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Road Surface Recognition Based on DeepSense Neural Network using Accelerometer Data

Shan Wu,Amnir Hadachi

Published 2020 in 2020 IEEE Intelligent Vehicles Symposium (IV)

ABSTRACT

Smartphones play an important role in our lives, which makes them a good sensor for perceiving our environment. Therefore, many applications have emerged using mobile sensors to solve different problems related to activity recognition, health monitoring, transportation, etc. One of the intriguing issues in transportation is mapping our road network's quality, road types, or discover unmapped roads in our road network, which is very costly to maintain and to examine. In this paper, we propose a methodology to recognize different road types by using accelerometer data of smartphones. The approach is based on DeepSense neural network with customised preprocessing and feature engineering steps. In addition, we compared our method performance against Convolutional Neural Network, Fully-connected Neural Network, Support Vector Machine, and RandomForest classifier. Our approach outperformed all four methods, and it was capable of distinguishing three road types (asphalt roads, stone roads, and off roads).

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    2020 IEEE Intelligent Vehicles Symposium (IV)

  • Publication date

    2020-10-19

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1109/IV47402.2020.9304737
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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