EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving

TL;DR

The EMT dataset provides a comprehensive multi-task benchmark for autonomous driving, featuring diverse annotations and tasks tailored to Gulf region traffic scenarios, facilitating advances in tracking, forecasting, and intention prediction.

Contribution

This paper introduces the EMT dataset, a large-scale multi-task benchmark specifically designed for autonomous driving in Gulf region traffic conditions, supporting tracking, forecasting, and intention prediction tasks.

Findings

Supports multi-agent tracking with occlusion handling

Enables trajectory forecasting with deep models

Facilitates intention prediction based on observed trajectories

Abstract

This paper introduces the Emirates Multi-Task (EMT) dataset, designed to support multi-task benchmarking within a unified framework. It comprises over 30,000 frames from a dash-camera perspective and 570,000 annotated bounding boxes, covering approximately 150 kilometers of driving routes that reflect the distinctive road topology, congestion patterns, and driving behavior of Gulf region traffic. The dataset supports three primary tasks: tracking, trajectory forecasting, and intention prediction. Each benchmark is accompanied by corresponding evaluations: (1) multi-agent tracking experiments addressing multi-class scenarios and occlusion handling; (2) trajectory forecasting evaluation using deep sequential and interaction-aware models; and (3) intention prediction experiments based on observed trajectories. The dataset is publicly available at https://avlab.io/emt-dataset, with pre-processing scripts and evaluation models at https://github.com/AV-Lab/emt-dataset.