Dragtraffic: A Non-Expert Interactive and Point-Based Controllable Traffic Scene Generation Framework
CoRR(2024)
摘要
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.
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