilir, Trauth, Rainer, Uhlemann, Nico, Würsching, Gerald, Zarrouki, Baha, Althoff, Matthias, Betz, Johannes, Bengler, Klaus, Carle, Georg, Diermeyer, Frank, Ott, Jörg, Lienkamp, Markus
While current research and development of autonomous driving primarily focuses on developing new features and algorithms, the transfer from isolated software components into an entire software stack has been covered sparsely. Besides that, due to the
Externí odkaz:
http://arxiv.org/abs/2309.15492
In this paper, we assess the state of the art in pedestrian trajectory prediction within the context of generating single trajectories, a critical aspect aligning with the requirements in autonomous systems. The evaluation is conducted on the widely-
Externí odkaz:
http://arxiv.org/abs/2308.05194
A reliable perception has to be robust against challenging environmental conditions. Therefore, recent efforts focused on the use of radar sensors in addition to camera and lidar sensors for perception applications. However, the sparsity of radar poi
Externí odkaz:
http://arxiv.org/abs/2304.06547
Autor:
Yanbin Zhao, Karl Fent
Publikováno v:
Eco-Environment & Health, Vol 3, Iss 3, Pp 257-259 (2024)
Externí odkaz:
https://doaj.org/article/e182ec5679f046f09106d2f3f5c36fff
Autor:
Asal, İlhan1 feedback32@hotmail.com, Fent, Meral2 m_fent@hotmail.com
Publikováno v:
Journal of the Heteroptera of Turkey. Nov2024, Vol. 6 Issue 2, p129-134. 6p.