An Approach to Recognize Vehicles Context Flow for Smartphone-Based Outdoor Parking Using Supervised Machine Learning Classifiers

Abstract

Abstract— Finding an available parking space in outdoor environments such as university campuses and roadsides need a good parking system. In a general parking system, detecting a vehicle entering leaving the parking premise is one of the major steps. Currently, there are parking systems that use cameras or external sensors to detect the leaving and entering of the automobiles. External sensors-based systems require a costly sensor installation at each parking slot while the camera-based systems require sophisticated camera setup. Both parking systems need very high cost of deployment and maintenance. Besides, the additional need for network setup and hardware capacity increases the complexity that makes the system difficult to be implemented in an outdoor environment that typically involve a bigger coverage area. To encounter the issues, paper presents a parking system for outdoor parking systems using only smartphone-oriented sensors. The proposed approach does not require additional sensors installation nor manpower support. The proposed system takes the inputs from smartphones to detect the driver’s context that is used to recognize the flow of the vehicle. Context flow recognition indicates whether a driver is parking or unparking his/her vehicle. Supervised classifiers like support vector machines (SMV) and decision trees (DT) are used to recognize the parking or unparking actions to enable vehicles tracking in the parking area. Outcome of the proposed approach is a significant contribution for outdoor parking system as it solely utilizes the sensors embedded in smartphones to detect parking behaviors.