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VED

VED (Vehicle Energy Dataset): A Large-scale Dataset for Vehicle Energy Consumption Research. (IEEE Transactions on Intelligent Transportation Systems, 2020)

https://github.com/gsoh/VED

Science Score: 33.0%

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    Links to: arxiv.org
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Repository

VED (Vehicle Energy Dataset): A Large-scale Dataset for Vehicle Energy Consumption Research. (IEEE Transactions on Intelligent Transportation Systems, 2020)

Basic Info
  • Host: GitHub
  • Owner: gsoh
  • License: apache-2.0
  • Default Branch: master
  • Homepage:
  • Size: 164 MB
Statistics
  • Stars: 109
  • Watchers: 6
  • Forks: 44
  • Open Issues: 2
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Created about 7 years ago · Last pushed over 4 years ago
Metadata Files
Readme License

README.md

VED (Vehicle Energy Dataset)

A novel large-scale database for fuel and energy use of diverse vehicles in real-world.

VED captures GPS trajectories of vehicles along with their timeseries data of fuel, energy, speed, and auxiliary power usage, and the data was collected through onboard OBD-II loggers from Nov, 2017 to Nov, 2018. The fleet consists of total 383 personal cars (264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs) in Ann Arbor, Michigan, USA. Driving scenarios range from highways to traffic-dense downtown area in various driving conditions and seasons. In total, VED accumulates approximately 374,000 miles.

A number of examples were presented in the paper to demonstrate how VED can be utilized for vehicle energy and behavior studies. Potential research opportunities include data-driven vehicle energy consumption modeling, driver behavior modeling, machine and deep learning, calibration of traffic simulators, optimal route choice modeling, prediction of human driver behaviors, and decision making of self-driving cars.

Link to the paper: Vehicle Energy Dataset (VED), A Large-scale Dataset for Vehicle Energy Consumption Research\ Geunseob (GS) Oh, David J. LeBlanc, Huei Peng\ IEEE Transactions on Intelligent Transportation Systems (T-ITS), 2020.\ The paper is also available on Arxiv.

Contact: gsoh@umich.edu.

GS Oh, Ph.D. Candidate, University of Michigan.

Files

VED consists of Dynamic Data (time-stamped naturalistic driving records of 383 vehicles) and Static Data (Vehicle parameters for the 383 vehicles)

Dynamic Data: "VEDDynamicData.7z" contains a number of "VEDmmddyy_week.csv" files - Includes a week worth dynamic data, for mmddyy ~ (mmddyy + 7 days) - Columns represent: DayNum, VehId, Trip, Timestamp(ms), Latitude[deg], Longitude[deg], Vehicle Speed[km/h], MAF[g/sec], Engine RPM[RPM], Absolute Load[%], Outside Air Temperature[DegC], Fuel Rate[L/hr], Air Conditioning Power[kW], Air Conditioning Power[Watts], Heater Power[Watts], HV Battery Current[A], HV Battery SOC[%], HV Battery Voltage[V], Short Term Fuel Trim Bank 1[%], Short Term Fuel Trim Bank 2[%], Long Term Fuel Trim Bank 1[%], Long Term Fuel Trim Bank 2[%] - Notes: Each combination of VehID, Trip is unique. DayNum represents elapsed days since a reference date. (DayNum 1 = Nov, 1st, 2017, 00:00:00, DayNum 1.5 = Nov, 1st, 2017, 12:00:00) For the details, refer to the VED paper

Static Data: "VEDStaticDataICE&HEV.xlsx", and "VEDStaticDataPHEV&EV.xlsx" - Includes parameters of all 383 vehicles (264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs) - There are 3 pure EV vehicles in the dataset. All of them are 2013 Nissan Leaf with an advertised battery capacity of 24 kWh. - Columns represent: VehId, EngineType, Vehicle Class, Engine Configuration & Displacement Transmission, Drive Wheels, Generalized_Weight[lb]

License

License under the Apache License 2.0

Owner

  • Name: GS Oh
  • Login: gsoh
  • Kind: user

Software Engineer @ Google. During my Ph.D., I worked on probabilistic ML models for AI applications (autonomous driving, generative models, sequence models).

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