Overview
- The book gives strong motivation for applying smartphone sensing technology in sensing vehicle dynamics and driving behaviors, which provides readers new direction for improving driving safety and convenience
- The book provides varies approaches for digging sensor readings of smartphones to sense vehicle dynamics and driving behaviors, which may give readers new ideas for processing similar data.
- All methods described in this book are implemented as Android Apps, which can be useful for readers who wants to turn the methods to products
- All approaches described in this book involves month-level data collection and performance evaluation in real driving environments, which can be meaningful to readers who cares about the generalization capability of proposed approaches
Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)
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Table of contents (6 chapters)
Keywords
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.
Authors and Affiliations
Bibliographic Information
Book Title: Sensing Vehicle Conditions for Detecting Driving Behaviors
Authors: Jiadi Yu, Yingying Chen, Xiangyu Xu
Series Title: SpringerBriefs in Electrical and Computer Engineering
DOI: https://doi.org/10.1007/978-3-319-89770-7
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s), under exclusive licence to Springer International Publishing AG, part of Springer Nature 2018
Softcover ISBN: 978-3-319-89769-1Published: 26 April 2018
eBook ISBN: 978-3-319-89770-7Published: 18 April 2018
Series ISSN: 2191-8112
Series E-ISSN: 2191-8120
Edition Number: 1
Number of Pages: VIII, 75
Number of Illustrations: 1 b/w illustrations, 36 illustrations in colour
Topics: Mobile Computing, Communications Engineering, Networks