基于生成对抗网络的自动驾驶交通场景生成

自动驾驶汽车是智能交通的重要组成部分，也是未来交通的发展趋势。提高自动驾驶技术的可靠性需要对自动驾驶汽车进行广泛的测试。然而，进行实车路测成本高昂且风险较高。如何建立模型生成多样且真实的交通场景，在测试自动驾驶技术的时候显得尤为重要。针对自动驾驶交通场景生成问题，提出了一个基于生成对抗网络的交通场景生成模型TSG-GAN。TSG-GAN模型利用丰富的交通场景数据（如车道的几何形状、人行横道、交通信号、周围车辆等）快速生成真实且多样的交通场景。在设置车辆驾驶意图的情况下，TSG-GAN模型可以精确地生成现实中未观察到的真实交通场景。通过在公开数据集上进行测试，验证了该模型的有效性。

Traffic scenario generation based on generative adversarial networks for autonomous driving

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.