In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …

Efficient data utilization is crucial for advancing 3D scene understanding in autonomous
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …

In the realm of autonomous driving, robust perception under out-of-distribution conditions is
paramount for the safe deployment of vehicles. Challenges such as adverse weather …

A unified and versatile LiDAR segmentation model with strong robustness and
generalizability is desirable for safe autonomous driving perception. This work presents …

With the rapid advances in autonomous driving, it becomes critical to equip its sensing
system with more holistic 3D perception. However, widely explored tasks like 3D detection …

Safety-critical 3D scene understanding tasks necessitate not only accurate but also
confident predictions from 3D perception models. This study introduces Calib3D, a …

In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is
crucial for understanding complex 3D environments. Traditional approaches often rely on …

Deep learning-based visual perception models lack robustness when faced with camera
motion perturbations in practice. The current certification process for assessing robustness is …

The robustness of driving perception systems under unprecedented conditions is crucial for
safety-critical usages. Latest advancements have prompted increasing interests towards …

Recent advancements in bird's eye view (BEV) representations have shown remarkable
promise for in-vehicle 3D perception. However, while these methods have achieved …