This dataset was named by a subset of the authors as "The I-24 Trajectory Dataset" and is given a subsequent date in the title of this node to distinguish it from ongoing trajectory data gathered by I-24 Motion and other experiments.The dataset was created by recording CAN and GPS data from a single vehicle driving on I-24. The dataset includes Time, Velocity, Acceleration, Space Gap, Lateral Distance, Relative Velocity, Longitude GPS, Latitude GPS, Score, TrackID, L_Approach, R_Approach, L_Adjacent, and R_Adjacent. The time feature was taken from recorded GPS data recorded at 10 Hz. Data feat
Submitted by Jonathan Sprinkle on May 13th, 2024
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Minitest 12
Validation of the PID controller used by the custom cruise control system. This is described in the paper linked at http://dx.doi.org/10.1109/DI-CPS56137.2022.00013 and is used in that paper in Section VII (Test 6), and is referenced elsewhere in CIRCLES research as Minitest 12. M. Bunting, R. Bhadani, M. Nice, S. Elmadani and J. Sprinkle, "Data from the Development Evolution of a Vehicle for Custom Control," 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Milan, Italy, 2022, pp. 40-46, doi: 10.1109/DI-CPS56137.2022.00013.This da
Submitted by Jonathan Sprinkle on May 13th, 2024
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Minitest 11
Data from applying step inputs to acceleration control of a Toyota for system characterization. This is described in the paper linked at http://dx.doi.org/10.1109/DI-CPS56137.2022.00013 and is used in that paper in Section VII (Test 5), and is referenced elsewhere in CIRCLES research as Minitest 11. M. Bunting, R. Bhadani, M. Nice, S. Elmadani and J. Sprinkle, "Data from the Development Evolution of a Vehicle for Custom Control," 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Milan, Italy, 2022, pp. 40-46, doi: 10.1109/DI-CPS561
Submitted by Jonathan Sprinkle on May 13th, 2024
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Minitest 7
This data corresponds to a vehicle driving on mainly long flat roads to determine energy consumption. This is described in the paper linked at http://dx.doi.org/10.1109/DI-CPS56137.2022.00013 and is used in that paper in Section VI (Test 4), and is referenced elsewhere in CIRCLES research as Minitest 7. M. Bunting, R. Bhadani, M. Nice, S. Elmadani and J. Sprinkle, "Data from the Development Evolution of a Vehicle for Custom Control," 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Milan, Italy, 2022, pp. 40-46, doi: 10.1109/DI-CP
Submitted by Jonathan Sprinkle on May 13th, 2024
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Minitest 6
This data corresponds to CAN-data for testing cruise controller state changes from user-interaction. The experiment involved modifications by the user on a Toyota Rav4 vehicle. This is described in the paper linked at http://dx.doi.org/10.1109/DI-CPS56137.2022.00013 and is used in that paper in Section V (Test 3).M. Bunting, R. Bhadani, M. Nice, S. Elmadani and J. Sprinkle, "Data from the Development Evolution of a Vehicle for Custom Control," 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Milan, Italy, 2022, pp. 40-46, doi: 10.1109/
Submitted by Jonathan Sprinkle on May 13th, 2024
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Minitest 4
CAN and GPS data from a controlled platoon experiment with 3 vehicles, to verify radar data as part of overhead camera information. This is described in the paper linked at http://dx.doi.org/10.1109/DI-CPS56137.2022.00013 and is used in that paper in Section IV (Test 2), and is referenced elsewhere in CIRCLES research as Minitest 4. M. Bunting, R. Bhadani, M. Nice, S. Elmadani and J. Sprinkle, "Data from the Development Evolution of a Vehicle for Custom Control," 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Milan, Italy, 2022,
Submitted by Jonathan Sprinkle on May 13th, 2024
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Minitest 1
CAN and GPS data from a controlled platoon experiment to verify radar data. This is described in the paper linked at http://dx.doi.org/10.1109/DI-CPS56137.2022.00013 and is used in that paper in Section III (Test 1). M. Bunting, R. Bhadani, M. Nice, S. Elmadani and J. Sprinkle, "Data from the Development Evolution of a Vehicle for Custom Control," 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS), Milan, Italy, 2022, pp. 40-46, doi: 10.1109/DI-CPS56137.2022.00013.
Submitted by Jonathan Sprinkle on May 12th, 2024
The Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) project aims to reduce instabilities in traffic flow, called "phantom jams," that cause congestion and wasted energy. If you have ever encountered a temporary traffic jam for no apparent reason, this might have been a phantom jam that occurred naturally because of human driving behavior.
Prior work on closed-course testing demonstrated that phantom jams can be reduced using autonomous vehicle technologies and specially-designed algorithms. The CIRCLES project seeks to extend this technology to real-world traffic, where reducing these negative traffic effects could provide ≥10% energy savings.
In 2022, the CIRCLES team conducted the largest open-road traffic experiment of CAVs designed for wave smoothing, in Nashville, TN. The resulting experiment produced news articles with an audience reach of over 1 Billion.
Please visit the CIRCLES website for a comprehensive description of the project, its scope and scale, and resulting data, videos and other products.
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US Department of Energy
Submitted by Jonathan Sprinkle on May 12th, 2024