Environment perception using camera, radar, and/or lidar sensors has significantly improved
in the last few years because of deep learning-based methods. However, a large group of …

Research on localization and perception for Autonomous Driving (AD) is mainly focused on
camera and LiDAR data sets, rarely on radar data. Transferring datadriven development …

The representation of objects in LiDAR point clouds is changed as the height of the
mounting position of sensor devices gets increased. Most of the available open datasets for …

High quality perception is essential for autonomous driving (AD) systems. To reach the
accuracy and robustness thatare required by such systems, several types of sensors must …

Automatic annotation of 3D objects in cluttered scenes shows its great importance to a
variety of applications. Nowadays, 3D point clouds, a new 3D representation of real-world …

LiDAR (Light Detection And Ranging) is an important and widely adopted sensor for
autonomous vehicles, particularly for those vehicles operating at higher levels (L4-L5) of …

Radar sensors employed for environment perception, eg in autonomous vehicles, output a
lot of unwanted clutter. These points, for which no corresponding real objects exist, are a …

Automotive radars operating in dense urban environments experience a multipath
propagation phenomenon, degrading radar performance, and challenging conventional …

As a part of the perception results of intelligent driving systems, static object detection (SOD)
in 3D space provides crucial cues for driving environment understanding. With the rapid …

A growing interest in technologies for autonomous driving emphasizes the demand for safe
and reliable perception systems in various driving conditions. The current state-of-the-art …