Depth estimation of monocular infrared image is a key to scene understanding of night driverless vehicle. Aiming at the depth estimation of night driverless vehicle scene, a depth estimation method based on the deep convolution-deconvolution neural network is proposed. Infrared images and radar depth data are fed to the deep convolution-deconvolution neural network. The depth estimation problem is transformed to a pixel-wise classification task in the training of the depth estimation model. The radar depth values are quantized into discrete bins corresponding to the pixels of infrared image and the bins are labeled according to their depth range. The deep convolution-deconvolution neural network based depth estimation model is trained by classifying each pixel to the corresponding depth. The experimental results show that the depth estimation time is 0.04 s/frame, which use the depth estimation model to estimate the scene depth information of infrared image captured by the night driverless vehicle, and the real-time requirement in practical applications is reached.