Search Results for author:
Found 3 papers, 2 papers with code
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection
In recent years, driven by the need for safer and more autonomous transport systems, the automotive industry has shifted toward integrating a growing number of Advanced Driver Assistance Systems (ADAS).
A recurrent CNN for online object detection on raw radar frames
Exploiting the time information (e. g., multiple frames) has been shown to help to capture better the dynamics of objects and, therefore, the variation in the shape of objects.
DAROD: A Deep Automotive Radar Object Detector on Range-Doppler maps
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 learning models in comparison to camera and lidar-based approaches.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
