In this paper it is demonstrated how 3D object detection can be achieved using deep
learning on radar pointclouds and camera images. A deep convolutional neural network is …

We present a radar-centric automotive dataset based on radar, lidar and camera data for the
purpose of 3D object detection. Our main focus is to provide high resolution radar data to the …

For many automated driving functions, a highly accurate perception of the vehicle
environment is a crucial prerequisite. Modern high-resolution radar sensors generate …

3D object detection based on LiDAR point clouds is a crucial module in autonomous driving
particularly for long range sensing. Most of the research is focused on achieving higher …

Understanding driving situations regardless the conditions of the traffic scene is a
cornerstone on the path towards autonomous vehicles; however, despite common sensor …

A crucial part of safe navigation of autonomous vehicles is the robust detection of
surrounding objects. While there are numerous approaches covering object detection in …

Despite its advantages as an inexpensive, weather-robust and long-range sensor which
additionally provides velocity information, radar sensors still lead a shadowy existence …

LiDAR-based 3D object detection for autonomous driving has recently drawn the attention of
both academia and industry since it relies upon a sensor that incorporates appealing …

Recently, 3D object detection based on deep learning has achieved impressive
performance in complex indoor and outdoor scenes. Among the methods, the two-stage …

We propose a new method for fusing LIDAR point cloud and camera-captured images in
deep convolutional neural networks (CNN). The proposed method constructs a new layer …