Smartphone-based real-time vehicle tracking in indoor parking structures

Although location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return. In this chapter, we propose VeTrack, a smartphone-only system that tracks the vehicle’s location in real time using the phone’s inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel “shadow” trajectory tracing method to accurately estimate phone’s and vehicle’s orientations despite their arbitrary poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps and turns) recognition methods reliable against noises, disturbances from bumpy rides, and even handheld movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, and collect data with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle’s real-time location with almost negligible latency, with error of 2 ∼ 4 parking spaces at the 80th percentile.