用多智能体系统在真实街景上快速生成符合要求的自行车道设计方案。
From Image Generation to Infrastructure Design: a Multi-agent Pipeline for Street Design Generation
- 通过多智能体协同，直接在街景图像上编辑自行车道布局。
- 可在不同道路形态下生成视觉连贯、指令合规的设计方案。
- 适合城市规划者和公众参与交通设施设计与讨论。
真实的街道设计可视化对促进主动出行规划中的公众参与至关重要。传统方法耗时费力，阻碍了集体讨论与协作决策。尽管人工智能辅助生成设计具有变革潜力，能快速生成设计场景，但现有方法通常需要大量领域特定训练数据，且难以在复杂的街景中实现精确的空间布局调整。本文提出一个多智能体系统，可直接在真实街景图像上编辑和重设计自行车设施。该框架整合了车道定位、提示优化、设计生成与自动化评估，合成出视觉逼真、情境契合的设计方案。在多种城市场景下的实验表明，系统能适应不同的道路几何形态与环境条件，持续产出视觉连贯且符合指令的结果。本工作为将多智能体流水线应用于交通基础设施规划与设施设计奠定了基础。
原文摘要 · Abstract (English)
Realistic visual renderings of street-design scenarios are essential for public engagement in active transportation planning. Traditional approaches are labor-intensive, hindering collective deliberation and collaborative decision-making. While AI-assisted generative design shows transformative potential by enabling rapid creation of design scenarios, existing generative approaches typically require large amounts of domain-specific training data and struggle to enable precise spatial variations of design/configuration in complex street-view scenes. We introduce a multi-agent system that edits and redesigns bicycle facilities directly on real-world street-view imagery. The framework integrates lane localization, prompt optimization, design generation, and automated evaluation to synthesize realistic, contextually appropriate designs. Experiments across diverse urban scenarios demonstrate that the system can adapt to varying road geometries and environmental conditions, consistently yielding visually coherent and instruction-compliant results. This work establishes a foundation for applying multi-agent pipelines to transportation infrastructure planning and facility design.
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