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.
|Title of host publication||2022 IEEE Symposium Series on Computational Intelligence (SSCI)|
|Editors||Hisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, Keeley Crockett|
|Number of pages||6|
|Publication status||Published - 30 Jan 2023|
|Event||2022 IEEE Symposium Series on Computational Intelligence (SSCI) - , Singapore|
Duration: 4 Dec 2022 → 7 Dec 2022
|Conference||2022 IEEE Symposium Series on Computational Intelligence (SSCI)|
|Period||4/12/22 → 7/12/22|
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- Transfer Learning
- Vehicle safety
- Vehicle occupancy