Multi-object detection and multi-object-tracking in diverse driving situations is the main
challenge in autonomous vehicles. Vehicle manufacturers and research organizations are …

Collaborative perception is essential to address occlusion and sensor failure issues in
autonomous driving. In recent years, theoretical and experimental investigations of novel …

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 …

Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly,
remarkable progresses have been achieved. However, the existing methods tend to use …

Modern perception systems of autonomous vehicles are known to be sensitive to occlusions
and lack the capability of long perceiving range. It has been one of the key bottlenecks that …

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in
videos. Most methods obtain identities by associating detection boxes whose scores are …

3D-LiDAR-based cooperative perception has been generating significant interest for its
ability to tackle challenges such as occlusion, sparse point clouds, and out-of-range issues …

Most online multi-object trackers perform object detection stand-alone in a neural net without
any input from tracking. In this paper, we present a new online joint detection and tracking …

Multi-object tracking (MOT) has been notoriously difficult to evaluate. Previous metrics
overemphasize the importance of either detection or association. To address this, we …

False negatives (FN) in 3D object detection, eg, missing predictions of pedestrians, vehicles,
or other obstacles, can lead to potentially dangerous situations in autonomous driving. While …