The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/1255994c-c64f-40f5-8121-9e952e308c9a

Li, Guopeng; Jiao, Yiru; Victor Knoop; Simeon Calvert; van Lint, Hans (2024): Large Car-following Dataset Based on Lyft level-5: Following Autonomous Vehicles vs. Human-driven Vehicles. Version 4. 4TU.ResearchData. dataset. https://doi.org/10.4121/1255994c-c64f-40f5-8121-9e952e308c9a.v4

Dataset

x choose version: version 3 - 2024-08-14 version 2 - 2023-08-30 version 1 - 2023-05-31

• Civil Engineering
• Automotive Engineering
• Engineering
• Transport

Autonomous driving, Car-following, Driving behaviour, Trajectory dataset

CC BY-NC-SA 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Guopeng Li , Yiru Jiao , Victor Knoop , Simeon Calvert , Hans van Lint

Studying how human drivers react differently when following autonomous vehicles (AV) vs. human-driven vehicles (HV) is critical for mixed traffic flow. This dataset contains extracted and enhanced two categories of car-following data, HV-following-AV (H-A) and HV-following-HV (H-H), from the open Lyft level-5 dataset.

• 2023-05-31 first online

• 2024-10-15 published, posted

4TU.ResearchData

zipped files of 6 .zarr trajectory folders and 6 .csv regime index files in regimes.zip, 2 .npz driver id files, 1 zip file for full regimes of each timestamp, and an extra readme.md file

Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles

• https://woven.toyota/en/future-automated-driving-applications

• MiRRORs (grant code 16270) NWO/TTW

TU Delft, Faculty of Civil Engineering and Geosciences, department of Transport & Planning