Novel Radar based In-Vehicle Occupant Detection Using Convolutional Neural Networks

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

48 Downloads (Pure)


Vehicle Occupant Detection has gathered attention with the advancement of Connected Automated Vehicles (CAVs) since it enhances vehicular safety features and contributes to Vehicle-to-Everything (V2X) communication features. In this paper, a novel Frequency Modulated Continuous Wave (FMCW) radar-based occupancy detection utilizing Convolutional Neural Networks (CNN) is introduced. The proposed methodology tackles disadvantages posed by visual and sensor-based methods when privacy, computational complexity, line-of-sight requirements, and robustness are concerned. The system uses time-domain raw radar data signals to form visual heatmaps based on signal intensity variation caused by presence of a target. The heatmaps developed for each data frame acts as an input to the neural network. Visually generated signal based heatmaps differentiate three classes of vehicle occupancy: vacant, driver seat and rear passenger occupancy. The adapted CNN architecture is an implementation of transfer learning where a version of the VGG-16 pretrained model consisting of 16 convolutional layers is used. A validation accuracy of 96.88% is achieved with a dataset containing 1000 heatmap images for each class. The results conclude that radar generated time domain heatmaps efficiently detect vehicle occupancy employing transfer learning even with smaller datasets.
Original languageEnglish
Title of host publication2022 IEEE Symposium Series on Computational Intelligence (SSCI)
EditorsHisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett
Number of pages6
ISBN (Electronic)978-1-6654-8768-9
ISBN (Print)978-1-6654-8769-6
Publication statusPublished - 30 Jan 2023
Event2022 IEEE Symposium Series on Computational Intelligence (SSCI) - , Singapore
Duration: 4 Dec 20227 Dec 2022


Conference2022 IEEE Symposium Series on Computational Intelligence (SSCI)

Bibliographical note

© 2023 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.

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.


  • FMCW
  • CNN
  • Radar
  • Transfer Learning
  • Classification
  • Vehicle safety
  • Vehicle occupancy


Dive into the research topics of 'Novel Radar based In-Vehicle Occupant Detection Using Convolutional Neural Networks'. Together they form a unique fingerprint.

Cite this