Surface Recognition for e-Scooter Using Smartphone IMU Sensor
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866913563016167424 |
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| author | Eweida, Areej Segol, Nimord Freydin, Maxim Sfaradi, Niv Or, Barak |
| author_facet | Eweida, Areej Segol, Nimord Freydin, Maxim Sfaradi, Niv Or, Barak |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_12720 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Surface Recognition for e-Scooter Using Smartphone IMU Sensor Eweida, Areej Segol, Nimord Freydin, Maxim Sfaradi, Niv Or, Barak Signal Processing Computer Vision and Pattern Recognition 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. |
| title | Surface Recognition for e-Scooter Using Smartphone IMU Sensor |
| topic | Signal Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2302.12720 |