AI City挑战十周年，聚焦智能交通多场景评测。
The 10th AI City Challenge
- 融合基础模型与几何约束，提升多摄像头感知能力。
- 325支队伍参与，覆盖6大核心赛道，含生成式交通预测。
- 适合关注城市智能系统与跨域泛化研究的开发者。
第10届AI City Challenge于ECCV 2026举行，标志着智能交通、智慧城市与物理AI领域社区基准测试的十年发展。自2017年启动以来，从车辆检测、分类与追踪起步，已扩展为涵盖多摄像头感知、多模态推理、合成到真实学习、生成式预测及隐私保护评估的综合性基准体系。2026年参赛队伍达325支（2025年为245支），来自26个国家和地区（2025年为15个）。六大主赛道包括：多摄像头3D感知、交通安全隐患描述与视觉问答、交通异常推理、基于文本的人体异常搜索、生成式交通视频预测，以及跨城市目标检测。其中第3赛道新增两个域外评测榜单（作为第7、8赛道），分别针对鱼眼视角违规识别和行人情境意图问答。本文总结了挑战设置、数据集、评估协议、排行榜结果及研讨会论文。各赛道成功系统普遍结合基础模型与几何对齐、检索或重排序、合成数据设计、域适应与受控推理技术。
原文摘要 · Abstract (English)
The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and physical AI. Since its 2017 start with vehicle detection, classification, and tracking, the challenge has grown into a broad benchmark suite for multi-camera perception, multimodal reasoning, synthetic-to-real learning, generative forecasting, and privacy-preserving evaluation. The 2026 edition continued this growth with 325 registered teams, up from 245 in 2025, and participation from 26 countries and regions, up from 15. Its six primary tracks cover multi-camera 3D perception, transportation safety captioning and VQA, traffic anomaly reasoning, text-based person anomaly search, generative traffic video forecasting, and cross-city object detection. Track 3 further includes two out-of-domain leaderboards, submitted as Tracks 7 and 8, for fisheye traffic-violation understanding and pedestrian situated-intent VQA. This paper summarizes the challenge setup, datasets, evaluation protocols, leaderboard results, and workshop papers. Across tracks, successful systems combine foundation models with geometric grounding, retrieval or reranking, synthetic-data design, domain adaptation, and controlled inference.
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