CarFi: Rider Side Localization using Wi-Fi CSI

With the rise of hailing services, people are increasingly relying on shared mobility (e.g., Uber, Lyft) drivers to pick up for transportation. However, such drivers and riders have difficulties finding each other in urban areas as GPS signals get blocked by skyscrapers, in crowded environments (e.g., in stadiums, airports, and bars), at night, and in bad weather. It wastes their time, creates a bad user experience, and causes more CO
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emissions due to idle driving. In this paper, we explore the potential of Wi-Fi to help drivers to determine the street side of the riders. Our proposed system CarFi uses Wi-Fi CSI from two antennas placed inside a moving vehicle, and leverages signal processing and data-driven techniques for this purpose. After collecting real-world data in realistic and challenging settings by blocking the signal with other people and parked cars, we see that CarFi achieves 95.44% accuracy in rider-side determination in both line-of-sight (LoS) and non-line-of-sight (nLoS) conditions and can be run on an embedded GPU in real-time.