General world models represent a crucial pathway toward achieving Artificial General
Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual …

Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor
savings. Over the past few years, the number of simulators for autonomous driving has …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

We present DrivingGaussian an efficient and effective framework for surrounding dynamic
autonomous driving scenes. For complex scenes with moving objects we first sequentially …

This paper aims to tackle the problem of modeling dynamic urban streets for autonomous
driving scenes. Recent methods extend NeRF by incorporating tracked vehicle poses to …

Scene simulation in autonomous driving has gained significant attention because of its huge
potential for generating customized data. However existing editable scene simulation …

Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD)
community. Recent methods show NeRFs' potential for closed-loop simulation enabling …

The field of autonomous driving increasingly demands high-quality annotated training data.
In this paper we propose Panacea an innovative approach to generate panoramic and …

Nowadays, autonomous cars can drive smoothly in ordinary cases, and it is widely
recognized that realistic sensor simulation will play a critical role in solving remaining corner …

In the context of autonomous driving the significance of effective feature learning is widely
acknowledged. While conventional 3D self-supervised pre-training methods have shown …