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A Machine Learning Pipeline to Automatically Identify and Classify Roadway Surface Disruptions.

, , , and . ENC, page 3:1-3:4. ACM, (2016)

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Becoming Smarter at Characterizing Potholes and Speed Bumps from Smartphone Data - Introducing a Second-Generation Inference Problem., , , , and . IEEE Trans. Mob. Comput., 20 (2): 366-376 (2021)Learning Roadway Surface Disruption Patterns Using the Bag of Words Representation., , , , and . IEEE Trans. Intell. Transp. Syst., 18 (11): 2916-2928 (2017)A Machine Learning Approach for Smartphone-based Sensing of Roads and Driving Style.. CoRR, (2019)A Machine Learning Pipeline to Automatically Identify and Classify Roadway Surface Disruptions., , , and . ENC, page 3:1-3:4. ACM, (2016)Are Android Smartphones Ready to Locally Execute Intelligent Algorithms?, , , and . MICAI (2), volume 10062 of Lecture Notes in Computer Science, page 15-25. Springer, (2016)The Citizen Road Watcher - Identifying Roadway Surface Disruptions Based on Accelerometer Patterns., , and . UCAmI, volume 8276 of Lecture Notes in Computer Science, page 374-377. Springer, (2013)Evaluating Reorientation Strategies for Accelerometer Data from Smartphones for ITS Applications., , , and . UCAmI (2), volume 10070 of Lecture Notes in Computer Science, page 407-418. (2016)