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A sensor fusion method of radar and camera

In autonomous vehicles, vehicle perception is very important for vehicle behavior planning and execution. In order to solve the problem that it is difficult to use a single sensor in autonomous driving to cope with the complex driving environment, and in the traditional method of lidar and camera fusion, lidar is affected by the weather environment, resulting in serious signal attenuation, short laser detection distance, high price and large amount of data calculation. In this paper, a method for fusion of radar and camera is proposed. In this method, two radars are selected to detect the object and obtain the speed, distance, angle and other information of the object. In terms of vision, the object category is obtained through yolov3 algorithm, and the object information detected by the radar is mapped to the image after semantic segmentation through spatial coordinate transformation, finally, the sensor joint detection model is established by k-nearest neighbor method, and the fused target detection results are obtained. Compared with the fusion method of lidar and camera, this method has the advantages of low cost, small amount of data calculation, strong anti-jamming ability, and can meet the requirements of real-time and accuracy in performance.