构建首个面向海湾地区交通的多任务自动驾驶数据集，支持跟踪、预测与意图识别。
EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving
- 基于3万帧行车视角图像，标注超57万框，覆盖150公里海湾区域道路。
- 涵盖多目标追踪、轨迹预测与驾驶意图识别三类任务，含完整评估方案。
- 适合研究复杂城市交通场景下多任务协同的自动驾驶团队使用。
本文提出迪拜多任务（EMT）数据集，旨在支持统一框架下的多任务基准测试。数据集包含超过3万帧车载摄像头视角视频，以及57万条标注边界框，覆盖约150公里的驾驶路线，真实反映海湾地区特有的道路结构、拥堵模式和驾驶行为。该数据集支持三大核心任务：多智能体追踪、轨迹预测与驾驶意图识别。每个任务均配备相应的评估方案：（1）针对多类别场景与遮挡处理的多目标追踪实验；（2）采用深度序列与交互感知模型的轨迹预测评估；（3）基于观测轨迹的意图预测实验。数据集已公开发布于 https://avlab.io/emt-dataset，配套预处理脚本与评估模型见 https://github.com/AV-Lab/emt-dataset。
原文摘要 · Abstract (English)
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.
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