Self-driving vehicles are an important part of intelligent transportation and a trend of future transportation. Improving the reliability of autonomous driving technology requires extensive testing of autonomous driving vehicles. However, conducting real-world road tests is costly and risky. It is especially important to build models to generate diverse and realistic traffic scenarios for testing autonomous driving techniques. A traffic scenario generation generative adversarial network model called TSG-GAN is proposed for the generation of traffic scenarios. The TSG-GAN model uses Generative Adversarial Networks to rapidly generate realistic and diverse traffic scenarios by using rich traffic scenario data (e.g., lane geometry, crosswalks, traffic signals, surrounding vehicles, etc.). With reasonable driving intentions of vehicles, the TSG-GAN model can precisely generate realistic traffic scenarios that are not observed in practice. The effectiveness of the proposed model is verified by testing on a publicly available dataset.