Adam Tonderski — accepted AI-conference papers

2024

NeuRAD: Neural Rendering for Autonomous Driving

Adam Tonderski, Carl Lindström, Georg Hess, William Ljungbergh, Lennart Svensson, Christoffer Petersson

CVPR 2024highlight

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. However existing methods often require long training…

Cited by 61 Source ↗PDF ↗code ↗Scholar ↗

2024

NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving

William Ljungbergh*, Adam Tonderski, Joakim Johnander, Holger Caesar, Kalle Åström, Michael Felsberg +1

ECCV 2024poster

"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 real-world driving sensor data and enables reconfi…

Cited by 24 Source ↗PDF ↗code ↗Scholar ↗

2023

Zenseact Open Dataset: A Large-Scale and Diverse Multimodal Dataset for Autonomous Driving

Mina Alibeigi, William Ljungbergh, Adam Tonderski, Georg Hess, Adam Lilja, Carl Lindström +4

ICCV 2023poster

Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360* perception and temporal reasoning. To address this gap, we introduce ZOD, a large-scale and diverse multimodal dataset collected over two years in various European countries, cove…

Cited by 81 PDF ↗code ↗Scholar ↗