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Smartphone Sensors-Based Abnormal Driving Behaviors Detection: Serial-Feature Network

机译：智能手机传感器的异常驾驶行为检测：串行特征网络

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摘要

One of the important factors leading to traffic accidents is the abnormal driving behavior of drivers. Early detection of abnormal driving behaviors can effectively reduce the occurrence of traffic accidents. At present, most of the main stream driving behavior detection methods are based on the data of a single moment, which separates the continuity of driving behavior. In this article, a driving behavior recognition algorithm based on Serial-Feature Network (SF-Net) and smart phone inertial sensor is proposed, which fully considers the continuity of driving events and uses adjacent multi time data to identify driving status. The data used in this article are collected from GPS data, 3-axis acceleration and gyroscope data of smart phone. Through the preprocessing operation, SF-netmakes the input vector not only contain the current sensor data, but also fuse the relevant information of adjacent time. In SF-net, deep convolution neural network is used for feature extracting, and 10 different driving behaviors can be identified by fusing multi-level and multi-time feature information. The field test results show that the accuracy rate of the serial feature network is 97.1%, and the recall rate is 98.4%, which is better than other test network models. When the number of training samples is small, the sequential feature network can still maintain a high recognition rate, and the network model is relatively stable.
机译：导致交通事故的重要因素之一是司机的异常驾驶行为。早期检测异常驾驶行为可以有效地减少交通事故的发生。目前，大多数主流驾驶行为检测方法基于单个时刻的数据，其分离驾驶行为的连续性。在本文中，提出了一种基于串行特征网络（SF-NET）和智能手机惯性传感器的驾驶行为识别算法，其完全考虑了驱动事件的连续性，并使用相邻的多时间数据来识别驾驶状态。本文中使用的数据从GPS数据，3轴加速度和智能手机的陀螺数据收集。通过预处理操作，SF-NetMakes输入载体不仅包含当前传感器数据，还融合了相邻时间的相关信息。在SF-NET中，深卷积神经网络用于特征提取，可以通过融合多级和多次特征信息来识别10种不同的驾驶行为。现场测试结果表明，串行特征网络的精度率为97.1％，召回率为98.4％，比其他测试网络模型更好。当训练样本的数量小时，顺序特征网络仍然可以保持高识别率，并且网络模型相对稳定。

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  • 来源
    《IEEE sensors journal》 |2021年第14期|15719-15728|共10页
  • 作者

    Wang Renjia; Xie Fei; Zhao Jing; Zhang Bin; Sun Rui; Yang Jiquan;

  • 作者单位

    Nanjing Normal Univ Sch Elect & Automat Engn Nanjing 210023 Peoples R China|NARI Grp Corp Nanjing 211000 Peoples R China;

    Nanjing Normal Univ Sch Elect & Automat Engn Nanjing 210023 Peoples R China|Nanjing Inst Intelligent High End Equipment Ind L Nanjing 210023 Peoples R China;

    Nanjing Univ Posts & Telecommun Coll Automat & Artificial Intelligence Nanjing 210023 Peoples R China;

    NARI Grp Corp Nanjing 211000 Peoples R China;

    Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 210016 Peoples R China|Imperial Coll London Ctr Transport Studies London SW7 2AZ England;

    Nanjing Normal Univ Sch Elect & Automat Engn Nanjing 210023 Peoples R China|Nanjing Inst Intelligent High End Equipment Ind L Nanjing 210023 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Abnormal driving behaviors; CNN; intelligent transportation.;

    机译：异常驾驶行为;CNN;智能交通。;

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