Li Wei, Wang Wenxu, Wang Hongzhi
Collage of Information, North China University of Technology, Beijing 100144, China.
Sensors (Basel). 2024 May 23;24(11):3334. doi: 10.3390/s24113334.
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%.
随着汽车智能化的不断发展，车辆乘员检测技术受到越来越多的关注。尽管该领域有各类研究，但仍缺乏一种简单、可靠且高度私密的检测方法。本文提出一种使用毫米波雷达进行车辆乘员检测的方法。具体而言，本文概述了使用毫米波雷达进行车辆乘员检测的系统设计。通过收集调频连续波（FMCW）雷达的原始信号并应用距离快速傅里叶变换（Range-FFT）和到达角（DoA）估计算法，生成了距离-方位热图，直观地描绘了车内人员的当前状态。此外，利用收集到的乘客距离-方位热图，本文将更快区域卷积神经网络（Faster R-CNN）深度学习网络与雷达信号处理相结合，以识别乘客信息。最后，为测试本文提出的检测方法的性能，在一辆汽车中进行了实验验证，并将结果与传统机器学习算法的结果进行了比较。结果表明，本实验采用的方法具有更高的准确率，达到了约99%。
