The ability to reliably perceive the environmental states, particularly the existence of objects
and their motion behavior, is crucial for autonomous driving. In this work, we propose an …

In this paper, we present BEVerse, a unified framework for 3D perception and prediction
based on multi-camera systems. Unlike existing studies focusing on the improvement of …

Vision-centric autonomous driving has recently raised wide attention due to its lower cost.
Pre-training is essential for extracting a universal representation. However current vision …

Abstract 3D visual perception tasks, including 3D detection and map segmentation based on
multi-camera images, are essential for autonomous driving systems. In this work, we present …

Integrating LiDAR and Camera information into Bird's-Eye-View (BEV) has become an
essential topic for 3D object detection in autonomous driving. Existing methods mostly adopt …

In recent years, vision-centric perception has flourished in various autonomous driving tasks,
including 3D detection, semantic map construction, motion forecasting, and depth …

Current perception models in autonomous driving have become notorious for greatly relying
on a mass of annotated data to cover unseen cases and address the long-tail problem. On …

Abstract 3D scene understanding plays a vital role in vision-based autonomous driving.
While most existing methods focus on 3D object detection, they have difficulty describing …

Existing trajectory prediction methods for autonomous driving typically rely on one-stage
trajectory prediction models which condition future trajectories on observed trajectories …

Perception and prediction are two separate modules in the existing autonomous driving
systems. They interact with each other via hand-picked features such as agent bounding …