In recent years, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
Modern autonomous driving system is characterized as modular tasks in sequential order, ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
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 …
In this research, we propose a new 3D object detector with a trustworthy depth estimation, dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work …
Multi-sensor fusion is essential for an accurate and reliable autonomous driving system. Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with …
Abstract 3D object detectors usually rely on hand-crafted proxies, eg, anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be …
Modern methods for vision-centric autonomous driving perception widely adopt the bird's- eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than …
Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision. We propose OpenScene, an alternative approach …
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 …