1 citations · 2 across the 7 of their papers we have counts for
9 papers
R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation
William Ljungbergh, Bernardo Taveira, Wenzhao Zheng +8
Validating autonomous driving (AD) systems requires diverse and safety-critical testing, making photorealistic virtual environments essential. Traditional simulation platforms, whi…
PEAR: Equal Area Weather Forecasting on the Sphere
Hampus Linander, Tage Tykesson, Pietro Rosso +3
Artificial intelligence is rapidly reshaping the natural sciences, with weather forecasting emerging as a flagship AI4Science application where machine learning models can now riva…
Decoupled Diffusion Sparks Adaptive Scene Generation
Yunsong Zhou, Naisheng Ye, William Ljungbergh +6
Controllable scene generation could reduce the cost of diverse data collection substantially for autonomous driving. Prior works formulate the traffic layout generation as predicti…
GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for Autonomous Driving
William Ljungbergh, Adam Lilja, Adam Tonderski. Arvid Laveno Ling +6
Self-supervised pre-training based on next-token prediction has enabled large language models to capture the underlying structure of text, and has led to unprecedented performance…
SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving
Georg Hess, Carl Lindström, Maryam Fatemi +2
Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive testing across diverse driving scenarios. Simulation is a key ingredient for conducting…
NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving
William Ljungbergh, Adam Tonderski, Joakim Johnander +4
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…
Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gap
Carl Lindström, Georg Hess, Adam Lilja +4
Neural Radiance Fields (NeRFs) have emerged as promising tools for advancing autonomous driving (AD) research, offering scalable closed-loop simulation and data augmentation capabi…
NeuRAD: Neural Rendering for Autonomous Driving
Adam Tonderski, Carl Lindström, Georg Hess +3
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…
You can have your ensemble and run it too -- Deep Ensembles Spread Over Time
Isak Meding, Alexander Bodin, Adam Tonderski +3
Ensembles of independently trained deep neural networks yield uncertainty estimates that rival Bayesian networks in performance. They also offer sizable improvements in terms of pr…