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 …

The 4D millimeter-Wave (mmWave) radar is a promising technology for vehicle sensing due
to its cost-effectiveness and operability in adverse weather conditions. However, the …

Automotive radar perception is an integral part of automated driving systems. Radar sensors
benefit from their excellent robustness against adverse weather conditions such as snow …

This paper presents novel hybrid architectures that combine grid-and point-based
processing to improve the detection performance and orientation estimation of radar-based …

In autonomous driving systems, LiDAR and radar play important roles in the perception of
the surrounding environment. LiDAR provides accurate 3D spatial sensing information but …

Architectures that first convert point clouds to a grid representation and then apply
convolutional neural networks achieve good performance for radar-based object detection …

The clustering unmanned aerial vehicles (UAVs) positioning is significant for preventing
unauthorized clustering UAVs from causing physical and informational damages. However …

To enable intelligent vehicles and transportation systems, the vehicles and relevant systems
need to have the ability to sense environment and recognize objects. In order to benefit from …

The perception of autonomous vehicles has to be efficient, robust, and cost-effective.
However, cameras are not robust against severe weather conditions, lidar sensors are …