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The real-time analysis of the road driving environment is the key technology of intelligent driving. Although the neural network can achieve good precision in semantic segmentation and depth estimation, it is difficult to realize real-time calculation due to problems such as many model parameters and large calculation amount. Aiming at this problem, this paper proposes a lightweight and efficient feature extraction module and a feature decoding module that comprehensively considers semantic information and depth information, and simultaneously performs two tasks of semantic segmentation and depth estimation in a network. In the CityScapes dataset, the mIOU of the semantic segmentation prediction result is 65.0%, the error of the depth estimation result is 0.21, and the inference speed reaches 65 FPS on a single GPU, meeting the real-time requirements.