Zobrazeno 1 - 10
of 979
pro vyhledávání: '"P Klemp"'
Anonymization plays a key role in protecting sensible information of individuals in real world datasets. Self-driving cars for example need high resolution facial features to track people and their viewing direction to predict future behaviour and re
Externí odkaz:
http://arxiv.org/abs/2410.08551
Autor:
Wagner, Royden, Tas, Ömer Sahin, Steiner, Marlon, Konstantinidis, Fabian, Königshof, Hendrik, Klemp, Marvin, Fernandez, Carlos, Stiller, Christoph
Self-driving vehicles rely on multimodal motion forecasts to effectively interact with their environment and plan safe maneuvers. We introduce SceneMotion, an attention-based model for forecasting scene-wide motion modes of multiple traffic agents. O
Externí odkaz:
http://arxiv.org/abs/2408.01537
Autor:
Eisemann, Leon, Fehling-Kaschek, Mirjam, Forkert, Silke, Forster, Andreas, Gommel, Henrik, Guenther, Susanne, Hammer, Stephan, Hermann, David, Klemp, Marvin, Lickert, Benjamin, Luettner, Florian, Moss, Robin, Neis, Nicole, Pohle, Maria, Schreiber, Dominik, Sowa, Cathrina, Stadler, Daniel, Stompe, Janina, Strobelt, Michael, Unger, David, Ziehn, Jens
Publikováno v:
Proceedings of the 7th International Symposium on Future Active Safety Technology toward zero traffic accidents (JSAE FAST-zero '23), 2023
With growing complexity and responsibility of automated driving functions in road traffic and growing scope of their operational design domains, there is increasing demand for covering significant parts of development, validation, and verification vi
Externí odkaz:
http://arxiv.org/abs/2405.06286
Autor:
Eisemann, Leon, Fehling-Kaschek, Mirjam, Gommel, Henrik, Hermann, David, Klemp, Marvin, Lauer, Martin, Lickert, Benjamin, Luettner, Florian, Moss, Robin, Neis, Nicole, Pohle, Maria, Romanski, Simon, Stadler, Daniel, Stolz, Alexander, Ziehn, Jens, Zhou, Jingxing
With growing complexity and criticality of automated driving functions in road traffic and their operational design domains (ODD), there is increasing demand for covering significant proportions of development, validation, and verification in virtual
Externí odkaz:
http://arxiv.org/abs/2405.01776
There is a gap in risk assessment of trajectories between the trajectory information coming from a traffic motion prediction module and what is actually needed. Closing this gap necessitates advancements in prediction beyond current practices. Existi
Externí odkaz:
http://arxiv.org/abs/2404.19283
We present JointMotion, a self-supervised pre-training method for joint motion prediction in self-driving vehicles. Our method jointly optimizes a scene-level objective connecting motion and environments, and an instance-level objective to refine lea
Externí odkaz:
http://arxiv.org/abs/2403.05489
Autor:
Klemp, Oliver
This technical document presents the committee driven innovation modeling methodology "Innovation Modeling Grid" in detail. This document is the successor of three publications on IMoG and focuses on presenting all details of the methodology
Com
Com
Externí odkaz:
http://arxiv.org/abs/2309.16507
We introduce RedMotion, a transformer model for motion prediction in self-driving vehicles that learns environment representations via redundancy reduction. Our first type of redundancy reduction is induced by an internal transformer decoder and redu
Externí odkaz:
http://arxiv.org/abs/2306.10840
In self-driving applications, LiDAR data provides accurate information about distances in 3D but lacks the semantic richness of camera data. Therefore, state-of-the-art methods for perception in urban scenes fuse data from both sensor types. In this
Externí odkaz:
http://arxiv.org/abs/2306.07087
[Context and motivation] The automotive industry is currently undergoing a fundamental transformation towards software defined vehicles. The automotive market of the future demands a higher level of automation, electrification of the power train, and
Externí odkaz:
http://arxiv.org/abs/2304.09110