Improved Shape-Based Distance Method for Correlation Analysis of Multi-Radar Data Fusion in Self-Driving Vehicle

Abstract

Multi-radar data fusion techniques are important for self-driving vehicles to better perceive the environment. In the process of obtaining targets location and constructing environmental models, multi-radar data fusion can be regarded as a homogeneous time-series data fusion problem which is commonly solved by weighted fusion methods. However, when most sensors data are biased, especially when the data are all larger or smaller than the accurate data at the same time, the weights of sensors defined by sensor consistency and sensor stability can not reflect the real reliability of sensors. Thus, in this study, the correlation change between different observed parameters of one sensor, which is calculated based on the shape-based distance, together with the sensor consistency and sensor stability is leveraged to judge the reliability of the sensors and calculate the weights of the sensors. An experimental study is carried out and the Nuscenes dataset is used where a multi-radar system consists one LIDAR and two millimeter wave radars. In the situation that most sensors data are biased, the relative error and the root-mean-square error of the method proposed in this study are significantly lower than that of the arithmetic averaging method, the trimmed mean method and the weighted fusion method which only considers the sensor consistency and stability. Besides, under other situations, the fusion accuracy is also improved.