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arXiv · 2302.12720

Surface Recognition for e-Scooter Using Smartphone IMU Sensor

Areej Eweida·Nimord Segol·Maxim Freydin·Niv Sfaradi·Barak Or
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Abstract

In recent years, as the use of micromobility gained popularity, technological challenges connected to e-scooters became increasingly important. This paper focuses on road surface recognition, an important task in this area. A reliable and accurate method for road surface recognition can help improve the safety and stability of the vehicle. Here a data-driven method is proposed to recognize if an e-scooter is on a road or a sidewalk. The proposed method uses only the widely available inertial measurement unit (IMU) sensors on a smartphone device. deep neural networks (DNNs) are used to infer whether an e-scooteris driving on a road or on a sidewalk by solving a binary classification problem. A data set is collected and several different deep models as well as classical machine learning approaches for the binary classification problem are applied and compared. Experiment results on a route containing the two surfaces are presented demonstrating the DNNs ability to distinguish between them.

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Areej Eweida, Nimord Segol, Maxim Freydin, Niv Sfaradi, Barak Or. 2023-01-19. Surface Recognition for e-Scooter Using Smartphone IMU Sensor. https://doi.org/10.1109/icsip57908.2023.10270978

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