Radiate: A Radar Dataset for Automotive Perception in Bad Weather

Marcel Sheeny, Emanuele de Pellegrin, Saptarshi Mukherjee, Alireza Ahrabian, Sen Wang, Andrew Wallace

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Datasets for autonomous cars are essential for the development and benchmarking of perception systems. However, most existing datasets are captured with camera and LiDAR sensors in good weather conditions. In this paper, we present the RAdar Dataset In Adverse weaThEr (RADIATE), aiming to facilitate research on object detection, tracking and scene understanding using radar sensing for safe autonomous driving. RADIATE includes 3 hours of annotated radar images with more than 200K labelled road actors in total, on average about 4.6 instances per radar image. It covers 8 different categories of actors in a variety of weather conditions (e.g., sun, night, rain, fog and snow) and driving scenarios (e.g., parked, urban, motorway and suburban), representing different levels of challenge. To the best of our knowledge, this is the first public radar dataset which provides high-resolution radar images on public roads with a large amount of road actors labelled. The data collected in adverse weathers, e.g., fog and snowfall, is unique. Some baseline results of radar based object detection and recognition are given to show that the use of radar data is promising for automotive applications in bad weather, where vision and LiDAR fail. RADIATE also has stereo images, 32-channel LiDAR and GPS data, directed at other applications such as sensor fusion, localisation and mapping. The public dataset can be accessed at http://pro.hw.ac.uk/radiate/.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation
PublisherIEEE
Pages5617-5623
Number of pages7
ISBN (Electronic)9781728190778
DOIs
Publication statusPublished - 18 Oct 2021
Event2021 IEEE International Conference on Robotics and Automation - Xi'an, China
Duration: 30 May 20215 Jun 2021

Conference

Conference2021 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2021
Country/TerritoryChina
CityXi'an
Period30/05/215/06/21

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Radiate: A Radar Dataset for Automotive Perception in Bad Weather'. Together they form a unique fingerprint.

Cite this