关键词: UWB；1DCNN；儿童存在探测；Fire 模块

Abstract: Aiming at the high-precision and low-latency requirements of ultra wide band (UWB) radar for child presence detection (CPD) in automotive systems, this paper proposes a child presence detection algorithm based on deep learning one dimensional convolutional neural network (1DCNN). The algorithm introduces the Fire module to optimize computational efficiency and improve detection accuracy. Meanwhile, to address the issues of high near-range Tap noise and correlation between adjacent Taps in UWB signals, specific Tap signals are selected for downsampling processing, and temporal features are incorporated to further enhance the algorithm's accuracy and real-time performance. Experimental results show that the algorithm occupies less than 30 KB of storage space in a low-computational-power, microcontroller unit (MCU) environment, achieving a test accuracy of 99.9%, which is significantly superior to traditional methods. This algorithm has been applied to in-vehicle child presence detection in an automobile and successfully passed the China new car assessment program (C-NCAP) child presence detection scenario benchmark test.

Key words: UWB; 1DCNN; child presence detection; Fire module