1 code implementation • 25 Nov 2024 • Georg Hess, Carl Lindström, Maryam Fatemi, Christoffer Petersson, Lennart Svensson
Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive testing across diverse driving scenarios.
no code implementations • 24 Mar 2024 • Carl Lindström, Georg Hess, Adam Lilja, Maryam Fatemi, Lars Hammarstrand, Christoffer Petersson, Lennart Svensson
Specifically, we evaluate object detectors and an online mapping model on real and simulated data, and study the effects of different fine-tuning strategies. Our results show notable improvements in model robustness to simulated data, even improving real-world performance in some cases.
no code implementations • 22 Dec 2023 • Juliano Pinto, Georg Hess, Yuxuan Xia, Henk Wymeersch, Lennart Svensson
Multi-object tracking (MOT) is the task of estimating the state trajectories of an unknown and time-varying number of objects over a certain time window.
2 code implementations • CVPR 2024 • Adam Tonderski, Carl Lindström, Georg Hess, William Ljungbergh, Lennart Svensson, Christoffer Petersson
Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community.
1 code implementation • ICCV 2023 • Mina Alibeigi, William Ljungbergh, Adam Tonderski, Georg Hess, Adam Lilja, Carl Lindstrom, Daria Motorniuk, Junsheng Fu, Jenny Widahl, Christoffer Petersson
The dataset is composed of Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatio-temporal learning, sensor fusion, localization, and mapping.
1 code implementation • 13 Dec 2022 • Georg Hess, Adam Tonderski, Christoffer Petersson, Kalle Åström, Lennart Svensson
We also explore zero-shot classification and show that LidarCLIP outperforms existing attempts to use CLIP for point clouds by a large margin.
1 code implementation • 1 Jul 2022 • Georg Hess, Johan Jaxing, Elias Svensson, David Hagerman, Christoffer Petersson, Lennart Svensson
Masked autoencoding has become a successful pretraining paradigm for Transformer models for text, images, and, recently, point clouds.
1 code implementation • 15 Mar 2022 • Georg Hess, Christoffer Petersson, Lennart Svensson
Accurate uncertainty estimates are essential for deploying deep object detectors in safety-critical systems.
1 code implementation • 16 Feb 2022 • Juliano Pinto, Georg Hess, William Ljungbergh, Yuxuan Xia, Henk Wymeersch, Lennart Svensson
Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as autonomous driving, tracking animal behavior, defense systems, and others.
no code implementations • 13 Apr 2021 • Georg Hess, William Ljungbergh
This paper deploys the Deep Deterministic Policy Gradient (DDPG) algorithm for longitudinal and lateral control of a simulated car to solve a path following task.
1 code implementation • 1 Apr 2021 • Juliano Pinto, Georg Hess, William Ljungbergh, Yuxuan Xia, Lennart Svensson, Henk Wymeersch
We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while matching their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime.