Identification Algorithm of Longitudinal Road Slope Based on Multi-sensor Data Fusion Filtering

doi:10.3901/JME.2018.14.116

Abstract

Abstract: The longitudinal road slope is the key parameter of the control strategy of vehicle power transmission electronic control system and the chassis active safety electric control system. Most previous researches on road slope identification mainly assume that the road slope is constant, and the longitudinal road slope is estimated by using a single sensor information. However, the complexities of road and driving conditions can easily lead to distortion or even error of the sensors information. It is difficult to ensure the identification accuracy of road slope by using a single road slope model and sensor information. Two road slope models including the fixed road slope model and fixed road slope rate model are established, and a real-time identification approach of longitudinal road slope based on multi-sensor data fusion filtering algorithm is applied to estimate the road slope through the weighted fusion of two measurements including the vehicle longitudinal dynamics and acceleration sensor information. The field test based on the driving simulator is carried out. The results show that the proposed algorithm can effectively estimate the complex road slope in real time with high identification accuracy, which can be applied to many kinds of real-time vehicle electric control systems.

Key words: driving simulator, Kalman filtering, multi-sensor data fusion, road slope identification

CLC Number:

U461

YONG Wenliang, GUAN Hsin, WANG Bo, LU Pingping. Identification Algorithm of Longitudinal Road Slope Based on Multi-sensor Data Fusion Filtering[J]. Journal of Mechanical Engineering, 2018, 54(14): 116-124.

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