DeepCPD: Deep Learning-Based In-Car Child Presence Detection Using WiFi

Abstract

Child presence detection (CPD) is a vital technology for vehicles to prevent heat-related fatalities or injuries by detecting the presence of a child left unattended. Regulatory agencies around the world are planning to mandate CPD systems in the near future. However, existing solutions have limitations in terms of accuracy, coverage, and additional device requirements. While WiFi-based solutions can overcome the limitations, existing approaches struggle to reliably distinguish between adult and child presence, leading to frequent false alarms, and are often sensitive to environmental variations. In this article, we present DeepCPD, a novel deep learning framework designed for accurate CPD in smart vehicles. DeepCPD utilizes an environment-independent feature—the autocorrelation function derived from WiFi channel state information—to capture human-related signatures while mitigating environmental distortions. A Transformer-based architecture, followed by a multilayer perceptron, is employed to differentiate adults from children by modeling motion patterns and subtle body size differences. To address the limited availability of in-vehicle child and adult data, we introduce a two-stage learning strategy that significantly enhances model generalization. Extensive experiments conducted across more than 30 car models and over 500 h of data collection demonstrate that DeepCPD achieves an overall accuracy of 92.86%, outperforming a convolutional neural network (CNN) baseline by a substantial margin (79.55% ). In addition, the model attains a 91.45% detection rate for children while maintaining a low false alarm rate of 6.14% .