Sensing Vehicle Conditions for Detecting Driving Behaviors

  • Jiadi Yu
  • Yingying Chen
  • Xiangyu Xu

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Jiadi Yu, Yingying Chen, Xiangyu Xu
    Pages 1-5
  3. Jiadi Yu, Yingying Chen, Xiangyu Xu
    Pages 7-23
  4. Jiadi Yu, Yingying Chen, Xiangyu Xu
    Pages 25-43
  5. Jiadi Yu, Yingying Chen, Xiangyu Xu
    Pages 45-64
  6. Jiadi Yu, Yingying Chen, Xiangyu Xu
    Pages 65-68
  7. Jiadi Yu, Yingying Chen, Xiangyu Xu
    Pages 69-70
  8. Back Matter
    Pages 71-75

About this book


This SpringerBrief  begins by introducing the concept of smartphone sensing and summarizing the main tasks of applying smartphone sensing in vehicles. Chapter 2 describes the vehicle dynamics sensing model that exploits the raw data of motion sensors (i.e., accelerometer and gyroscope) to give the dynamic of vehicles, including stopping, turning, changing lanes, driving on uneven road, etc. Chapter 3 detects the abnormal driving behaviors based on sensing vehicle dynamics. Specifically, this brief proposes a machine learning-based fine-grained abnormal driving behavior detection and identification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using the built-in motion sensors in smartphones.

As more vehicles taking part in the transportation system in recent years, driving or taking vehicles have become an inseparable part of our daily life. However, increasing vehicles on the roads bring more traffic issues including crashes and congestions, which make it necessary to sense vehicle dynamics and detect driving behaviors for drivers. For example, sensing lane information of vehicles in real time can be assisted with the navigators to avoid unnecessary detours, and acquiring instant vehicle speed is desirable to many important vehicular applications. Moreover, if the driving behaviors of drivers, like inattentive and drunk driver, can be detected and warned in time, a large part of traffic accidents can be prevented.   However, for sensing vehicle dynamics and detecting driving behaviors, traditional approaches are grounded on the built-in infrastructure in vehicles such as infrared sensors and radars, or additional hardware like EEG devices and alcohol sensors, which involves high cost.  The authors illustrate that smartphone sensing technology, which involves sensors embedded in smartphones (including the accelerometer, gyroscope, speaker, microphone, etc.), can be applied in sensing vehicle dynamics and driving behaviors.

Chapter 4 exploits the feasibility to recognize abnormal driving events of drivers at early stage. Specifically, the authors develop an Early Recognition system, ER, which recognize inattentive driving events at an early stage and alert drivers timely leveraging built-in audio devices on smartphones. An overview of the state-of-the-art research is presented in chapter 5. Finally, the conclusions and future directions are provided in Chapter 6.


vehicle dynamics driving behaviors smartphone sensing motion sensors acoustic signals Abnormal Driving Inattentive Driving Feature Extraction Machine Learning Pattern Recognitioin Early Recognition

Authors and affiliations

  • Jiadi Yu
    • 1
  • Yingying Chen
    • 2
  • Xiangyu Xu
    • 3
  1. 1.Department of Computer Science & EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.WINLABRutgers UniversityNew BrunswickUSA
  3. 3.Department of Computer Science & EngineeringShanghai Jiao Tong UniversityShanghaiChina

Bibliographic information

  • DOI
  • Copyright Information The Author(s), under exclusive licence to Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-319-89769-1
  • Online ISBN 978-3-319-89770-7
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • Buy this book on publisher's site
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