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" Sensing vehicle conditions for detecting driving behaviors / "
Jiadi Yu, Yingying Chen, Xiangyu Xu.
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BL
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| Record Number
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866033
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| Main Entry
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Yu, Jiadi
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| Title & Author
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Sensing vehicle conditions for detecting driving behaviors /\ Jiadi Yu, Yingying Chen, Xiangyu Xu.
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| Publication Statement
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Cham :: Springer,, 2018.
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| Series Statement
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SpringerBriefs in electrical and computer engineering
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| Page. NO
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1 online resource
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| ISBN
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3319897705
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: 9783319897707
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3319897691
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9783319897691
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Includes bibliographical references.
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| Contents
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Intro; Preface; Contents; 1 Overview; 1.1 Brief Introduction of Smartphone Sensing; 1.1.1 Representative Sensors Embedded in Smartphones; 1.1.2 Development of Smartphone Sensing; 1.2 Smartphone Sensing in Vehicles; 1.3 Overview of the Book; 2 Sensing Vehicle Dynamics with Smartphones; 2.1 Introduction; 2.2 Pre-processing Sensor Readings; 2.2.1 Coordinate Alignment; 2.2.2 Data Filtering; 2.3 Sensing Basic Vehicle Dynamics; 2.3.1 Sensing Movement of Vehicles; 2.3.2 Sensing Driving on Uneven Road; 2.3.3 Sensing Turning of Vehicles; 2.3.4 Sensing Lane-Changes of Vehicles.
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2.3.4.1 Identifying Single Lane-Change2.3.4.2 Identifying Sequential Lane-Change; 2.3.5 Estimating Instant Speed of Vehicles; 2.4 Evaluation; 2.4.1 Setup; 2.4.2 Metrics; 2.4.3 Performance of Sensing Vehicle Dynamics; 2.4.4 Performance of Sensing Lane-Change; 2.4.5 Performance of Sensing Instance Speed; 2.5 Conclusion; 3 Sensing Vehicle Dynamics for Abnormal Driving Detection; 3.1 Introduction; 3.2 Driving Behavior Characterization; 3.2.1 Collecting Data from Smartphone Sensors; 3.2.2 Analyzing Patterns of Abnormal Driving Behaviors; 3.3 System Design; 3.3.1 Overview.
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3.3.2 Extracting and Selecting Effective Features3.3.2.1 Feature Extraction; 3.3.2.2 Feature Selection; 3.3.3 Training a Fine-Grained Classifier Model to Identify Abnormal Driving Behaviors; 3.3.4 Detecting and Identifying Abnormal Driving Behaviors; 3.4 Evaluations; 3.4.1 Setup; 3.4.2 Metrics; 3.4.3 Overall Performance; 3.4.3.1 Total Accuracy; 3.4.3.2 Detecting the Abnormal vs. the Normal; 3.4.3.3 Identifying Abnormal Driving Behaviors; 3.4.4 Impact of Training Set Size; 3.4.5 Impact of Traffic Conditions; 3.4.6 Impact of Road Type; 3.4.7 Impact of Smartphone Placement; 3.5 Conclusion.
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4 Sensing Driver Behaviors for Early Recognition of Inattentive Driving4.1 Introduction; 4.2 Inattentive Driving Events Analysis; 4.2.1 Defining Inattentive Driving Events; 4.2.2 Analyzing Patterns of Inattentive Driving Events; 4.3 System Design; 4.3.1 System Overview; 4.3.2 Model Training at Offline Stage; 4.3.2.1 Establishing Training Dataset; 4.3.2.2 Extracting Effective Features; 4.3.2.3 Training a Multi-Classifier; 4.3.2.4 Setting Up Gradient Model Forest for Early Recognition; 4.3.3 Recognizing Inattentive Driving Events at Online Stage; 4.3.3.1 Segmenting Frames Through Sliding Window.
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4.3.3.2 Detecting Inattentive Driving Events at Early Stage4.4 Evaluation; 4.4.1 Setup; 4.4.2 Metrics; 4.4.3 Overall Performance; 4.4.3.1 Total Accuracy; 4.4.3.2 Recognizing Inattentive Driving Events; 4.4.3.3 Realizing Early Recognition; 4.4.4 Impact of Training Set Size; 4.4.5 Impact of Road Types and Traffic Conditions; 4.4.6 Impact of Smartphone Placement; 4.5 Conclusion; 5 State-of-Art Researches; 5.1 Smartphone Sensing Researches; 5.2 Vehicle Dynamics Sensing Researches; 5.3 Driver Behaviors Detection Researches; 5.4 Common Issues; 6 Summary; 6.1 Conclusion of the Book.
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| Abstract
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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.
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| Subject
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Driver assistance systems.
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Intelligent transportation systems.
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BUSINESS ECONOMICS-- Industries-- Transportation.
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Communications engineering-- telecommunications.
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Driver assistance systems.
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Intelligent transportation systems.
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Mobile handheld device programming-- Apps programming.
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TRANSPORTATION-- Public Transportation.
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| Dewey Classification
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388.3/12
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| LC Classification
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TE228.3
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| Added Entry
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Chen, Yingying,Dr
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Xu, Xiangyu
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