{"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/the-highd-dataset-a-drone-dataset-of","title":"The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems","arxiv_id":"1810.05642","date":"2018-10-11","proceeding":null,"authors":["Robert Krajewski","Julian Bock","Laurent Kloeker","Lutz Eckstein"],"abstract":"Scenario-based testing for the safety validation of highly automated vehicles\nis a promising approach that is being examined in research and industry. This\napproach heavily relies on data from real-world scenarios to derive the\nnecessary scenario information for testing. Measurement data should be\ncollected at a reasonable effort, contain naturalistic behavior of road users\nand include all data relevant for a description of the identified scenarios in\nsufficient quality. However, the current measurement methods fail to meet at\nleast one of the requirements. Thus, we propose a novel method to measure data\nfrom an aerial perspective for scenario-based validation fulfilling the\nmentioned requirements. Furthermore, we provide a large-scale naturalistic\nvehicle trajectory dataset from German highways called highD. We evaluate the\ndata in terms of quantity, variety and contained scenarios. Our dataset\nconsists of 16.5 hours of measurements from six locations with 110 000\nvehicles, a total driven distance of 45 000 km and 5600 recorded complete lane\nchanges. The highD dataset is available online at: http://www.highD-dataset.com","url_abs":"http://arxiv.org/abs/1810.05642v1","url_pdf":"http://arxiv.org/pdf/1810.05642v1.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":"the-highd-dataset-a-drone-dataset-of","repo_url":"https://github.com/RobertKrajewski/highD-dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"the-highd-dataset-a-drone-dataset-of","repo_url":"https://github.com/westny/dronalize","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"}],"methods":[],"datasets_introduced":[{"slug":"highd-dataseth","name":"highD Dataset","full_name":"The Highway Drone Dataset Naturalistic Trajectories of 110 500 Vehicles Recorded at German Highways"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.05642","atlas_url":"https://app.syntology.ai/?focus=1810.05642","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}