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

The most advanced text-to-image (T2I) models require significant training costs (eg, millions
of GPU hours), seriously hindering the fundamental innovation for the AIGC community …

Vision-based perception for autonomous driving requires an explicit modeling of a 3D space
where 2D latent representations are mapped and subsequent 3D operators are applied …

Integrating LiDAR and camera information into Bird's-Eye-View (BEV) representation has
emerged as a crucial aspect of 3D object detection in autonomous driving. However …

The reliability of driving perception systems under unprecedented conditions is crucial for
practical usage. Latest advancements have prompted increasing interest in multi-LiDAR …

Abstract Online vectorized High-Definition (HD) map construction is critical for downstream
prediction and planning. Recent efforts have built strong baselines for this task, however …

In the current state of 3D object detection research, the severe scarcity of annotated 3D data,
substantial disparities across different data modalities, and the absence of a unified …

In recent years, with the continuous development of autonomous driving technology, 3D
object detection has naturally become a key focus in the research of perception systems for …

Multi-sensor object detection is an active research topic in automated driving, but the
robustness of such detection models against missing sensor input (modality missing), eg …

3D perception is a critical problem in autonomous driving. Recently, the Bird's-Eye-View
(BEV) approach has attracted extensive attention, due to low-cost deployment and desirable …