Decision fusion of two sensors object classification based on the evidential reasoning rule

Abstract

The application of multiple sensors for object classification is commonly used in unmanned intelligent systems such as autonomous driving and robots. Current researches based on evidential reasoning rules mainly consider multiple sensor fusion. The reliability is determined by the decisions of the majority of sensors and the weight is determined by historical accuracy. However, the existing researches using the evidential reasoning rule are not applicable when there are only two sensors working in some situations. This paper proposes a two sensors decision fusion method for object classification based on the evidential reasoning rule. The reliability of sensors is calculated with the Logistic model based on the differences of the classification decisions in a certain time span and the adaptive weights of sensors are calculated based on the coefficient of variation to adapt to different environments. The final decision is drawn out by fusing the classification decisions of two sensors based on the fusion rules of evidential reasoning rule. Comparative experimental studies of object classification decision fusion in autonomous driving are carried out with the Nuscenes dataset and the Waymo dataset. The results show that the method proposed can effectively improve the accuracy of the fusion when there are only two sensors.