The evaluation and training of autonomous driving systems require diverse and
scalable corner cases. However, most existing scene generation methods lack
controllability, accuracy, and versatility, resulting in unsatisfactory
generation results. To address this problem, we propose Dragtraffic, a
generalized, point-based, and controllable traffic scene generation framework
based on conditional diffusion. Dragtraffic enables non-experts to generate a
variety of realistic driving scenarios for different types of traffic agents
through an adaptive mixture expert architecture. We use a regression model to
provide a general initial solution and a refinement process based on the
conditional diffusion model to ensure diversity. User-customized context is
introduced through cross-attention to ensure high controllability. Experiments
on a real-world driving dataset show that Dragtraffic outperforms existing
methods in terms of authenticity, diversity, and freedom.

通过基于条件扩散的广义、基于点的可控交通场景生成框架 Dragtraffic，使用回归模型提供初始解决方案，并通过条件扩散模型的细化过程来确保多样性，引入用户自定义上下文以确保高可控性，实验证明 Dragtraffic 在真实驾驶数据集上的真实性、多样性和自由度方面优于现有方法。