67 citations · 77 across the 5 of their papers we have counts for
7 papers
Raw High-Definition Radar for Multi-Task Learning
Julien Rebut, Arthur Ouaknine, Waqas Malik +1
With their robustness to adverse weather conditions and ability to measure speeds, radar sensors have been part of the automotive landscape for more than two decades. Recent progre…
StyleLess layer: Improving robustness for real-world driving
Julien Rebut, Andrei Bursuc, Patrick Pérez
Deep Neural Networks (DNNs) are a critical component for self-driving vehicles. They achieve impressive performance by reaping information from high amounts of labeled data. Yet, t…
PolarNet: Accelerated Deep Open Space Segmentation Using Automotive Radar in Polar Domain
Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor +5
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 d…
Multi-View Radar Semantic Segmentation
Arthur Ouaknine, Alasdair Newson, Patrick Pérez +2
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 reduced…
CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
A. Ouaknine, A. Newson, J. Rebut +2
High quality perception is essential for autonomous driving (AD) systems. To reach the accuracy and robustness that are required by such systems, several types of sensors must be c…
Deep Open Space Segmentation using Automotive Radar
Farzan Erlik Nowruzi, Dhanvin Kolhatkar, Prince Kapoor +5
In this work, we propose the use of radar with advanced deep segmentation models to identify open space in parking scenarios. A publically available dataset of radar observations c…
How much real data do we actually need: Analyzing object detection performance using synthetic and real data
Farzan Erlik Nowruzi, Prince Kapoor, Dhanvin Kolhatkar +3
In recent years, deep learning models have resulted in a huge amount of progress in various areas, including computer vision. By nature, the supervised training of deep models requ…