Aiming at the different photosensitive characteristics of infrared non visible light and visible light vision in the imaging process,and facing the typical "black hole" and "white hole" problem in tunnels,the visual identification and fusion perception technology under the sudden illumination environment is studied from the perspective of autonomous vehicles. Two scenarios are selected:low illumination vehicles entering the tunnel and vehicles leaving the tunnel under low light conditions. Local energy and convolution sparse representation algorithm (CSR) are used to fuse the two images,and six evaluation indexes including MI,SF,AG,Q AB/F,SSIM and PSNR are used to evaluate the images. The experimental results show that CSR E algorithm for images at the tunnel entrance improves the edge information transfer factor (Q AB/F) by 14.14%,the average running time of image at the tunnel exit is reduced by 1.17ms and the structural similarity (SSIM) is improved by 3.38%compared with the five algorithms of Curvelet,NSCT,NSCT T,SR C&L and SF Energy Q. The proposed infrared non visible and visible vision fusion imaging method made up for the incomplete representation of a specific scene by a single sensor,achievesa comprehensive,clear and accurate representation of the scene,effectively solves the loss of edge information in the source image,and enhances the spectral information of the image.