Budi Darma Setiawan, Mate Kovacs, Uwe Serdult, Victor Kryssanov
Improving road safety is one of the critical issues for road maintenance and management. Motion sensors embedded in smartphones to sense vibrations can be used to detect rough road surfaces when carried in moving vehicles. Finding segments in the signal which reflect the condition of the road surface, however, is a challenging task. This study proposes a modified U-Net architecture with integrated bidirectional Long Short-Term Memory layers to perform semantic segmentation on smartphone motion sensor data for road surface classification. Experiments show that using z-axis accelerometer and z-axis gyroscope features, the proposed method outperforms multiple existing semantic segmentation algorithms. © 2022 Elsevier B.V.. All rights reserved.
Graduate School of Information Science, Engineering Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, 525-0058, Japan; Faculty of Computer Science Brawijaya University, Jalan Veteran no.8, Malang, 65145, Indonesia; College of Information Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, 525-0058, Japan; Center for Democracy Studies Aarau (ZDA), The University of Zurich, Kuttigerstrasse 21, Aarau, 5000, Switzerland
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