Sensing Vehicle Conditions for Detecting Driving Behaviors (eBook)
VIII, 75 Seiten
Springer International Publishing (Verlag)
978-3-319-89770-7 (ISBN)
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.
Preface 6
Contents 8
1 Overview 10
1.1 Brief Introduction of Smartphone Sensing 10
1.1.1 Representative Sensors Embedded in Smartphones 10
1.1.2 Development of Smartphone Sensing 11
1.2 Smartphone Sensing in Vehicles 12
1.3 Overview of the Book 13
2 Sensing Vehicle Dynamics with Smartphones 15
2.1 Introduction 15
2.2 Pre-processing Sensor Readings 16
2.2.1 Coordinate Alignment 16
2.2.2 Data Filtering 18
2.3 Sensing Basic Vehicle Dynamics 19
2.3.1 Sensing Movement of Vehicles 19
2.3.2 Sensing Driving on Uneven Road 20
2.3.3 Sensing Turning of Vehicles 21
2.3.4 Sensing Lane-Changes of Vehicles 22
2.3.4.1 Identifying Single Lane-Change 22
2.3.4.2 Identifying Sequential Lane-Change 23
2.3.5 Estimating Instant Speed of Vehicles 25
2.4 Evaluation 28
2.4.1 Setup 28
2.4.2 Metrics 28
2.4.3 Performance of Sensing Vehicle Dynamics 29
2.4.4 Performance of Sensing Lane-Change 29
2.4.5 Performance of Sensing Instance Speed 30
2.5 Conclusion 31
3 Sensing Vehicle Dynamics for Abnormal Driving Detection 32
3.1 Introduction 32
3.2 Driving Behavior Characterization 35
3.2.1 Collecting Data from Smartphone Sensors 35
3.2.2 Analyzing Patterns of Abnormal Driving Behaviors 36
3.3 System Design 37
3.3.1 Overview 37
3.3.2 Extracting and Selecting Effective Features 39
3.3.2.1 Feature Extraction 39
3.3.2.2 Feature Selection 39
3.3.3 Training a Fine-Grained Classifier Model to Identify Abnormal Driving Behaviors 40
3.3.4 Detecting and Identifying Abnormal Driving Behaviors 42
3.4 Evaluations 44
3.4.1 Setup 44
3.4.2 Metrics 45
3.4.3 Overall Performance 45
3.4.3.1 Total Accuracy 45
3.4.3.2 Detecting the Abnormal vs. the Normal 46
3.4.3.3 Identifying Abnormal Driving Behaviors 46
3.4.4 Impact of Training Set Size 47
3.4.5 Impact of Traffic Conditions 48
3.4.6 Impact of Road Type 48
3.4.7 Impact of Smartphone Placement 49
3.5 Conclusion 50
4 Sensing Driver Behaviors for Early Recognition of Inattentive Driving 51
4.1 Introduction 51
4.2 Inattentive Driving Events Analysis 52
4.2.1 Defining Inattentive Driving Events 53
4.2.2 Analyzing Patterns of Inattentive Driving Events 54
4.3 System Design 56
4.3.1 System Overview 56
4.3.2 Model Training at Offline Stage 57
4.3.2.1 Establishing Training Dataset 57
4.3.2.2 Extracting Effective Features 57
4.3.2.3 Training a Multi-Classifier 58
4.3.2.4 Setting Up Gradient Model Forest for Early Recognition 60
4.3.3 Recognizing Inattentive Driving Events at Online Stage 62
4.3.3.1 Segmenting Frames Through Sliding Window 62
4.3.3.2 Detecting Inattentive Driving Events at Early Stage 63
4.4 Evaluation 64
4.4.1 Setup 64
4.4.2 Metrics 64
4.4.3 Overall Performance 65
4.4.3.1 Total Accuracy 65
4.4.3.2 Recognizing Inattentive Driving Events 66
4.4.3.3 Realizing Early Recognition 66
4.4.4 Impact of Training Set Size 67
4.4.5 Impact of Road Types and Traffic Conditions 68
4.4.6 Impact of Smartphone Placement 69
4.5 Conclusion 69
5 State-of-Art Researches 71
5.1 Smartphone Sensing Researches 71
5.2 Vehicle Dynamics Sensing Researches 72
5.3 Driver Behaviors Detection Researches 73
5.4 Common Issues 74
6 Summary 75
6.1 Conclusion of the Book 75
6.2 Future Directions 76
References 77
| Erscheint lt. Verlag | 18.4.2018 |
|---|---|
| Reihe/Serie | SpringerBriefs in Electrical and Computer Engineering | SpringerBriefs in Electrical and Computer Engineering |
| Zusatzinfo | VIII, 75 p. 37 illus., 36 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
| Technik ► Nachrichtentechnik | |
| Schlagworte | Abnormal Driving • acoustic signals • driving behaviors • early recognition • feature extraction • Inattentive Driving • machine learning • motion sensors • Pattern Recognitioin • smartphone sensing • Vehicle Dynamics |
| ISBN-10 | 3-319-89770-5 / 3319897705 |
| ISBN-13 | 978-3-319-89770-7 / 9783319897707 |
| Haben Sie eine Frage zum Produkt? |
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