  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 CO2 emissions due to idle
driving. In this work, we explore the potential of Wi-Fi to help drivers to
determine the street side of the riders. Our proposed system is called CarFi
that uses Wi-Fi CSI from two antennas placed inside a moving vehicle and a
data-driven technique to determine the street side of the rider. By collecting
real-world data in realistic and challenging settings by blocking the signal
with other people and other parked cars, we see that CarFi is 95.44% accurate
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.
