{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/road-the-road-event-awareness-dataset-for","title":"ROAD: The ROad event Awareness Dataset for Autonomous Driving","arxiv_id":"2102.11585","date":"2021-02-23","proceeding":null,"authors":["Gurkirt Singh","Stephen Akrigg","Manuele Di Maio","Valentina Fontana","Reza Javanmard Alitappeh","Suman Saha","Kossar Jeddisaravi","Farzad Yousefi","Jacob Culley","Tom Nicholson","Jordan Omokeowa","Salman Khan","Stanislao Grazioso","Andrew Bradley","Giuseppe Di Gironimo","Fabio Cuzzolin"],"abstract":"Humans drive in a holistic fashion which entails, in particular, understanding dynamic road events and their evolution. Injecting these capabilities in autonomous vehicles can thus take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. ROAD comprises videos originally from the Oxford RobotCar Dataset annotated with bounding boxes showing the location in the image plane of each road event. We benchmark various detection tasks, proposing as a baseline a new incremental algorithm for online road event awareness termed 3D-RetinaNet. We also report the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as that of the winners of the ICCV2021 ROAD challenge, which highlight the challenges faced by situation awareness in autonomous driving. ROAD is designed to allow scholars to investigate exciting tasks such as complex (road) activity detection, future event anticipation and continual learning. The dataset is available at https://github.com/gurkirt/road-dataset; the baseline can be found at https://github.com/gurkirt/3D-RetinaNet.","url_abs":"https://arxiv.org/abs/2102.11585v3","url_pdf":"https://arxiv.org/pdf/2102.11585v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"road-the-road-event-awareness-dataset-for","repo_url":"https://github.com/gurkirt/3D-RetinaNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"road-the-road-event-awareness-dataset-for","repo_url":"https://github.com/gurkirt/road-dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"road-the-road-event-awareness-dataset-for","repo_url":"https://github.com/salmank255/ROAD_Waymo_Baseline","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"action-detection","task_name":"Action Detection"},{"task_slug":"activity-detection","task_name":"Activity Detection"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"continual-learning","task_name":"Continual Learning"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"event-detection","task_name":"Event Detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fpn","method_name":"FPN"},{"method_slug":"focal-loss","method_name":"Focal Loss"},{"method_slug":"retinanet","method_name":"RetinaNet"}],"datasets_introduced":[{"slug":"road","name":"ROAD","full_name":"ROAD: The ROad event Awareness Dataset for Autonomous Driving"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2102.11585","atlas_url":"https://app.syntology.ai/?focus=2102.11585","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.11585"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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