Autonomous driving has recently gained lots of attention due to its disruptive potential and
impact on the global economy; however, these high expectations are hindered by strict …

Understanding the scene around the ego-vehicle is key to assisted and autonomous driving.
Nowadays, this is mostly conducted using cameras and laser scanners, despite their …

Occupancy grid mapping is an important component in road scene understanding for
autonomous driving. It encapsulates information of the drivable area, road obstacles and …

Camera and Lidar processing have been revolutionized with the rapid development of deep
learning model architectures. Automotive radar is one of the crucial elements of automated …

With the rapid development of autonomous driving technology, radar sensors play a vital
role in the perception system due to their robustness under harsh environmental conditions …

Detecting obstacles is crucial for safe and efficient autonomous driving. To this end, we
present NVRadarNet, a deep neural network (DNN) that detects dynamic obstacles and …

The high performance of RGB-D based road segmentation methods contrasts with their rare
application in commercial autonomous driving, which is owing to two reasons: 1) the prior …

This paper presents an efficient annotation procedure and an application thereof to end-to-
end, rich semantic segmentation of the sensed environment using Frequency-Modulated …

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 …

Current automotive radars output sparse point clouds with very low angular resolution. Such
output lacks semantic information of the environment and has prevented radars from …