{"url":"/dataset/summit","name":"SUMMIT","full_name":null,"description_markdown":"**SUMMIT** is a high-fidelity simulator that facilitates the development and testing of crowd-driving algorithms. By leveraging the open-source OpenStreetMap map database and a heterogeneous multi-agent motion prediction model developed in our earlier work, SUMMIT simulates dense, unregulated urban traffic for heterogeneous agents at any worldwide locations that OpenStreetMap supports. SUMMIT is built as an extension of [CARLA](carla) and inherits from it the physical and visual realism for autonomous driving simulation. SUMMIT supports a wide range of applications, including perception, vehicle control, planning, and end-to-end learning.","description_withheld":null,"homepage":"https://github.com/AdaCompNUS/summit","introduced_date":"2019-11-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/summit-a-simulator-for-urban-driving-in","title":"SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic","first_author":null,"url":null},"license":{"name":"Multiple licenses","url":"https://github.com/AdaCompNUS/summit#license"},"modalities":[{"name":"Environment","url":"/datasets/modality/environment"}],"tasks":[],"languages":[],"variants":["SUMMIT"],"data_loaders":[{"repo":"https://github.com/AdaCompNUS/summit","url":"https://github.com/AdaCompNUS/summit","frameworks":[]}],"num_papers_in_archive":14,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}