Vehicle Localization Based On IMU, OBD2, and GNSS Sensor Fusion Using Extended Kalman Filter

Abstract

Multiple systems have been developed to identify drivers’ drowsiness. Among all, the vehicle-based driver drowsiness detection system relies on lane lines to determine the lateral position of the vehicle for drowsiness detection. However, the lane lines may fade out, affecting its reliability. To resolve this issue, a vehicle localization algorithm based on the Inertial Measurement Unit (IMU), Global Navigation Satellite System (GNSS), and Onboard Diagnostics (OBD2) sensors is introduced. Initially, the kinematic bicycle model estimates the vehicle motion by using inputs from the OBD2 and IMU. Subsequently, the GNSS measurement is used to update the vehicle motion by applying the extended Kalman filter. To evaluate the algorithm’s performance, the tests were conducted at the residential area in Bukit Beruang, Melaka and Multimedia University Melaka Campus. The results showed that the proposed technique achieved a total root-mean-square error of 3.892 m. The extended Kalman filter also successfully reduced the drift error by 40 – 60%. Nevertheless, the extended Kalman filter suffers from the linearization error. It is recommended to employ the error-state extended Kalman filter to minimize the error. Besides, the kinematic bicycle model only generates accurate predictions at low vehicle speeds due to the assumption of zero tire slip angles. The dynamic bicycle model can be utilized to handle high-speed driving scenarios. It is also advised to integrate the LiDAR sensor since it offers supplementary position measurements, particularly in GNSS-denied environments. Lastly, the proposed technique is expected to enhance the reliability of the vehicle-based system and reduce the risk of accidents.

Keywords

• extended kalman filter
• kinematic bicycle model
• vehicle localization