With the rapid development of automated vehicles (AVs), more and more demands are
proposed towards environmental perception. Among the commonly used sensors, MMW …

The deep learning technique has proven to be effective in the classification and localization
of objects on the image or ground plane over time. The strength of the technique's features …

Environmental perception with the multi-modal fusion is crucial in autonomous driving to
increase accuracy, completeness, and robustness. This paper focuses on utilizing millimeter …

This article provides an elaborate overview of current research in extended object tracking.
We provide a clear definition of the extended object tracking problem and discuss its …

Real-time multi-object tracking (MOT) is a complex task involving detecting and tracking
multiple objects. After the objects are detected, they are assigned markers, and their …

Autonomous perception requires high-quality environment sensing in the form of 3D
bounding boxes of dynamic objects. The primary sensors used in automotive systems are …

Object tracking is a fundamental computer vision problem that refers to a set of methods
proposed to precisely track the motion trajectory of an object in a video. Multiple Object …

This tutorial introduces state-of-the-art methods for tracking multiple spatially extended
objects based on unlabeled noisy point clouds, eg, from radar or lidar sensors. In the first …

In the context of autonomous driving environment perception, multi-modal fusion plays a
pivotal role in enhancing robustness, completeness, and accuracy, thereby extending the …

High-resolution radar sensors are able to resolve multiple detections per object and,
therefore, provide valuable information for vehicle environment perception. For instance …