Vehicle Position Estimation Using Vehicle Dynamic Model

Abstract Accurate vehicle positioning information is essential reliable operation of advanced driver assistance systems (ADAS). Conventionally, fusing information by attaching an inertial measurement unit (IMU) to the outside of the vehicle with GPS is widely used for this purpose. However, because a linear tire model is used as the dynamic model for positioning estimation, most methods have low vehicle positioning accuracy during high speed driving. This paper proposes a position estimation algorithm that rectifies this problem. The proposed algorithm is equivalent to a high-precision DGPS but fuses information from the inexpensive GPS in the vehicle and vehicle status information acquired from modules such as electronic stability control (ESC) and electric power steering (EPS) via controller area network (CAN) communication and extended Kalman filter (EKF) without a special linearization process.