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Road anomaly detection using a dynamic sliding window technique

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Abstract

The need to use roads is vital. In reality, smooth asphalt roads help people in their daily lives by saving time, avoiding traffic, and preserving the means of transportation. Recently, road anomaly detection using smartphone sensors such as accelerometers, gyroscopes, and GPS has become an important topic in the field of Intelligent Transportation Systems (ITS). In this context, many solutions have been proposed using Static Sliding Window (SSW), which is based on fixed window length. However, in the real world, the window length of the anomaly changes according to the speed value and the anomaly width, which is considered as a major drawback of SSW. In this paper, we propose a new technique called Dynamic Sliding Window (DSW), which aims to improve the quality of road anomaly detection by preprocessing the accelerometer signal. The proposed technique is applied to the same dataset and under the same conditions as the SSW. To cover all scenarios, thirty different virtual roads and several types of anomalies (speed bumps, metal bumps, and potholes) were used as training and test data. The resulting outputs of the DSW and SSW have been used by seven heuristic algorithms proposed by previous researchers and seven classifiers based on twelve feature detectors. The obtained results using the proposed DSW have been compared to those obtained using the SSW to demonstrate the efficiency of the former. Indeed, based on the comparison, the proposed DSW has proven its potential to outperform all previous road anomaly detection methods.

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Notes

  1. http://www.accelerometer.xyz/pothole_lab.

  2. https://www.accelerometer.xyz/datasets.

  3. http://scikit-learn.org/stable.

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Authors and Affiliations

  1. Ecole Militaire Polytechnique, PO Box 17, Bordj El Bahri, Algiers, Algeria

    Noureddine Chibani, Faouzi Sebbak, Walid Cherifi & Khadidja Belmessous

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  1. Noureddine Chibani
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  2. Faouzi Sebbak
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Correspondence to Faouzi Sebbak.

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Chibani, N., Sebbak, F., Cherifi, W. et al. Road anomaly detection using a dynamic sliding window technique. Neural Comput & Applic 34, 19015–19033 (2022). https://doi.org/10.1007/s00521-022-07436-6

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