Front vehicle detection based on multi-sensor information fusion

In order to improve the ability of Advanced Driving Assisted System (ADAS) to perceive vehicles in the road environment, the information fusion algorithm of machine vision and millimeter wave radar was proposed to detect front vehicles in this paper. Firstly, the camera and millimeter wave radar were jointly calibrated to determine their conversion formula using the coordinate measuring machine in the fusion system. The candidate frame of SSD for deep learning algorithm was optimized to improve the speed of vehicle detection, while long focus camera and short focus camera were selected for two front images acquisition, the overlapped images were fused to improve sharpness of small target image ahead. The appropriate threshold parameters of radar data were determined by radar simulator and the effective vehicle target was extracted. According to these effective target data, the image collected by the camera was selected and the region of interest was established. Vehicles in the selection region were detected with the improved SSD algorithm. In the test, the vehicle detection rate is 95.3%, and the total processing time for single frame image is 32 ms. It proves that the algorithm can help ADAS system to archieve vehicle detetcion with higher real-time and environmental adaptability.