With the continuous increase of the amount of information, people urgently need to identify the information in the image in more detail in order to obtain richer information from the image. This work explores the dynamic complex image segmentation of self-driving vehicle under Digital Twins (DTs) based on Memory-augmented Neural Networks (MANNs), so as to further improve the performance of self-driving in intelligent transportation. In view of the complexity of the environment and the dynamic changes of the scene in intelligent transportation, this work constructs a segmentation model for dynamic complex image of self-driving vehicle under DTs based on MANNs by optimizing the Deep Learning algorithm and further combining with the DTs technology, so as to recognize the information in the environment image during the self-driving. Finally, the performance of the constructed model is analyzed by experimenting with different image datasets (PASCALVOC 2012, NYUDv2, PASCAL CONTEXT, and real self-driving complex traffic image data). The results show that compared with other classical algorithms, the established MANN-based model has an accuracy of about 85.80%, the training time is shortened to 107.00 s, the test time is 0.70 s, and the speedup ratio is high. In addition, the average algorithm parameter of the given energy function α=0.06 reaches the maximum value. Therefore, it is found that the proposed model shows high accuracy and short training time, which can provide experimental reference for future image visual computing and intelligent information processing.