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

Sriranga, Ashwini Kanakapura; Lu, Qian; Birrell, Stewart A.

doi:10.1109/ssci51031.2022.10022146

Cited by 2 publications

(1 citation statement)

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“…Furthermore, this technology can also be used to realize the functions of automatic seat adjustment and automatic air conditioning temperature adjustment, improving the comfort of passengers. With the continuous development of autonomous driving technology, it will play a more important role in the field of automotive safety [ 1 ]. By working with other vehicle systems, it can more accurately estimate the status and needs of passengers, thereby achieving a more intelligent and personalized driving experience.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Occupant Detection Based on MM-Wave Radar

Li,

Wang,

Wang

2024

Sensors

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With the continuous development of automotive intelligence, vehicle occupant detection technology has received increasing attention. Despite various types of research in this field, a simple, reliable, and highly private detection method is lacking. This paper proposes a method for vehicle occupant detection using millimeter-wave radar. Specifically, the paper outlines the system design for vehicle occupant detection using millimeter-wave radar. By collecting the raw signals of FMCW radar and applying Range-FFT and DoA estimation algorithms, a range–azimuth heatmap was generated, visually depicting the current status of people inside the vehicle. Furthermore, utilizing the collected range–azimuth heatmap of passengers, this paper integrates the Faster R-CNN deep learning networks with radar signal processing to identify passenger information. Finally, to test the performance of the detection method proposed in this article, an experimental verification was conducted in a car and the results were compared with those of traditional machine learning algorithms. The findings indicated that the method employed in this experiment achieves higher accuracy, reaching approximately 99%.

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Section: Introductionmentioning

confidence: 99%