Help You Locate the Car: A Smartphone-Based Car-Finding System in Underground Parking Lot

While location awareness is common outdoors due to global navigation satellite system (GNSS) systems and devices, pedestrians are back into darkness indoors such as in underground parking lots. We often forget where we parked the car and get confused by such maze-like structure. In order to help drivers find their cars without any additional equipment and map support, we propose a car-finding navigation system that only relies on smartphones. It automatically identifies the user’s drop-off point, records the walking trajectory from car’s location to the exit, and provides fine-grained return navigation to help user back to the car. To address the accuracy and diversity of pedestrian tracking, we propose an inertial sequence learning framework based on outdoor crowdsourced trajectories, which avoids the dedicated efforts on groundtruth collection for model training. We also explore a smartphone posture detection method that supports multiple placements of the smartphone, and a support vector machines (SVM)-based trajectory refinement algorithm with only inertial readings. Besides, we explore a particle filter framework to track and navigate the pedestrian in real time. We have developed a prototype and conducted a series of experiments in multiple underground parking lots, and the results have demonstrated our effectiveness compared with the state-of-the-art.