# The 9th AI City Challenge

arXiv: https://arxiv.org/abs/2508.13564 · HTML: https://mlsanity.com/paper/2508.13564/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection](https://mlsanity.com/paper/2305.17449/) (2023)
- [WTS: A Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding](https://mlsanity.com/paper/2407.15350/) (2024)
- [Warehouse Spatial Question Answering with LLM Agent](https://mlsanity.com/paper/2507.10778/) (2025)

## Built on by (most cited first, 12 of 12)

- [The 10th AI City Challenge](https://mlsanity.com/paper/2608.17044/) (2026)
- [Online 3D Multi-Camera Perception through Robust 2D Tracking and Depth-based Late Aggregation](https://mlsanity.com/paper/2509.09946/) (2025)
- [A Unified 3D Object Perception Framework for Real-Time Outside-In Multi-Camera Systems](https://mlsanity.com/paper/2601.10819/) (2026)
- [From Detection to Understanding: TAR and TAR-Bench for Multi-Task Traffic Anomaly Reasoning](https://mlsanity.com/paper/2608.10317/) (2026)
- [TinyGiantVLM: A Lightweight Vision-Language Architecture for Spatial Reasoning under Resource Constraints](https://mlsanity.com/paper/2508.17595/) (2025)
- [Advancing Reliable Synthetic Video Detection: Insights from the SAFE Challenge](https://mlsanity.com/paper/2605.06912/) (2026)
- [Frozen High-Resolution Inference for Cross-City Object Detection: An AI City Challenge 2026 Study](https://mlsanity.com/paper/2608.03136/) (2026)
- [DRAFE: Domain-Robust Asymmetric Fusion of Heterogeneous Detection Transformers for Cross-City Fine-Grained Traffic Object Detection](https://mlsanity.com/paper/2608.16632/) (2026)
- [Syn2RealTrack: Bridging the Gap Between Synthetic and Real-World Datasets for Online Multi-View Multi-Target Tracking](https://mlsanity.com/paper/2608.24130/) (2026)
- [Rethinking Pre-Training and Augmentation for Zero-Shot Cross-City Object Detection](https://mlsanity.com/paper/2608.24154/) (2026)
- [Online Multi-Camera 3D Tracking via ID Prediction over Recurrent Sparse Queries](https://mlsanity.com/paper/2609.18363/) (2026)
- [Physical AI Smart Spaces: A Large-Scale Benchmark for Multi-Camera 3D Perception in Smart Spaces](https://mlsanity.com/paper/2610.02580/) (2026)

## Abstract (arXiv)

The ninth AI City Challenge continues to advance real-world applications of computer vision and AI in transportation, industrial automation, and public safety. The 2025 edition featured four tracks and saw a 17% increase in participation, with 245 teams from 15 countries registered on the evaluation server. Public release of challenge datasets led to over 30,000 downloads to date. Track 1 focused on multi-class 3D multi-camera tracking, involving people, humanoids, autonomous mobile robots, and forklifts, using detailed calibration and 3D bounding box annotations. Track 2 tackled video question answering in traffic safety, with multi-camera incident understanding enriched by 3D gaze labels. Track 3 addressed fine-grained spatial reasoning in dynamic warehouse environments, requiring AI systems to interpret RGB-D inputs and answer spatial questions that combine perception, geometry, and language. Both Track 1 and Track 3 datasets were generated in NVIDIA Omniverse. Track 4 emphasized efficient road object detection from fisheye cameras, supporting lightweight, real-time deployment on edge devices. The evaluation framework enforced submission limits and used a partially held-out test set to ensure fair benchmarking. Final rankings were revealed after the competition concluded, fostering reproducibility and mitigating overfitting. Several teams achieved top-tier results, setting new benchmarks in multiple tasks.