| 摘要 | 第4-6页 |
| ABSTRACT | 第6-7页 |
| CHAPTER 1: INTRODUCTION | 第11-29页 |
| 1.1 The significance and context of research | 第11-13页 |
| 1.1.1 The context of research | 第11-12页 |
| 1.1.2 The significance of the research | 第12-13页 |
| 1.2 Analyze related studies | 第13-23页 |
| 1.2.1 Base on physiological features of driver to determine driver’s fatigue | 第13-16页 |
| 1.2.2 Base on driver’s behavior | 第16-20页 |
| 1.2.3 Base on vehicle state information | 第20-21页 |
| 1.2.4 Based on information fusion technology | 第21-23页 |
| 1.3 Assess the study situation at home and abroad | 第23-24页 |
| 1.4 The content of study | 第24-29页 |
| CHAPTER 2: TEST DESIGN AND DATA COLLECTION | 第29-47页 |
| 2.1 The classification of lane departure and overview of driving fatigue | 第29-33页 |
| 2.1.1 The classification of lane departure | 第29-30页 |
| 2.1.2 Overview on fatigue driving | 第30-32页 |
| 2.1.3 The relationship between driving behavior and fatigue driving | 第32-33页 |
| 2.2 Test design | 第33-39页 |
| 2.2.1 Test purposes | 第34页 |
| 2.2.2 Test parameters | 第34页 |
| 2.2.3 Test subjects | 第34-35页 |
| 2.2.4 Test equipment | 第35-37页 |
| 2.2.5 Test procedure | 第37-39页 |
| 2.3 Confirmation of recognition time window of fatigue lane departure and sample selection | 第39-45页 |
| 2.3.1 Determine start time of fatigue lane departure | 第39-41页 |
| 2.3.2 Confirm recognition time window of fatigue lane departure | 第41-45页 |
| 2.4 Chapter summary | 第45-47页 |
| CHAPTER 3: DRIVING BEHAVIOR CHARACTERISTIC ANALYSIS OF FATIGUELANE DEPARTURE | 第47-65页 |
| 3.1 Analyzing characteristics of driver’s operating behavior | 第47-57页 |
| 3.1.1 Steering wheel angle | 第47-54页 |
| 3.1.2 Steering wheel angle velocity (SWAV) | 第54-57页 |
| 3.2 Analyze characteristics of vehicle’s motion state | 第57-63页 |
| 3.2.1 Lateral acceleration (LA) | 第57-59页 |
| 3.2.2 Yaw velocity (YV) | 第59-61页 |
| 3.2.3. Lateral location of vehicle (LLV) | 第61-62页 |
| 3.2.4 Average lateral velocity of vehicle (ALV) | 第62-63页 |
| 3.3 Chapter summary | 第63-65页 |
| CHAPTER 4: FATIGUE LANE DEPARTURE RECOGNITION BASED ON GM-HMM554.1 Pattern recognition (PR) overview | 第65-95页 |
| 4.1 Pattern recognition (PR) overview | 第65-67页 |
| 4.2 Hidden Markov Model Basic Idea | 第67-84页 |
| 4.2.1 The basic structure of Hidden Markov Model | 第67-70页 |
| 4.2.2 The three basic problems for HMMs | 第70-71页 |
| 4.2.3 Solutions for the three basic problems of HMMs | 第71-84页 |
| 4.3 Recognition model building of fatigue lane departure based on GM-HMM | 第84-91页 |
| 4.3.1 Filtration of characteristic parameters | 第84-85页 |
| 4.3.2 Select the structure of Hidden Markov Model | 第85-86页 |
| 4.3.3 Classify fatigue lane departure recognition model | 第86页 |
| 4.3.4 Training model | 第86-89页 |
| 4.3.5 Model test | 第89-91页 |
| 4.4 Model verification | 第91-92页 |
| 4.5 Chapter summary | 第92-95页 |
| CHAPTER 5: THE IMPACT OF CHARACTERISTIC PARAMETERS AND MODELBUILDING METHOD ON THE EFFECT OF RECOGNITION | 第95-111页 |
| 5.1 Recognize fatigue lane departure state based on Support Vector Machine | 第95-102页 |
| 5.1.1 Support Vector Machine’s basic idea | 第95-100页 |
| 5.1.2 Steps to recognize fatigue lane departure based on support vector machine | 第100-102页 |
| 5.2 Driver’s fatigue recognition based on Time Series Analysis of steering wheel anglevelocity (TSA) | 第102-106页 |
| 5.2.1. The experiment and data collection method | 第102-103页 |
| 5.2.2 Specific expressions of the state of fatigue | 第103-105页 |
| 5.2.3 Model test | 第105-106页 |
| 5.3 Compare the effect of GM-HMM model to the other model | 第106-110页 |
| 5.3.1. Compare GM-HMM model to support vector machine model | 第106-109页 |
| 5.3.2 Compare GM-HMM model to driver fatigue recognition model based on timeseries analysis of steering wheel angle velocity (TSA) | 第109-110页 |
| 5.4 Chapter summary | 第110-111页 |
| CHAPTER 6: CONCLUSION | 第111-115页 |
| 6.1 Work summary | 第111-113页 |
| 6.2 Innovation result | 第113页 |
| 6.3 Research prospects | 第113-115页 |
| REFERENCES | 第115-125页 |
| APPENDIX 1 | 第125-126页 |
| 作者简介及攻读博士期间所取得的科研成果 | 第126-128页 |
| ACKNOWLEDGEMENTS | 第128页 |
