ParkZoom: A parking spot identification system

Abstract

Accurate localization in outdoor and indoor spaces is a challenging task. The widely used GPS is not designed for high accuracy applications and yields accuracy levels not sufficient for lane or spot level localization. In addition, errors from inertial sensors accumulate with time due to integration drift. We introduce a smartphone based, infrastructure aided parking localization system called ParkZoom for estimating (zooming into) the precise parking spot location of a vehicle during traversal in both indoor and outdoor parking lots. On the vehicle side, the proposed method utilizes conventional smartphones for generating and transferring continuous sensor data, such as accelerometer, gyroscope, and compass readings. On the infrastructure side, ParkZoom employs statistical learning of sensor data signatures, pattern classification of data, constraint propagation and error correction for accurate parking spot identification. The paper presents experimental results with the ParkZoom algorithm obtained on real data in city driving and two parking areas.