3 accepted papers
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 such testing in a cost-effective and scalable way. Neural rendering methods have gained popularity, as they can build si…
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…
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…