Realistic and diverse traffic scenarios in large quantities are crucial for the development and validation of autonomous driving systems. However, owing to numerous difficulties in the data collection process and the reliance on intensive annotations, real-world datasets lack sufficient quantity and diversity to support the increasing demand for data. This work introduces DriveSceneGen, a data-driven driving scenario generation method that learns from the real-world driving dataset and generates entire dynamic driving scenarios from scratch. DriveSceneGen is able to generate novel driving scenarios that align with real-world data distributions with high fidelity and diversity. Experimental results on 5k generated scenarios highlight the generation quality, diversity, and scalability compared to real-world datasets. To the best of our knowledge, DriveSceneGen is the first method that generates novel driving scenarios involving both static map elements and dynamic traffic participants from scratch.

翻译：摘要：大规模、多样且逼真的交通场景对于自动驾驶系统的开发与验证至关重要。然而，由于数据采集过程中的诸多困难以及对密集标注的依赖，真实世界数据集在数量和多样性上不足以满足日益增长的数据需求。本文提出DriveSceneGen，一种数据驱动的驾驶场景生成方法，能够从真实驾驶数据集中学习并从头生成完整的动态驾驶场景。DriveSceneGen可生成与真实数据分布高度一致、具有高保真度和多样性的新颖驾驶场景。基于5000个生成场景的实验结果，验证了其相比于真实数据集在生成质量、多样性和可扩展性方面的优势。据我们所知，DriveSceneGen是首个能够从头生成同时包含静态地图元素与动态交通参与者的新颖驾驶场景的方法。