ROAD-Waymo: Action Awareness at Scale for Autonomous Driving
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arXiv
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| Format: | Preprint |
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2024
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| author | Khan, Salman Teeti, Izzeddin Alitappeh, Reza Javanmard Stoian, Mihaela C. Giunchiglia, Eleonora Singh, Gurkirt Bradley, Andrew Cuzzolin, Fabio |
| author_facet | Khan, Salman Teeti, Izzeddin Alitappeh, Reza Javanmard Stoian, Mihaela C. Giunchiglia, Eleonora Singh, Gurkirt Bradley, Andrew Cuzzolin, Fabio |
| contents | Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic understanding of what is happening within the scene for safe interaction with other road users. Few datasets exist for the purpose of developing and training algorithms to comprehend the actions of other road users. This paper presents ROAD-Waymo, an extensive dataset for the development and benchmarking of techniques for agent, action, location and event detection in road scenes, provided as a layer upon the (US) Waymo Open dataset. Considerably larger and more challenging than any existing dataset (and encompassing multiple cities), it comes with 198k annotated video frames, 54k agent tubes, 3.9M bounding boxes and a total of 12.4M labels. The integrity of the dataset has been confirmed and enhanced via a novel annotation pipeline designed for automatically identifying violations of requirements specifically designed for this dataset. As ROAD-Waymo is compatible with the original (UK) ROAD dataset, it provides the opportunity to tackle domain adaptation between real-world road scenarios in different countries within a novel benchmark: ROAD++. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01683 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ROAD-Waymo: Action Awareness at Scale for Autonomous Driving Khan, Salman Teeti, Izzeddin Alitappeh, Reza Javanmard Stoian, Mihaela C. Giunchiglia, Eleonora Singh, Gurkirt Bradley, Andrew Cuzzolin, Fabio Computer Vision and Pattern Recognition Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic understanding of what is happening within the scene for safe interaction with other road users. Few datasets exist for the purpose of developing and training algorithms to comprehend the actions of other road users. This paper presents ROAD-Waymo, an extensive dataset for the development and benchmarking of techniques for agent, action, location and event detection in road scenes, provided as a layer upon the (US) Waymo Open dataset. Considerably larger and more challenging than any existing dataset (and encompassing multiple cities), it comes with 198k annotated video frames, 54k agent tubes, 3.9M bounding boxes and a total of 12.4M labels. The integrity of the dataset has been confirmed and enhanced via a novel annotation pipeline designed for automatically identifying violations of requirements specifically designed for this dataset. As ROAD-Waymo is compatible with the original (UK) ROAD dataset, it provides the opportunity to tackle domain adaptation between real-world road scenarios in different countries within a novel benchmark: ROAD++. |
| title | ROAD-Waymo: Action Awareness at Scale for Autonomous Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.01683 |