Abstract
Environmental perception is a key technology for Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles, and thus a crucial prerequisite for traffic situation assessment and reasonable decision making. Sensor fusion can make full use of the characteristics of multi-sensors to achieve complementary advantages to improve the target recognition precision in various weather conditions. In this paper, a robust target detection algorithm based on millimeter wave (MMW) radar and camera fusion is proposed. Firstly, the images captured by the camera with low visibility in foggy weather are defogged. Then, the effective target is filtered by MMW radar, and mapped to the camera image to obtain the corresponding regions of interest (ROIs). Finally, the method of weighted is used to combine camera vision network recognition results with the radar target estimation results, and obtain the final result of the ROIs. The simulation results indicates that the detection precision through fusion of MMW radar and camera significantly outperforms that of single sensor.
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