Shallow Neural Networks for mmWave Radar Based Recognition of Vulnerable Road Users

Md Robiul Islam Minto, Bo Tan, Sara Sharifzadeh, Taneli Riihonen, Mikko Valkama

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Abstract

A millimetre-wave (mmWave) radar, having synergies with the multi-beam light detection and ranging (LiDAR) and cameras, has been considered as a must-have sensor in the connected and autonomous vehicles (CAV) in the future intelligent transportation systems (ITS). Besides the traditional target detection and ranging functions, the mmWave radar is expected to perform more intelligent tasks to improve the road safety, for example recognising the targets, especially the vulnerable road users like pedestrians and cyclists. This paper describes a simulation study of the micro-Doppler signatures of the pedestrian and cyclist based on mmWave vehicle radar and investigates the recognition capabilities through both the convolutional neural networks (CNN), recurrent neural networks (RNN) and mixed convolutional and recurrent approach respectively. The result demonstrates the usability of the mmWave radar Doppler information and complementary with the video and laser data streams in the CAV auto-piloting.

Original languageEnglish
Title of host publication2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728167435
DOIs
Publication statusPublished - 20 Jul 2020
Event12th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2020 - Porto, Portugal
Duration: 20 Jul 202022 Jul 2020

Publication series

Name2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2020

Conference

Conference12th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2020
CountryPortugal
CityPorto
Period20/07/2022/07/20

Bibliographical note

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

  • automated vehicles
  • CNN
  • LSTM
  • micro Doppler
  • mmWave
  • object recognition
  • radar
  • RNN

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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