In the complex traffic scene image, road segmentation is difficult and the edges of the segmentation are rough. In order to solve this problem, a road segmentation method based on multi-feature fusion and conditional random field is proposed. Firstly, the textons and color features of the image are extracted from the traffic image. Then, the road segmentation problem is regarded as a pixel-based binary classification problem. The extracted texton features and color features are fused and input into the SVM classifier, which can achieve the coarse segmentation of the road area and the background area in the traffic image. Finally, by using the color and position constraints of the fully connected conditional random field to optimize segmentation results, a smoother segmentation edge can be obtained and compared with other segmentation algorithms. The experimental results demonstrate that road segmentation method that based on the multi-feature fusion and the conditional random field achieves 95.37% of average segmentation accuracy and 94.55% of mean pixel accuracy.