Skip to content
Preprint

The 10th AI City Challenge

Aug 2026 · 8 citations · ⚡ 2 influential · 59 references
Computer Science

TL;DR

The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and physical AI with 325 registered teams, and participation from 26 countries and regions, up from 15.

Abstract

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.

View source

Similar papers

#artificial intelligence Review Aug 2026

One-Stage Object Detectors in Autonomous Driving

Autonomous vehicles depend on fast and reliable perception systems to detect surrounding vehicles, pedestrians, cyclists, traffic signs, and other road objects in real time. This paper presents a comprehensive survey and analysis of one-stage object detectors for autonomous driving rather than an implementation of a ne...

Jonel Roman, Ryan Sirjue, Peter Nguyen et al. · 0 citations
Preprint Sep 2026

A Benchmark for Vehicle Attribute Classification in Cross-Domain Surveillance Scenarios

Vehicle attribute analysis is a key component of Intelligent Transportation Systems (ITS), supporting applications such as vehicle identification, traffic monitoring, and forensic investigation. However, models trained under controlled conditions often degrade in real surveillance scenarios due to changes in viewpoint,...

S. Silva, Otavio T. Remer, G. E. Lima et al. · 0 citations
Open access Aug 2026

Amneen: An AI-Based System for Real-Time Tracking and Management of Crowds

Amneen is a crowd management system that uses Artificial Intelligence (AI) and Computer Vision (CV) to track crowd density in public places and prevent dangerous situations before they occur, and predicts congestion before it occurs.

Rsha Mirza, Dareen Alsulami, Amal Aljadani et al. · 0 citations
Open access Aug 2026

Explainable Artificial Intelligence and Computer Vision for RealTime Detection and Prediction of Critical Events in Smart Cities

This study presents XAI-CityVision, an explainable artificial intelligence platform that combines computer vision, object identification, temporal learning, risk assessment, and visual explanation that offers a repeatable architecture for integrating deployment-aware evaluation, operator-oriented explanations, and pred...

K. N. V. R. Kumar, S. D. Bhopale, Konolla Siva Ramakrishna et al. · 0 citations
Sep 2026

Clemson Off-Road Dataset (CORD): A Multimodal Benchmark for Autonomy beyond the Flat-World

While autonomous perception has matured within the structured confines of urban roadways, it remains brittle when confronting the chaotic, non-rigid terrain of the natural world. This paper introduces the Clemson Off-Road Dataset, a high-fidelity, multimodal dataset engineered to bridge this gap by challenging standard...

Ashish Patil, Prakhar Gupta, Mayuresh Bhosale et al. · 0 citations
Review Open access Aug 2026

Middle-East Traffic Sign (METS) recognition: databases and models

A new model is proposed, which improves the best performance of existing databases; it merges the Arabic Traffic Signs collection with the Jordanian dataset, and it uses several data augmentation techniques to create the largest publicly available database in the Arabic world to date.

Elena Battini Sonmez, Hamza Arnaout, Albano Younes · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.