With recent developments, the performance of automotive radar has improved significantly.
The next generation of 4D radar can achieve imaging capability in the form of high …

Driven by deep learning techniques, perception technology in autonomous driving has
developed rapidly in recent years, enabling vehicles to accurately detect and interpret …

Camera and radar sensors have significant advantages in cost, reliability, and maintenance
compared to LiDAR. Existing fusion methods often fuse the outputs of single modalities at …

Radar is a key component of the suite of perception sensors used for safe and reliable
navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity …

We present a method for inferring dense depth from a camera image and a sparse noisy
radar point cloud. We first describe the mechanics behind mmWave radar point cloud …

Multi-view radar-camera fused 3D object detection provides a farther detection range and
more helpful features for autonomous driving, especially under adverse weather. The …

As a direct depth sensor, radar holds promise as a tool to improve monocular 3D object
detection, which suffers from depth errors, due in part to the depth-scale ambiguity. On the …

Remote physiology, which involves monitoring vital signs without the need for physical
contact, has great potential for various applications. Current remote physiology methods rely …

With the rapid advancements of sensor technology and deep learning, autonomous driving
systems are providing safe and efficient access to intelligent vehicles as well as intelligent …

This paper explores a machine learning approach on data from a single-chip mmWave
radar for generating high resolution point clouds–a key sensing primitive for robotic …