用地图和视觉提示生成新城市场景，让自动驾驶系统零标注跨城部署。
CityGen: Structure-Guided City-Style Synthesis for Cross-City Autonomous Driving
- 基于高精地图和城市视觉提示，用扩散模型生成新城市驾驶场景。
- 在多个任务中显著提升跨城市鲁棒性，无需目标城市标注数据。
- 适合需要快速适配新城市的自动驾驶研发团队使用。
自动驾驶系统通常在有限地理区域内训练与评估，导致在新城市部署时面临严重性能下降。由于外观、道路拓扑和交通模式的显著域偏移，现有基于领域自适应、数据增强或合成数据生成的方法往往依赖目标域标注数据、城市特异性标注或任务定制设计，难以实现全面评估与可扩展性。本文提出CityTransfer-Bench，一个地理上分离的跨城市泛化评估基准，涵盖感知、分割与规划任务；并设计CityGen，一种基于扩散的生成框架，通过高精地图引导、城市级视觉提示驱动的零标注城市适配合成。大量实验表明，CityGen在多任务下持续提升跨城市鲁棒性，为可泛化的自动驾驶系统构建了高效、可扩展的基础。
原文摘要 · Abstract (English)
Autonomous driving systems are commonly trained and evaluated within limited geographic regions, which hinders their scalability when deployed in new cities. However, significant domain shifts in appearance, road topology, and traffic patterns often cause severe performance degradation under cross-city deployment. Existing approaches based on domain adaptation, data augmentation, or synthetic data generation typically rely on labeled target data, city-specific annotations, or task-specific designs, limiting their scalability and effectiveness for holistic evaluation. In this paper, we introduce CityTransfer-Bench, a geographically disjoint benchmark for evaluating cross-city generalization across perception, segmentation, and planning, and propose CityGen, a diffusion-based generative framework that performs zero-label city adaptation via HD-map-conditioned synthesis guided by city-level visual prompts. Extensive experiments demonstrate that CityGen consistently improves cross-city robustness across multiple tasks, establishing a scalable and label-efficient foundation for generalizable autonomous driving.
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