Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …

Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending
and drawing extensive attention both from industry and academia. Conventional …

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace
due to the convenience, safety, and economic benefits. Although a number of surveys have …

Autonomous driving faces great safety challenges for a lack of global perspective and the
limitation of long-range perception capabilities. It has been widely agreed that vehicle …

Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …

Abstract We present Voxel Transformer (VoTr), a novel and effective voxel-based
Transformer backbone for 3D object detection from point clouds. Conventional 3D …

We introduce Argoverse 2 (AV2)-a collection of three datasets for perception and forecasting
research in the self-driving domain. The annotated Sensor Dataset contains 1,000 …

Abstract Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled
text-image pairs, has demonstrated great performance in open-world vision understanding …

Abstract 3D object detection is an important task in autonomous driving to perceive the
surroundings. Despite the excellent performance, the existing 3D detectors lack the …

Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of
surrounding traffic participants can significantly improve decision-making and safety in …