Zobrazeno 1 - 10
of 337
pro vyhledávání: '"Tonderski, A."'
Autor:
Ljungbergh, William, Tonderski, Adam, Johnander, Joakim, Caesar, Holger, Åström, Kalle, Felsberg, Michael, Petersson, Christoffer
We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation of safety-critical scenarios. The simulator learns from sequences of r
Externí odkaz:
http://arxiv.org/abs/2404.07762
Autor:
Calvo, Ernesto Lozano, Taveira, Bernardo, Kahl, Fredrik, Gustafsson, Niklas, Larsson, Jonathan, Tonderski, Adam
Object detection applied to LiDAR point clouds is a relevant task in robotics, and particularly in autonomous driving. Single frame methods, predominant in the field, exploit information from individual sensor scans. Recent approaches achieve good pe
Externí odkaz:
http://arxiv.org/abs/2312.17260
Autor:
Tonderski, Adam, Lindström, Carl, Hess, Georg, Ljungbergh, William, Svensson, Lennart, Petersson, Christoffer
Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation, enabling testing of AD systems, and as an advanced training data augmentation technique.
Externí odkaz:
http://arxiv.org/abs/2311.15260
Autor:
Meding, Isak, Bodin, Alexander, Tonderski, Adam, Johnander, Joakim, Petersson, Christoffer, Svensson, Lennart
Ensembles of independently trained deep neural networks yield uncertainty estimates that rival Bayesian networks in performance. They also offer sizable improvements in terms of predictive performance over single models. However, deep ensembles are n
Externí odkaz:
http://arxiv.org/abs/2309.11333
Autor:
Alibeigi, Mina, Ljungbergh, William, Tonderski, Adam, Hess, Georg, Lilja, Adam, Lindstrom, Carl, Motorniuk, Daria, Fu, Junsheng, Widahl, Jenny, Petersson, Christoffer
Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360{\deg} perception and temporal reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a large-scale and diverse
Externí odkaz:
http://arxiv.org/abs/2305.02008
Research connecting text and images has recently seen several breakthroughs, with models like CLIP, DALL-E 2, and Stable Diffusion. However, the connection between text and other visual modalities, such as lidar data, has received less attention, pro
Externí odkaz:
http://arxiv.org/abs/2212.06858
Autor:
Jan-Olof Drangert, Karin Tonderski
Publikováno v:
City and Environment Interactions, Vol 23, Iss , Pp 100149- (2024)
Externí odkaz:
https://doaj.org/article/701286115286426990faa0ee456c918f
Autor:
Skvortsova P. O., Ablieieva I. Yu., Tonderski K., Chernysh Ye. Yu., Plyatsuk L. D., Sipko I. O., Mykhno H. I.
Publikováno v:
Журнал інженерних наук, Vol 11, Iss 1, Pp H9-H20 (2024)
The main idea was to justify the natural, technological, and ecological aspects of digestate-based composite for heavy metals (HMs) binding in soil due to organic matter content and mineral additives’ biosorption properties. The study aimed to dete
Externí odkaz:
https://doaj.org/article/db654888c063496e8f9daba5050ad476
We propose the task Future Object Detection, in which the goal is to predict the bounding boxes for all visible objects in a future video frame. While this task involves recognizing temporal and kinematic patterns, in addition to the semantic and geo
Externí odkaz:
http://arxiv.org/abs/2204.10321
Publikováno v:
In Resources, Conservation & Recycling October 2024 209