Interpretable In-Cabin Occupancy Detection Using FHRS-Based Residual Features from Fixed-ROI UWB Radar Signals

Abstract

Vehicle in-cabin occupancy detection supports restraint-system control, child presence detection, and intelligent cabin sensing, with child presence detection linked to safety assessment frameworks such as the European New Car Assessment Program. High-dimensional radar representations show strong potential, but their physical basis can be difficult to interpret. Engineered-feature approaches improve interpretability, yet few explicitly separate the linear-delay component from the residual structure of the complex reflection response. This study proposes an interpretable representation derived from the complex reflection ratio within a fixed time-domain region of interest (ROI). The method extracts direct-delay cues, frequency-domain Hilbert residual signature (FHRS) scalar features, and continuous wavelet transform (CWT)-based residual-delay and log-magnitude-derivative shape features. In the original experiment, the residual-centered condition achieved 97.72% accuracy and a 97.73% macro F1-score, whereas the direct-delay-only condition achieved 100.00%. A separately acquired 20-trial challenge experiment used Empty Seat, Person, and Human-like Object-loaded Seat conditions, with a human-shaped test object used for the latter. In this challenge, the direct-delay-only, residual-centered, and all-feature conditions achieved accuracies of 79.44%, 96.92%, and 97.39%, respectively. These results demonstrate that residual features provide nonredundant discriminative information when direct-delay cues alone are insufficient. Participant-independent, cross-vehicle, and cross-installation generalization remain to be verified.