駕駛介面、身體與心智負荷對情境知覺與績效知影響｜國立臺灣科技大學博碩士論文系統

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研究生：	Mia Tri Utami Mia Tri Utami
論文名稱：	駕駛介面、身體與心智負荷對情境知覺與績效知影響 Effect of Interface, Physical and Mental Workload on the Situation Awareness and Performance in Driving Task
指導教授：	林久翔 Chiu-Hsiang Lin
口試委員:	許聿靈 Yu-Ling Hsu 林承哲 Cheng-Jhe Lin
學位類別：	碩士 Master
系所名稱：	管理學院 - 工業管理系 Department of Industrial Management
論文出版年：	2022
畢業學年度：	110
語文別：	英文
論文頁數：	102
中文關鍵詞：	體力工作量、腦力工作量、駕駛性能、態勢感知、駕駛任務
外文關鍵詞：	physical workload, mental workload, driving performance, situational awareness, driving task
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交通運輸系統在幫助實現人員和貨物的流動性方面具有非常重要的作用。 2014 年至 2018 年期間，機動車輛數量的增長有所增加，印度尼西亞每年增長 6.49%（BPS，2018 年）。每項研究都必須確保在駕駛模擬器研究中獲得的結果不能歸因於實際交通環境中不存在的駕駛模擬器的特定特性。高分辨率圖形的處理非常重要，這樣駕駛員才能獲得真實的感覺並對駕駛環境做出反應。在這項研究中，我們想通過使用因變量 LF/HF 比、駕駛性能、ASA 和 NASA TLX 來證明哪種界面更適合測量 SA。這項研究的結果是，當用於測量體力工作量時，VR 優於顯示器。

The transportation system has a very important role in helping the implementation of the mobility of people and goods. The increase in number of motorized vehicles has increased in the 2014 – 2018 period, increasing by 6.49% per year in Indonesia (BPS, 2018). Each research must ensure that the results obtained in the driving simulator study cannot be attributed to specific characteristics of the driving simulator that do not exist in the actual traffic environment. Processing of high-resolution graphics is very important so that the driver has a realistic feeling and reacts to the driving environment. In this study, we want to prove which interface is better to measure SA by using the dependent variable LF/HF ratio, driving performance, ASA and NASA TLX. The results in this study are that when used to measure physical workload, VR i s better than monitors.

摘 要    iv
ABSTRACT    v
Table of Contents    vi
List of Table    viii
List of Figure    ix
CHAPTER I    1
INTRODUCTION    1
1.1 Background    1
1.2 Formulation Problem    3
1.3 Problem Limitation    3
1.4 Research Objectives    4
1.5 Research Benefits    4
CHAPTER II    5
LITERATURE REVIEW    5
2.1 Effect of Interface on SA    5
2.2 Effect of Physical Workload on SA    6
2.3 Effect of Mental Workload on SA    6
CHAPTER III    9
THEORETICAL BASIS    9
3.1 Situational Awareness    9
3.2  Method SA    11
3.3  Quantitative Analysis of Situational Awareness (QUASA)    13
3.4 Workload    14
3.4.1 Mental Workload    14
3.4.2 Measurement Mental Workload    14
3.4.3 Physical Workload    17
3.4.5 Measurement Physical Workload    17
3.5     Interface Visual Driving Simulator    19
3.6  Interface Visual VR    20
3.7 Signal Detection Theory (SDT)    20
3.8 Virtual Reality and Driving Simulator    23
CHAPTER IV    24
RESEARCH METHODS    24
4.1 Research subject    24
4.2 Instrumental    24
4.4  Design of Experiment (DoE)    25
4.5  Hypothesis    28
4.6  Research Stage    30
4.7  Experimental Stage    31
CHAPTER V    35
RESULT AND DISCUSSION    35
5.1 Virtual Reality vs Monitor    35
5.2 Effect of Interaction Heart Rate    36
5.2.1 Effect of Interface on LF/HF ratio    36
5.2.2 Effect of Mental Workload on LF/HF ratio    38
5.2.3 Effect of Interface on Physical Workload    39
5.3 Effect of Interface on Performance    40
5.3.1 The effect of Interface on Performance    40
5.3.2 Effect of Mental Workload on Performance    41
5.3.3 Effect of Physical Workload on Performance    41
5.4 Effect of Interface on Situation Awareness    42
5.4.1 The effect of Interface on ASA    42
5.4.1.1 The Effect of Mental Workload on ASA    43
5.4.1.2 The effect of Physical Workload on ASA    43
5.4.2 The effect of Interface (BIAS)    44
5.4.2.1 The Effect of Mental Workload on BIAS    45
5.4.2.2 The Effect Physical Workload on BIAS    46
5.4.3 The effect of Interface on PSA    46
5.4.3.1 The Effect Mental Workload on PSA    47
5.4.3.2 The Effect Physical Workload on PSA    47
5.5 EEG Analysis    48
5.6 NASA TLX Questionnaire based on scenario interface.    50


List of Table

Table 2. 1 Riset GAP    8
Table 3. 1 Rating Scale and Definisi NASATLX    16
Table 3. 2 Bruce protocol data observations.    19
Table 4. 1 Experiment Scenario Design    25
Table 4. 2 Operational Definition    26
Table 4. 3 Experiment Factorial    28
Table 5. 1 Riset Scenario    35
Table 5. 2 Factor and Level Factor    36
Table 5. 3. RM ANOVA Hear Rate    36
Table 5. 4 Mean heart rate    37
Table 5. 5 RM ANOVA on Performance    40
Table 5. 6 Summary of RM ANOVA result for Performance    40
Table 5. 7 Summary of  RM ANOVA result for ASA    42
Table 5. 8 Summary score ASA    43
Table 5. 9 Summary of RM ANOVA result for BIAS    44
Table 5. 10. Summary score BIAS SA    45
Table 5. 11 Summary of RM ANOVA result for PSA    46
Table 5. 12. Summary score PSA SA    47
Table 5. 13. Summary average and SD EEG    48














List of Figure

Figure 2. 1 Research framework    8
Figure 3. 1 SA Model in Dynamic Decision Making    9
Figure 3. 2 Examples of QUASA pertanyaan questions and answers    13
Figure 3. 3  Self rating QUASA    14
Figure 3. 4 EEG waveform    15
Figure 3. 5 Electrodeposition based on the International 10-20 system    16
Figure 3. 6 Bruce Protocol    18
Figure 3. 7 Results of possible wrong or right responses    21
Figure 3. 8 Combined Representation of VR and DS.    23
Figure 4. 1 Experimental Design    26
Figure 4. 2 Riset Hypothesis    28
Figure 4. 3 Research Stages    30
Figure 4. 4 Experimental Procedure    32
Figure 5. 1 Teta wave    50
Figure 5. 2 Mental Demand NASA TLX    51
Figure 5. 3 Physical Demand NASA TLX    52
Figure 5. 4 Temporal Demand NASA TLX    53
Figure 5. 5 Performance NASA TLX    54
Figure 5. 6 Effort NASA TLX    55
Figure 5. 7 Frustation NASA TLX    56


                                

Anggadamari, B., & Wijayanto, T. (2015). Analisis Pengaruh Physical Workload Terhadap Situation Awareness Dan Performansi Mengemudi Di Pagi Dan Malam Hari [UGM]. http://etd.repository.ugm.ac.id/home/detail_pencarian/90089
Choi, M., Ahn, S., & Seo, J. O. (2020). VR-Based investigation of forklift operator situation awareness for preventing collision accidents. Accident Analysis and Prevention, 136(November 2019), 105404. https://doi.org/10.1016/j.aap.2019.105404
Cremer, J., Kearney, J., & Papelis, Y. (1996). Driving simulation: Challenges for VR technology. IEEE Computer Graphics and Applications, 16(5), 16–20. https://doi.org/10.1109/38.536270
de Winter, J. C. F., de Groot, S., Mulder, M., Wieringa, P. A., Dankelman, J., & Mulder, J. A. (2009). Relationships between driving simulator performance and driving test results. Ergonomics, 52(2), 137–153. https://doi.org/10.1080/00140130802277521
Fallahi, M., Motamedzade, M., Heidarimoghadam, R., Soltanian, A. R., & Miyake, S. (2016). Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study. Applied Ergonomics, 52, 95–103. https://doi.org/10.1016/j.apergo.2015.07.009
Faure, V., Lobjois, R., & Benguigui, N. (2016). The effects of driving environment complexity and dual tasking on drivers’ mental workload and eye blink behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 40, 78–90. https://doi.org/10.1016/j.trf.2016.04.007
Galante, F., Bracco, F., Chiorri, C., Pariota, L., Biggero, L., & Bifulco, G. N. (2018). Validity of mental workload measures in a driving simulation environment. Journal of Advanced Transportation, 2018. https://doi.org/10.1155/2018/5679151
Ihemedu-Steinke, Q. C., Sirim, D., Erbach, R., Halady, P., & Meixner, G. (2015). Development and evaluation of a virtual reality driving simulator. Mensch Und Computer 2015 - Workshop, 491–500. https://doi.org/10.1515/9783110443905-070
Kaptein, Nico A., Jan Theeuwes, and R. V. D. H. (1996). Driving Simulator Validity : Transportation Research Record, 1550, 30–36.
Lee, W. S., Kim, J. H., & Cho, J. H. (1998). A driving simulator as a virtual reality tool. Proceedings - IEEE International Conference on Robotics and Automation, 1(May), 71–76. https://doi.org/10.1109/ROBOT.1998.676264
McGuiness, B. (2004). 2004 Command and Control Research and Technology Symposium Quantitative Analysis of Situational Awareness ( QUASA ): Applying Signal Detection Theory to True / False Probes and Self-Ratings Barry McGuinness BAE SYSTEMS Quantitative Analysis of Situational. Command and Control Research and Technology Symposium.
Rizalmi, S. R. (2019). Analisis Pengaruh Interaksi Penumpang Dan Pengemudi Terhadap Situation Awareness Dan Driving Performance Pada Kondisi Sleep Deprivation. http://etd.repository.ugm.ac.id/home/detail_pencarian/173373
Shakouri, M., Ikuma, L. H., Aghazadeh, F., & Nahmens, I. (2018). Analysis of the sensitivity of heart rate variability and subjective workload measures in a driving simulator: The case of highway work zones. International Journal of Industrial Ergonomics, 66, 136–145. https://doi.org/10.1016/j.ergon.2018.02.015
Walch, M., Frommel, J., Rogers, K., Schüssel, F., Hock, P., Dobbelstein, D., & Weber, M. (2017). Evaluating VR driving simulation from a player experience perspective. Conference on Human Factors in Computing Systems - Proceedings, Part F1276(May), 2982–2989. https://doi.org/10.1145/3027063.3053202
Weidner, F., Hoesch, A., Poeschl, S., & Broll, W. (2017). Comparing VR and non-VR driving simulations: An experimental user study. Proceedings - IEEE Virtual Reality, 281–282. https://doi.org/10.1109/VR.2017.7892286
Wibisono, Y. T., & Hartono, B. (2015). Evaluasi Alat Pengukuran Situational awareness. In UGM (Vol. 13, Issue 3). http://etd.repository.ugm.ac.id/home/detail_pencarian/89599
Zhang, T., Kaber, D., & Hsiang, S. (2010). Characterisation of mental models in a virtual reality-based multitasking scenario using measures of situation awareness. Theoretical Issues in Ergonomics Science, 11(1–2), 99–118. https://doi.org/10.1080/14639220903010027
Anggadamari, B., & Wijayanto, T. (2015). Analisis Pengaruh Physical Workload Terhadap Situation Awareness Dan Performansi Mengemudi Di Pagi Dan Malam Hari [UGM]. http://etd.repository.ugm.ac.id/home/detail_pencarian/90089
Choi, M., Ahn, S., & Seo, J. O. (2020). VR-Based investigation of forklift operator situation awareness for preventing collision accidents. Accident Analysis and Prevention, 136(November 2019), 105404. https://doi.org/10.1016/j.aap.2019.105404
Cremer, J., Kearney, J., & Papelis, Y. (1996). Driving simulation: Challenges for VR technology. IEEE Computer Graphics and Applications, 16(5), 16–20. https://doi.org/10.1109/38.536270
de Winter, J. C. F., de Groot, S., Mulder, M., Wieringa, P. A., Dankelman, J., & Mulder, J. A. (2009). Relationships between driving simulator performance and driving test results. Ergonomics, 52(2), 137–153. https://doi.org/10.1080/00140130802277521
Fallahi, M., Motamedzade, M., Heidarimoghadam, R., Soltanian, A. R., & Miyake, S. (2016). Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study. Applied Ergonomics, 52, 95–103. https://doi.org/10.1016/j.apergo.2015.07.009
Faure, V., Lobjois, R., & Benguigui, N. (2016). The effects of driving environment complexity and dual tasking on drivers’ mental workload and eye blink behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 40, 78–90. https://doi.org/10.1016/j.trf.2016.04.007
Galante, F., Bracco, F., Chiorri, C., Pariota, L., Biggero, L., & Bifulco, G. N. (2018). Validity of mental workload measures in a driving simulation environment. Journal of Advanced Transportation, 2018. https://doi.org/10.1155/2018/5679151
Ihemedu-Steinke, Q. C., Sirim, D., Erbach, R., Halady, P., & Meixner, G. (2015). Development and evaluation of a virtual reality driving simulator. Mensch Und Computer 2015 - Workshop, 491–500. https://doi.org/10.1515/9783110443905-070
Kaptein, Nico A., Jan Theeuwes, and R. V. D. H. (1996). Driving Simulator Validity : Transportation Research Record, 1550, 30–36.
Lee, W. S., Kim, J. H., & Cho, J. H. (1998). A driving simulator as a virtual reality tool. Proceedings - IEEE International Conference on Robotics and Automation, 1(May), 71–76. https://doi.org/10.1109/ROBOT.1998.676264
McGuiness, B. (2004). 2004 Command and Control Research and Technology Symposium Quantitative Analysis of Situational Awareness ( QUASA ): Applying Signal Detection Theory to True / False Probes and Self-Ratings Barry McGuinness BAE SYSTEMS Quantitative Analysis of Situational. Command and Control Research and Technology Symposium.
Rizalmi, S. R. (2019). Analisis Pengaruh Interaksi Penumpang Dan Pengemudi Terhadap Situation Awareness Dan Driving Performance Pada Kondisi Sleep Deprivation. http://etd.repository.ugm.ac.id/home/detail_pencarian/173373
Shakouri, M., Ikuma, L. H., Aghazadeh, F., & Nahmens, I. (2018). Analysis of the sensitivity of heart rate variability and subjective workload measures in a driving simulator: The case of highway work zones. International Journal of Industrial Ergonomics, 66, 136–145. https://doi.org/10.1016/j.ergon.2018.02.015
Walch, M., Frommel, J., Rogers, K., Schüssel, F., Hock, P., Dobbelstein, D., & Weber, M. (2017). Evaluating VR driving simulation from a player experience perspective. Conference on Human Factors in Computing Systems - Proceedings, Part F1276(May), 2982–2989. https://doi.org/10.1145/3027063.3053202
Weidner, F., Hoesch, A., Poeschl, S., & Broll, W. (2017). Comparing VR and non-VR driving simulations: An experimental user study. Proceedings - IEEE Virtual Reality, 281–282. https://doi.org/10.1109/VR.2017.7892286
Wibisono, Y. T., & Hartono, B. (2015). Evaluasi Alat Pengukuran Situational awareness. In UGM (Vol. 13, Issue 3). http://etd.repository.ugm.ac.id/home/detaCharacterization99
Zhang, T., Kaber, D., & Hsiang, S. (2010). Characterisation of mental models in a virtual reality-based multitasking scenario using measures of situation awareness. Theoretical Issues in Ergonomics Science, 11(1–2), 99–118.https://doi.org/10.1080/14639220903010027

全文公開日期 2032/02/08 (校內網路)
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