This dataset is a collection of measurements performed with smartphone inertial sensors while a vehicle was driven and a pothole, speed bump, or line of metal speed bumps were passed. The depths of potholes were recorded, in cm, while the functional condition (OK, not OK) was registered for speed bumps and metal bumps. It was used in this paper.
If you use this dataset in your own work, please cite us as follows:
M. R. Carlos and L. C. González and J. Wahlström and R. Cornejo and F. Martínez, "Becoming smarter at characterizing potholes and speed bumps from smartphone data - Introducing a second-generation inference problem," in IEEE Transactions on Mobile Computing., , . doi: 10.1109/TMC.2019.2947443
@article{carlos2019,
author={M. R. {Carlos} and L. C. {González} and J. {Wahlstrom} and R. {Cornejo} and F. {Martínez}},
journal={IEEE Transactions on Mobile Computing},
title={Becoming smarter at characterizing potholes and speed bumps from smartphone data - Introducing a second-generation inference problem},
year={2019},
volume={},
number={},
pages={},
doi={10.1109/TMC.2019.2947443},
ISSN={},
month={},
}
This dataset consists of triaxial acceleration time series, sampled at 50 Hz, with tagged examples of aggressive driving maneuvers. It was used in this paper.
Download data (a hdf5 file containing acceleration samples and annotations)
If you use this dataset in your own work, please cite us as follows:
M. R. Carlos and L. C. González and J. Wahlström and G. Ramírez and F. Martínez and G. Runger, "How Smartphone Accelerometers Reveal Aggressive Driving Behavior?--The Key Is the Representation," in IEEE Transactions on Intelligent Transportation Systems, , . doi: 10.1109/TITS.2019.2926639
@article{carlos2019a,
author={M. R. {Carlos} and L. C. {González} and J. {Wahlström} and G. {Ramírez} and F. {Martínez} and G. {Runger}},
journal={IEEE Transactions on Intelligent Transportation Systems},
title={How Smartphone Accelerometers Reveal Aggressive Driving Behavior?--The Key Is the Representation},
year={2019},
volume={},
number={},
pages={1-11},
keywords={Accelerometers;Sensors;Acceleration;Vehicles;Machine learning;Data models;Proposals;Driving analytics;aggressive driving;driving behavior;bag of words;insurance telematics.},
doi={10.1109/TITS.2019.2926639},
ISSN={},
month={},
}
This dataset consists of vertical acceleration time series, sampled at 50 Hz, with several annotated road anomalies. It was used in this paper.
Download data (JSON files containing acceleration samples and annotations)
If you use this dataset in your own work, please cite us as follows:
M. R. Carlos, M. E. Aragón, L. C. González, H. J. Escalante and F. Martínez, "Evaluation of Detection Approaches for Road Anomalies Based on Accelerometer Readings--Addressing Who's Who," in IEEE Transactions on Intelligent Transportation Systems, 19(10), 3334-3343. doi: 10.1109/TITS.2017.2773084
@article{carlos2018,
author={M. R. {Carlos} and M. E. {Aragón} and L. C. {González} and H. J. {Escalante} and F. {Martínez}},
journal={IEEE Transactions on Intelligent Transportation Systems},
title={Evaluation of Detection Approaches for Road Anomalies Based on Accelerometer Readings—AddressingWho’s Who},
year={2018},
volume={19},
number={10},
pages={3334-3343},
doi={10.1109/TITS.2017.2773084},
ISSN={1524-9050},
month={Oct},
}
For acceleration samples containing several anomalies, check Pothole Lab:
If you use this dataset in your own work, please cite it as follows:
J. R. López, L. C. González, J. Wahlström, M. Montes y Gómez, L. Trujillo, and G. Ramírez-Alonso. "A Genetic Programming Approach for Driving Score Calculation in the Context of Intelligent Transportation Systems," in IEEE Sensors Journal, 18(17), 7183-7192. doi: 10.1109/JSEN.2018.2856112
@article{lopez2018,
author={J. R. {López} and L. C. {González} and J. {Wahlström} and M. {Montes y Gómez} and L. {Trujillo} and G. {Ramírez-Alonso}},
journal={IEEE Sensors Journal},
title={A Genetic Programming Approach for Driving Score Calculation in the Context of Intelligent Transportation Systems},
year={2018},
volume={18},
number={17},
pages={7183-7192},
doi={10.1109/JSEN.2018.2856112},
ISSN={1530-437X},
month={Sep.},
}
This dataset consists of 500 vertical acceleration time series, sampled at 50 Hz, 100 for each of five categories of road anomalies: asphalt bumps, metal bumps, potholes, regular road and worn-out road segments. We used a Bag of Words strategy to detect and classify these different types of anomalies in this paper.
Download data (plain CSV files in one ZIP)
If you use this dataset in your own work, please cite us as follows:
L. C. González, R. Moreno, H. J. Escalante, F. Martínez and M. R. Carlos, "Learning Roadway Surface Disruption Patterns Using the Bag of Words Representation," in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 11, pp. 2916-2928, Nov. 2017. doi: 10.1109/TITS.2017.2662483
@article{gonzalez2017,
author={L. C. González and R. Moreno and H. J. Escalante and F. Martínez and M. R. Carlos},
journal={IEEE Transactions on Intelligent Transportation Systems},
title={Learning Roadway Surface Disruption Patterns Using the Bag of Words Representation},
year={2017},
volume={18},
number={11},
pages={2916-2928},
doi={10.1109/TITS.2017.2662483},
ISSN={1524-9050},
month={Nov},
}
Other datasets will be available upon publication.
If you have any questions, contact us at:
lcgonzalez@uach.mx
http://gonzalezgurrola.ml