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For the safety of meeting vehicles at night, a design scheme of vehicle lighting recognition system for 5G vehicular communication networks is proposed in this paper. Firstly, a camera is used for image acquisition, and then the acquired RGB image is converted to a hue-saturation-lightness (HSL) image in Dedicated cloud. Secondly, a $3\times 3$ convolution kemel is constructed, and the threshold of this HSL is used to create a mask. In which we traverse the original image data and perform uitwise operations on the original image, as well as mask by using the Urightest point as the center of calculation. Lastly, the effective area of the image is divided according to the farthest point of the lane line extension, which can shield the sky and street lights. In addition, all of interference points are excluded according to the symmetry of the headlights shape, the number of false detection is reduced effectively. So, the highlight areas can be marked to indicate the lights from the front vehicles. The results of the analysis are issued to the driving assistant system. The experimental results show that the system can identify the front vehicle lighting with an accuracy of 96.8%. With the help of the identified lighting position, the matrix lamp is set to realize the dynamic management of the beam, and it can be applied to traffic at night for preventing traffic accidents.

Vehicle Lighting Recognition System Based on Erosion Algorithm and Effective Area Separation in 5G Vehicular Communication Networks