{"url":"/dataset/ddd17","name":"DDD17","full_name":"DAVIS Driving Dataset 2017","description_markdown":"DDD17 has over 12 h of a 346x260 pixel DAVIS sensor recording highway and city driving in daytime, evening, night, dry and wet weather conditions, along with vehicle speed, GPS position, driver steering, throttle, and brake captured from the car's on-board diagnostics interface. \r\n\r\nSource: [DDD17: End-To-End DAVIS Driving Dataset](https://arxiv.org/pdf/1711.01458v1.pdf)\r\nImage Source: [DVS Driving Dataset 2017 (DDD17) README](https://docs.google.com/document/d/1HM0CSmjO8nOpUeTvmPjopcBcVCk7KXvLUuiZFS6TWSg/pub)","description_withheld":null,"homepage":"https://docs.google.com/document/d/1HM0CSmjO8nOpUeTvmPjopcBcVCk7KXvLUuiZFS6TWSg/pub","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/ddd17-end-to-end-davis-driving-dataset","title":"DDD17: End-To-End DAVIS Driving Dataset","first_author":"Jonathan Binas","url":null},"license":{"name":"CC BY-SA 4.0","url":"https://creativecommons.org/licenses/by-sa/4.0/"},"modalities":[],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Visual Place Recognition","url":"/task/visual-place-recognition","datasets_with_task":"/datasets/task/visual-place-recognition"},{"name":"Autonomous Driving","url":"/task/autonomous-driving","datasets_with_task":"/datasets/task/autonomous-driving"},{"name":"Motion Estimation","url":"/task/motion-estimation","datasets_with_task":"/datasets/task/motion-estimation"}],"languages":[],"variants":["DDD17"],"data_loaders":[],"num_papers_in_archive":41,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-ddd17","task":"Semantic Segmentation","dataset_variant":"DDD17","rows":9,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"BRENet","paper":"/paper/rethinking-rgb-event-semantic-segmentation","metrics":{"mIoU":"78.56"},"code_links":[{"title":"zyaocoder/BRENet","url":"https://github.com/zyaocoder/BRENet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rethinking-rgb-event-semantic-segmentation","title":"Rethinking RGB-Event Semantic Segmentation with a Novel Bidirectional Motion-enhanced Event Representation","date":"2025-05-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/delivering-arbitrary-modal-semantic","title":"Delivering Arbitrary-Modal Semantic Segmentation","date":"2023-03-02","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":6,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/halsie-hybrid-approach-to-learning","title":"HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalities","date":"2022-11-19","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/segnext-rethinking-convolutional-attention","title":"SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation","date":"2022-09-18","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/ess-learning-event-based-semantic","title":"ESS: Learning Event-based Semantic Segmentation from Still Images","date":"2022-03-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cmx-cross-modal-fusion-for-rgb-x-semantic","title":"CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers","date":"2022-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/exploring-event-driven-dynamic-context-for","title":"Exploring Event-driven Dynamic Context for Accident Scene Segmentation","date":"2021-12-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/segformer-simple-and-efficient-design-for","title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","date":"2021-05-31","rows_on_this_dataset":1,"code_links":28,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":86,"samples_ran":48,"samples_unverified":38,"pointer_only_for_licence":15,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ev-segnet-semantic-segmentation-for-event","title":"EV-SegNet: Semantic Segmentation for Event-based Cameras","date":"2018-11-29","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":100,"samples_ran":60,"samples_unverified":40,"pointer_only_for_licence":15,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}