The detection of multiple extended targets in complex environments using high-resolution
automotive radar is considered. A data-driven approach is proposed where unlabeled …

Understanding the environment is essential for an autonomous driving system. Most
learning-based perception models leverage data acquired with Lidar or camera sensors …

In this paper, we address the limitations of traditional constant false alarm rate (CFAR) target
detectors in automotive radars, particularly in complex urban environments with multiple …

Recent research explored advantages of applying a learning-based method to the radar
target detection problem. A single point target case was mainly considered, though. This …

Due to the small number of raw data automotive radar datasets and the low resolution of
such radar sensors, automotive radar object detection has been little explored with deep …

This paper presents a single shot detection and classification system in urban automotive
scenarios with a 77 GHz frequency modulated continuous wave radar sensor. The detection …

This paper presents a comprehensive study on radar detection using deep learning with
application to automotive vehicles. Automotive radars face complex target scenarios …

Target detection is a fundamental radar application that is traditionally carried out by
Constant False Alarm Rate (CFAR) detectors. This paper proposes a Convolutional Neural …

This work proposes a Deep Learning based technique for radar target detection to replace
standard radar signal processing techniques. The proposed technique comprises a …

Modern vehicles are equipped with many semi-automated and even highly automated
driving functions, which enormously increase safety and comfort while traveling. A basic …