EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915299659350016 |
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| author | Madjid, Nadya Abdel Mebrahtu, Murad Ahmad, Abdulrahman Nasser, Abdelmoamen Hassan, Bilal Werghi, Naoufel Dias, Jorge Khonji, Majid |
| author_facet | Madjid, Nadya Abdel Mebrahtu, Murad Ahmad, Abdulrahman Nasser, Abdelmoamen Hassan, Bilal Werghi, Naoufel Dias, Jorge Khonji, Majid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19260 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving Madjid, Nadya Abdel Mebrahtu, Murad Ahmad, Abdulrahman Nasser, Abdelmoamen Hassan, Bilal Werghi, Naoufel Dias, Jorge Khonji, Majid Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2502.19260 |