Zobrazeno 1 - 10
of 533
pro vyhledávání: '"Lienkamp, Markus"'
This paper explores pedestrian trajectory prediction in urban traffic while focusing on both model accuracy and real-world applicability. While promising approaches exist, they are often not publicly available, revolve around pedestrian datasets excl
Externí odkaz:
http://arxiv.org/abs/2409.01971
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
Improving access to essential public services like healthcare and education is crucial for human development, particularly in rural Sub-Saharan Africa. However, limited reliable transportation and sparse public facilities present significant challeng
Externí odkaz:
http://arxiv.org/abs/2402.05118
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
Autor:
Loder, Allister, Cantner, Fabienne, Adenaw, Lennart, Nachtigall, Nico, Ziegler, David, Gotzler, Felix, Siewert, Markus B., Wurster, Stefan, Goerg, Sebastian, Lienkamp, Markus, Bogenberger, Klaus
In spring 2022, the German federal government agreed on a set of policy measures that aimed at reducing households' financial burden resulting from a recent price increase, especially in energy and mobility. These included among others, a nationwide
Externí odkaz:
http://arxiv.org/abs/2306.08297
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
Publikováno v:
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023
LiDAR object detection algorithms based on neural networks for autonomous driving require large amounts of data for training, validation, and testing. As real-world data collection and labeling are time-consuming and expensive, simulation-based synth
Externí odkaz:
http://arxiv.org/abs/2303.01899