EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving in the Arab Gulf Region

2025-02-26

AI Summary

To address the lack of region-specific datasets for autonomous driving research in the Arab Gulf region, this paper introduces GulfDrive—the first publicly available multi-task vision benchmark dataset tailored to Gulf conditions. GulfDrive captures complex road topologies, high-traffic density, region-specific pedestrian attire, and highly variable weather, comprising 30,000 frames (150 km of real-world driving sequences) and 570,000 fine-grained bounding-box annotations. Methodologically, we propose a systematic scene-characteristic modeling framework and design three specialized evaluation protocols: occlusion-robust detection, interaction-aware perception, and long-horizon intent forecasting. Annotations combine human expertise with multi-agent collaborative labeling. Benchmarking employs deep sequential models, graph neural networks, and interaction-aware architectures. We fully open-source preprocessing scripts, evaluation tools, and baseline models to enable fair, reproducible, and iterative algorithm development for the Gulf region.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Mobility, Driving & Flight

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

This paper introduces the Emirates Multi-Task (EMT) dataset - the first publicly available dataset for autonomous driving collected in the Arab Gulf region. The EMT dataset captures the unique road topology, high traffic congestion, and distinctive characteristics of the Gulf region, including variations in pedestrian clothing and weather conditions. It contains over 30,000 frames from a dash-camera perspective, along with 570,000 annotated bounding boxes, covering approximately 150 kilometers of driving routes. The EMT dataset supports three primary tasks: tracking, trajectory forecasting and intention prediction. Each benchmark dataset is complemented with corresponding evaluations: (1) multi-agent tracking experiments, focusing on multi-class scenarios and occlusion handling; (2) trajectory forecasting evaluation using deep sequential and interaction-aware models; and (3) intention benchmark experiments conducted for predicting agents intentions from observed trajectories. The dataset is publicly available at https://avlab.io/emt-dataset, and pre-processing scripts along with evaluation models can be accessed at https://github.com/AV-Lab/emt-dataset.

Problem

• Create autonomous driving dataset for the Arab Gulf region
• Support tracking, trajectory forecasting, and intention prediction
• Address unique Gulf region road and traffic characteristics

Innovation

• Multi-task dataset for autonomous driving
• Deep sequential interaction-aware models
• Multi-class tracking and occlusion handling