Road Slope Aided Vehicle Position Estimation System Based on Sensor Fusion of GPS and Automotive Onboard Sensors

This paper proposes a road slope aided position estimation algorithm based on the fusion of GPS data with information from automotive onboard sensors. Many previous studies for position estimation did not consider the effect of road slope, although many sloped roads are existing. In order to analyze the influence of road slope on position estimation, theoretical proof and simulation are performed. Based on this analysis, a road slope aided position estimation algorithm is presented, which includes a vehicle motion model that can compensate for the effect of the road slope. This algorithm can estimate the position and road slope simultaneously. Furthermore, by compensating for the error due to the road slope, the algorithm can improve the position estimation accuracy and reliability. The estimation algorithm in this paper is implemented and evaluated using automotive onboard sensors and embedded system; therefore, additional motion sensors and high-performance computational units are not necessary. The experimental results show that the accuracy and reliability of the road slope aided position algorithm provide superior performance compared with a planar vehicle model-based position estimation algorithm in mountainous terrain.