駕駛者心智負荷之動態評估｜國立清華大學博碩士論文庫

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研究生：	林品君 Ping-Chun Lin
論文名稱：	駕駛者心智負荷之動態評估 The Dynamic Measurement of Driver Mental Workload
指導教授：	黃雪玲 Sheue-Ling Hwang
口試委員:
學位類別：	碩士 Master
系所名稱：	工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management
論文出版年：	2006
畢業學年度：	94
語文別：	英文
論文頁數：	76
中文關鍵詞：	心智負荷、駕駛行為、動態量測、人為失誤
外文關鍵詞：	Mental workload, Driving behavior, Dynamic measurement, Human error
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摘要

駕駛者必須付出連續且高程度的專注力在駕駛行為上。警覺性降低抑或心智負荷過高都會導致人為失誤的發生。而人為錯誤往往都是交通事故的主因。因此，瞭解駕駛者的心智負荷是非常重要的。
在這篇論文中，模擬如同平常高速公路的虛擬實境。利用不同狀況的環境或是非預期事件的發生來影響駕駛者的心智負荷。同時，駕駛績效、生理指標、主觀評量值會被擷取下來當成重要資訊。
根據駕駛指標、生理績效與主觀評量值的資料，利用多元迴歸與多項式網路來建構預測心智負荷的模式。在多元迴歸模式中，主觀評量值與平均車速、煞車平均變動量以及心跳有明顯的相關性。此研究的結果可用來發展適合的輔助系統並且潛在地提升交通舒適與安全。

Abstract

Driving task consumes a great deal of operator attention continuously. Either low vigilance or information overload may lead to human errors. Human errors were always major cause of traffic accidents. Therefore, understanding operator mental state is important.

In this study, the virtual environment of freeway was simulated where the drivers drove as usual. Mental workload of bus drivers were affected via different conditions or unexpected events. At the same time, driving performance, physiological index, and subjective ratings were measured during or after driving.

We constructed a multiple regression and polynomial neural networks to predict mental workload which are evaluated by data from subjective ratings, task performance, and physiological indexes. In multiple regression model, it is found the mental workload is effectively related to average speed, average braking depth variation, and heart rate (p=0.000<0.01). The results in this study can be referred to develop adaptive aiding systems and also are potential to enhance comfort and safety in traffic.

Content
Chapter 1 Introduction…………………………………………………………………1
1.1 Motivation    1
1.2 Importance    2
1.3 Objectives    3
1.4 Research framework    3
Chapter 2 Literature Review...…………………………………………………………5
2.1 Workload    5
2.2 Cognitive Operation Analysis    6
2.2.1 Cognitive load    7
2.2.2 Mental workload    7
2.3 Criteria for Workload Indexes    8
2.4 Measurements of mental workload     10
2.4.1 Physiological techniques    10
2.4.1.1 Brain Activity    10
2.4.1.2 Heart Rate and Heart Rate Variability    12
2.4.1.3 Eyes Activity    14
2.4.1.4 Others    15
2.4.2 Performance-based assessment techniques    18
2.4.2.1 Primary-Task measurement    18
2.4.2.2 Secondary task methodology........................................................... 20
2.4.3 Subjective workload assessment technique    28
2.4.3.1 NASA Task Load Index    28
2.4.3.2 Subjective Workload Assessment Technique    29
2.4.3.3 Cooper-Harper scale    29
2.4.3.4 Modified Cooper-Harper scale    30
2.4.3.5 RSME    30
2.5 Dynamic measurement     31
2.6 Summary     37
Chapter 3 Research Methodology…………………………………………………    39
3.1 Experiment design    41
3.2 Apparatus    42
3.2.1 Simulator design    42
3.2.2 Heart rate monitors    43
3.3 Data Collection    43
3.3.1 Driving performance    43
3.3.2 Physiological measures    44
3.3.3 Subjective ratings    44
3.4 Experimental procedure    45
3.5 Expected Results    46
Chapter 4 Results……………………………………………………………………..47
4.1 Comparison of mental workload under normal condition and emergency     condition……...……………………….…………………………………….47
4.2 Development of Mental workload Model…………………………………..48
4.3 Using Polynomial net (GMDH) to construct the model……………………54
4.3.1 Polynomial net (GMDH)…………………………………………… 54
4.3.2 Advantages in Polynomial Neural Network approach    56
4.3.3 Results    57
Chapter 5 Discussions...……………………………………………………………...58
5.1 Discussions of the multiple linear regression……………………………….58
5.2 Disscussions of the polynomial nets(GMDH)    60
5.3 Comparisons of regression method and the polynomial networks…………61
5.4 Application – Yerkes-Dodson law (inverted U shape)...…………………62
Chapter 6 Conclusion………………………………………………....................…66
6.1 Contribution and Implications to application area    66
6.2 Limitation    67
6.3 Future research    68
References……………………………………………………………………………70
Appendix Ⅰ…………………………………………………………………………74
Appendix Ⅱ…………………………………………………………………………75
Appendix Ⅲ…………………………………………………………………………76











List of Tables

Table 2.1 Performance indexes for measuring drivers’ mental workload……………20
Table 2.2 NASA-TLX rating scale (Hart and Staveland,1988)………………………29
Table 2.3 Dynamics Measurements of Mental Workload……………………………34
Table 2.3 Dynamics Measurements of Mental Workload (con.1)……………………35
Table 2.3 Dynamics Measurements of Mental Workload (con.2)……………………36
Table 3.1 Experimental layout…………………………………………..……………41
Table 3.2 Dependent variables……………………………………………………….42
Table 4.1 Statistics of mental workload under normal condition and emergency condition…………………………………………………………………...47
Table 4.2 ANOVA of mental workload under normal condition and emergency condition…………………………………………………………………...47
Table 4.3 Curve estimation…………………………………………………………..48
Table 4.4 Comparison the variance of residuals for different levels of predicted mental workload…………………………………………………………………...51
Table 4.5 Entered variables…………………………………………………………..52
Table 4.6 ANOVA analysis…………………………………………………………...52
Table 4.7 Model summary……………………………………………………………53
Table 4.8 Coefficients table……………………………………….………………….54
Table 5.1 Comparisons of regression method and the polynomial nets.......................61
Table 5.2 Comparisons of results in regression method and the polynomial nets.......61


List of Figures

Fig. 1.1 Research framework.........................................................................................4
Fig. 2.1 The adaptive task allocation human-computer interface……………………32
Fig. 3.1 The flow chart of implementation process………………………………….40
Fig. 3.2 The process of experiment and data collection……………………………..44
Fig. 3.3 The experiment procedure…………………………………………………..45
Fig. 4.1 Normal Probability Plot…………………………………………………….50
Fig. 4.2 Results of polynomial neural networks…………………………………….57
Fig. 5.1 An inverted U-shaped curve depicting the relationship between a measure of stress or arousal horizontal axis and efficiency of performance of a cognitive task vertical axis……………………………………………………………..62
Fig. 5.2 Scatter plot showing the relationship between “mental workload” and “1/average speed”……………………………………………………………63
Fig. 5.3 Scatter plot showing the relationship between “mental workload” and “average braking depth variation”…………………………………………...64

                                

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