Minimizing GPS Dependency for a Vehicle’s Trajectory Identification by Using Data from Smartphone Inertial Sensors and Onboard Diagnostics Device

Abstract

For the past few years, several studies have focused on identifying a vehicle’s trajectory with smartphone data. However, these studies predominantly used GPS coordinate information for that purpose. Considering the known limitations of GPS, such as connectivity issues at urban canyons and underpasses, low precision of localization, and high power consumption of smartphones while GPS is in use, this paper focuses on developing alternative methods for identifying a vehicle’s trajectory at an intersection with sensor data other than GPS to minimize GPS dependency. In particular, accelerometer and gyroscope data collected with smartphone inertial sensors and speed data collected with an onboard diagnostics device are used to develop algorithms for maneuver (i.e., left and right turns and through) and trip direction identification at an intersection. In addition, techniques for noise removal and orientation correction from raw inertial sensor data are described. The effectiveness of the method for trajectory identification is assessed with collected field data. Results demonstrate that the developed method is effective in identifying a vehicle’s trajectory at an intersection. Overall, this research shows the feasibility of using alternative sensor data for trajectory identification and thus eliminating the need for continuous GPS connectivity.