CarOSense

Abstract

Ultra-Wideband (UWB) is a popular technology to provide high accuracy localization, asset tracking and access control applications. Due to the accurate ranging feature and robustness to relay attacks, car manufacturers are upgrading the keyless entry infrastructure to UWB. As car occupancy monitoring is an essential step to support regulatory requirements and provide customized user experience, we build CarOSense to explore the possibility of reusing UWB keyless infrastructure as an orthogonal sensing modality to detect per-seat car occupancy. CarOSense uses a novel deep learning model, MaskMIMO, to learn spatial/time features by 2D convolutions and per-seat attentions by a multi-task mask. We collect UWB data from 10 car locations with up to 16 occupancy states in each location. We implement CarOSense as a cross-platform demo and evaluate it in 15 different scenarios, including leave-one-out test of unknown car locations and stress test of unseen scenarios. Results show that the average accuracy is 94.6% for leave-one-out test and 87.0% for stress test. CarOSense is robust in a large set of untrained scenarios with the model trained on a small set of training data. We also benchmark the computation cost and demonstrate that CarOSense is lightweight and can run smoothly in real-time on embedded devices.