一種新的基於高斯混合模型之行為塑模方法用於智慧型手錶之駕駛者識別

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姓名

楊青翰(Ching-Han Yang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

一種新的基於高斯混合模型之行為塑模方法用於智慧型手錶之駕駛者識別
(A Novel GMM-Based Behavioral Modeling Approach for Smartwatch-Based Driver Recognition)

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摘要(中)

每位駕駛者都有屬於他們獨特的駕駛習慣，且通常在不同駕駛情境下，同一位駕駛者握住或操作方向盤的方式也不同。在本研究中，我們提出一種新的基於高斯混合模型塑模方法，此塑模方法可以改善傳統高斯混合模型在駕駛行為塑模上的問題。此外，我們提出的塑模方法可以應用在建構較佳的智慧型手錶感測器(如加速度計、方位感測器)之駕駛者認證系統或駕駛者識別系統上。為了驗證我們提出的方法之可行性，我們建構兩個實驗系統，分別為駕駛者認證系統與駕駛者識別系統。對於駕駛者認證系統的實驗結果顯示，在模擬環境中相等錯誤率(Equal Error Rate)可達4.46%；在真實駕駛環境中相等錯誤率達可11.35%。對於駕駛者識別系統而言，實驗結果顯示，在模擬環境中識別率可達87.16%；在真實環境中識別率可達73.07%。上述實驗結果皆比傳統高斯混合模型方法佳，因此，可以證實我們提出新的基於高斯混合模型塑模方法具有可用性。

摘要(英)

All drivers have their own distinct driving habits, and usually hold and operate the steering wheel differently in different driving scenarios. In this study, we proposed a novel Gaussian mixture model (GMM)-based method that can improve the traditional GMM in modeling driving behavior. This new method can be applied to build a better driver authentication or identification system based on the accelerometer and orientation sensor of a smartwatch. To demonstrate the feasibility of the proposed method, we created two experimental systems that analyzes driving behavior using the built-in sensors of a smartwatch. The experimental results for driver authentication—an equal error rate (EER) of 4.46% in the simulated environment and an EER of 11.35% in the real-driving environment—confirm the feasibility of this approach. For driver identification, the experimental results indicated that the proposed approach had identification rates of 87.16% in a simulated environment and 73.07% in a real-driving environment.

關鍵字(中)

★ 加速感測器
★ 駕駛者認證
★ 駕駛者識別
★ 高斯混合模型
★ 方位感測器
★ 智慧型手錶

關鍵字(英)

★ Accelerometer sensor
★ Driver authentication
★ Driver identification
★ Gaussian mixture model
★ Orientation sensor
★ Smartwatch

論文目次

摘要 i
Abstract ii
Table of Contents iii
List of Figures v
List of Tables vii
1. Introduction 1
2. Related Work 5
2.1 Driver Recognition by Driving Behavior 5
2.2 Gaussian Mixture Model 9
2.3 Support Vector Machine 10
2.4 Stacked Generalization 11
3. Data Collection Environments and Apparatus 13
3.1 The Simulated Environment 13
3.2 The Real-driving Environment 14
3.3 Apparatus 15
4. The Proposed Driver Behavior Modeling Approach and Recognition Mechanism 16
4.1 Data Preprocessing 17
4.2 Feature Extraction 22
4.2.1 Base Learner 1: SVM Based on the IDM Log-
likelihoods 22
4.2.2 Base Learner 2: SVM Based on Posterior
Probabilities of Gaussian Components of
the UDM 23
4.3 Proposed Driver Behavior Model 26
4.4 Smartwatch-based Driver Authentication and
Identification Mechanisms 27
4.4.1 Application Scenario 29
5. Experiments and Discussion 31
5.1 Experimental Setups 32
5.1.1 Data Acquisition 32
5.1.2 Evaluation and Performance Indices 33
5.2 Experimental Results 37
5.2.1 Experiment 1: Analysis of the Number of
Gaussian Components 37
5.2.2 Experiment 2: Weight Analysis of Each
Feature for Base Learner SIDM 38
5.2.3 Experiment 3: Performance Evaluation of
Driver Authentication in Both Environments40
5.2.4 Experiment 4: Performance Evaluation of
Driver Identification in Both Environments43
5.3 Discussion 45
6. Conclusions and Future Works 47
Appendix 49
Bibliography 51

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指導教授

梁德容張欽圳(Deron Liang Chin-Chun Chang)

審核日期

2018-6-25

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