臺灣博碩士論文加值系統

在自駕車發展的過渡期，較少探討關於自駕車與人駕車及人的互動。在正面對應時，因自駕車內無駕駛者，導致人駕車駕駛、行人與自駕車溝通發生改變，其中以正面會車與正面左轉最容易發生駕駛意圖不清。所以人類駕駛需要解讀自駕車的交流訊號才能協調行車順序，若信號理解不當則可能導致事故。本研究旨在優化自駕車信號設計，提高人類駕駛員對自駕車訊息的理解度，並定義人類與自駕車間的溝通模式。 前測結果顯示，受測者最關心的訊息是自駕車是否要讓行給人駕車。因此，實驗設計旨在讓人能夠理解自駕車是否要讓行。 實驗變數包括視覺和聽覺因子。視覺因子考慮信號顯示位置和內容，建議將信號顯示於車前窗，控制LED燈條顏色在中性含義的青色，並採用18cm以上的文字尺寸，以及距離30m處開始顯示以提升注意力。聽覺因子則根據先前研究，將聽覺訊號分為語音和音效，其中音效選用高頻和慢節奏的提示音搭配自駕車減速情境，而語音則播報「請先行」，但語速和語言保持一致。透過實驗結果，本研究發現明確的語言訊號反饋在正面轉彎情境中最為有效，而在正面會車情境中，單一模態信號已足夠。此外，語音訊號相較於非語言訊號更清晰易懂，而聽覺提示音在正面轉彎情境下有一定的引導注意效果，但在會車情境下效果較差。因此，訊號應根據情境組合運用，並持續改進以滿足駕駛員需求。

During the transitional phase of autonomous vehicle development, the interaction between autonomous vehicles, human-driven vehicles, and pedestrians has been less explored. In face-to-face encounters, the absence of a driver in autonomous vehicles leads to a change in communication, with head-on meetings and left turns being particularly prone to unclear driving intentions. Thus, human drivers need to interpret the communication signals from autonomous vehicles to coordinate movement, where a misunderstanding of these signals could result in accidents. This study aims to optimize the signal design of autonomous vehicles to enhance human drivers' comprehension of the information conveyed by these vehicles and to define a communication model between humans and autonomous vehicles. Preliminary results indicated that the information most concerned by participants was whether the autonomous vehicle would yield to the human-driven vehicle. Therefore, the experimental design focused on enabling humans to understand whether the autonomous vehicle would yield. The experimental variables included visual and auditory factors. Visual factors considered the signal's display location and content, recommending displaying signals on the front windshield, controlling LED strip colors to the neutral meaning of cyan, adopting text sizes larger than 18cm, and starting to display at a distance of 30m to enhance attention. Auditory factors, based on previous studies, divided auditory signals into voice and sound effects. The sound effects used were high-frequency and slow-tempo hints matching the autonomous vehicle's deceleration scenario, while the voice broadcasted "After you", maintaining consistency in speech speed and language. Through the experimental results, this study found that explicit verbal signal feedback was most effective in the opposing turn scenario, while a single modality signal was sufficient in head-on meeting scenarios. Additionally, voice signals compared to non-verbal signals were clearer and easier to understand. Auditory hints had a certain attention-guiding effect in turning scenarios, but were less effective in meeting scenarios. Therefore, signals should be utilized in combination according to the scenario and continuously improved to meet driver needs.

致謝 i
摘要 ii
Abstract iii
目次 iv
表次 viii
圖次 ix
第壹章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 5
1.3 研究範圍與限制 5
1.4 研究架構 6
第貳章 文獻探討 9
2.1自動駕駛汽車 9
2.1.1自動駕駛汽車歷史 9
2.1.2自動駕駛各級別定義 11
2.1.3自動駕駛汽車發展現況 11
2.1.4自動駕駛汽車結構趨勢 13
2.2自駕車與人駕車 13
2.2.1 情境意識 15
2.2.2 資訊傳播理論 16
2.2.3汽車事故分析 19
2.3道路互動情境分析 20
2.3.1交通互動理論 20
2.3.2溝通策略 21
2.3.3對向情境的合作協調 22
2.3自駕車輛信號設計研究 23
2.3.1信號顏色 24
2.3.2 LED動態介面研究 26
2.3.3聲音信號之研究 27
2.4科技接受模型(TAM) 28
2.5文獻小結(TAM) 31
第參章 研究方法 32
3.1 實驗步驟 32
3.1.1實驗設計規劃 32
3.1.2實驗測試 32
3.1.3實驗問卷 33
3.1.4實驗結果分析與討論 33
3.2 受測者限制 35
3.3 實驗情境設計 35
3.3.1 自駕車與級別設定 35
3.3.2 實驗影片情境設定 36
 
3.3.3 道路使用者情境需求調查 37
3.3.4 自駕車情境歷程設定 40
3.4實驗設計 42
3.4.1實驗設計方法 42
3.4.2 視覺變數因子 42
3.4.3聽覺變數因子 51
3.5實驗樣本與素材製作 52
3.5.1 視覺與聲音特效編輯 52
3.5.2實驗器材 53
3.6 實驗任務設定 55
3.7 評估效標 56
3.7.1 信號意圖判斷 56
3.7.2 主觀認知評量 56
3.8 統計分析方法 58
3.8.1 信度分析 58
3.8.2 多變量變異數分析 58
3-8-3 LSD事後多重比較分析 58
第肆章 結果與討論 60
4.1 受測者基本資料 60
4.2 問卷信度分析結果 61
4.3平均分數對比統計結果 62
4.3.1正面轉彎情境 62
4.3.2會車情境 63
4.4 主觀評量多變量變異數(MANOVA)分析結果 64
4.4.1 MANOVA分析結果(正面轉彎情境) 66
4.4.2 LSD事後多重比較分析結果(正面轉彎情境) 69
4.4.3 MANOVA分析結果(會車情境) 73
4.4.4 LSD多重比較分析結果(會車情境) 75
第伍章 結論與建議 79
5.1 結論 79
5.2 建議 79
參考文獻 82
附錄一：受測者背景調查問卷 95
附錄二：主觀認知評量表 96



表次
表3-1：實驗設計總表 51
表3-2：實驗影片編號與內容總表 51
表4-1：情境樣本信度分析結果 61
表4-2：正面轉彎情境評分總表 62
表4-3：會車情境境評分總表 63
表4-4：正面轉彎情境MANOVA分析 66
表4-5：因子交互作用分析-正面轉彎情境 67
表4-6：文字視覺多重比較分析結果-正面轉彎情境 71
表4-7：聽覺多重比較分析結果-正面轉彎情境 72
表4-8：會車情境MANOVA分析 73
表4-9：因子交互作用分析-會車情境 74
表4-10：文字視覺多重比較分析結果-會車情境 75
表4-11：聽覺多重比較分析結果-會車情境 77









圖次
圖1-1：自動化程度六階級表 1
圖1-2：自駕車產業規模預測 2
圖1-3：研究架構圖 9
圖2-1：SAE J3016 各等級自駕車定義 12
圖2-2：2022各國自駕技術Level 3、Level 4立法進展 13
圖2-3：動態決策中的情境意識模型，描述行人與自駕車之間的互動 17
圖2-4：適用於車輛與其他道路使用者互動的交易溝通模型 18
圖2-5：通訊術語之間的相互關係 19
圖2-6：106-110年道路交通事故死傷人數概況 20
圖2-7：典型空間共享衝突示範，問號標記為衝突空間 21
圖2-8：五個典型的空間共享衝突原型，淺色箭頭表示可能衍生的不同方向變化 22
圖2-9：無障礙物對向路徑衝突 24
圖2-10：有障礙物對向路徑衝突 24
圖2-11：綠色前剎車燈設計 26
圖2-12：紅色前剎車燈設計 26
圖2-13：動畫模擬直行車輛的實驗場景 27
圖2-14：不同造型視覺介面的實驗變數 27
圖2-15：車輛讓行下，不同介面的平均感覺安全百分比波數的函數 28
圖2-16：TAM 科技接受模型 30
圖2-17：Z.Wu等人(2021) 所驗證的延伸科技接受模型 31
圖3-1：實驗流程架構圖 35
圖3-2：動畫影片情境設定 38
圖3-3：情境一：正面轉彎 38
圖3-4：情境二：會車 38
圖3-5：一般車輛與自駕車對向情境交流圖 41
圖3-6：自駕車造型架構四維分類量表 44
圖3-7：汽車前窗顯示設計 45
圖3-8：Nissan IDS 的燈條設計 46
圖3-9：Semcon 微笑動畫 46
圖3-10：本研究微笑燈條顯示設計 47
圖3-11：視覺顯示位置與其功能匹配圖 49
圖3-12：車內HUD信號顯示位置 50
圖3-13：車前窗文字顯示 51
圖3-14：車內HUD文字顯示 51
圖3-15：After Effects聲影整合與編輯 54
圖3-16：實驗播放器材(曲面螢幕)與相關道具 54
圖3-17：實驗環境 55
圖3-18：現場實驗情況 55
圖3-19：情境一：正面轉彎情境 56
圖3-20：情境二：會車情境 56
圖4-1：受測者性別分佈圖 61
圖4-2：受測者年齡分佈圖 62




 

參考文獻

一、外文文獻
1.Accident Analysis and Prevention, Vol. 27, No. 4, 1995, pp. 571–581.
2.Adrienne Lafrance. (2016). Will Pedestrians Be Able to Tell What a Driverless Car Is About to Do? https://www.theatlantic.com/technology/archive/2016/08/designing-a-driverless-car-with-pedestrians-in-mind/497801/
3.Allen, B. L., B. T. Shin, and P. J. Cooper. Analysis of Traffic Conflicts and Collisions.1978 ;Chin, H.-C., and S.-T. Quek. Measurement of Traffic Conflicts.1997 ; Minderhoud, M. M., and P. H. L. Bovy. Extended Time-to-Collision Measures for Road Traffic Safety Assessment.2001
and control systems—Specifications and test procedures for in-vehicle visual
4.András Bálint, Volker Labenski, Markus Köbe, Carina Vogl, Johan Stoll, Lars
5.Antonescu, O. (2013). Front stop lamps for a safer traffic. In Proceedings of the FISITA 2012 World Automotive Congress: Volume 9: Automotive Safety Technology (pp. 311-314). Springer Berlin Heidelberg.
6.Archer, J. Traffic Conflict Technique: Historical to Current State-of-the-Art. 2001 ; Muhlrad, N. Traffic Conflict Techniques and Other Forms of Behavioural Analysis: Application to Safety Diagnoses. 1993
Automated Vehicles Policy. Accelerating the next revolution in roadway safety.
7.Barua, N., Natarajan, P., Chandrasekar, P., Singh, S., 2014. Strategic Analysis of the European Market for V2V and V2I Communication Systems. Frost & Sullivan report MA29-18.
8.Bazilinskyy, P., et al. (2021). 'The Impact of Colored Light Signals from Autonomous Vehicles on Human Cognition in Varying Road Conditions: A Topic in Need of Further Research.' Advances in Human-Computer Interaction, 2021, Article ID 7845932.
9.Bazilinskyy, P., Kooijman, L., Dodou, D., & De Winter, J. C. F. (2021). How should external Human-Machine Interfaces behave? Examining the effects of colour, position, message, activation distance, vehicle yielding, and visual distraction among 1,434 participants. Applied ergonomics, 95, 103450.
10.Bazilinskyy, P.; Dodou, D.; de Winter, J. Survey on eHMI concepts: The effect of text, color, and perspective. Transp. Res. Part. F Traffic Psychol. Behav. 2019, 67, 175–194. [CrossRef]
11.Becton L., (2019): Discover Your Learning Style - Comprehensive Guide on Different Learning Styles.
12.Bengler, K., Rettenmaier, M., Fritz, N., Feierle, A., 2020. From HMI to HMIs: towards an HMI framework for automated driving. Information 11 (2), 61. https://doi.org/ 10.3390/info11020061.
13.Biever, W., Angell, L., & Seaman, S. (2020). Automated driving system collisions: early lessons. Human factors, 62(2), 249-259.
14.Campbell, J. L., Brown, J. L., Graving, J. S., Richard, C. M., Lichty, M. G.,Sanquist, T., & Morgan, J. (2016). Human factors design guidance for driver-vehicle interfaces. Report No. DOT HS, 812(360), 252. Washington, DC: National Highway Traffic Safety Administration
15.Carmona, J., Guindel, C., Garcia, F., & de la Escalera, A. (2021). eHMI: Review and guidelines for deployment on autonomous vehicles. Sensors, 21(9), 2912.
16.Carmona, J., Guindel, C., Garcia, F., & de la Escalera, A. (2021). eHMI: Review and guidelines for deployment on autonomous vehicles. Sensors,21(9), 2912.
17.Chang, C. M., Toda, K., Igarashi, T., Miyata, M., & Kobayashi, Y. (2018, September). A video-based study comparing communication modalities between an autonomous car and a pedestrian. In Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 104-109).
18.Charisi, V., Habibovic, A., Andersson, J., Li, J., & Evers, V. (2017, June).
19.Charisi, V.; Habibovic, A.; Andersson, J.; Li, J.; Evers, V. Children’s Views on Identification and Intention Communication of Self-driving Vehicles. In Proceedings of the 2017 Conference on Interaction Design and Children, Stanford, CA, USA, 27–30 June 2017; pp. 399–404.
children (pp. 399-404).
Children's views on identification and intention communication of self-driving
20.Clamann, M., Aubert, M., & Cummings, M. L. (2017). Evaluation of vehicle-to-pedestrian communication displays for autonomous vehicles (No. 17-02119)
21.Clamann, M., Aubert, M., & Cummings, M. L. (2017). Evaluation of vehicle-topedestrian communication displays for autonomous vehicles (No. 17-02119).
22.Colley, M., Bajrovic, E., & Rukzio, E. (2022, April). Effects of pedestrian behavior, time pressure, and repeated exposure on crossing decisions in front of automated vehicles equipped with external communication. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-11).
23.Daimler, A. G. (2017). Autonomous concept car smart vision EQ fortwo: welcome to the future ofcar sharing-Daimler global media site.
24.Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
25.De Ceunynck, T., Polders, E., Daniels, S., Hermans, E., Brijs, T., & Wets, G. (2013). Road safety differences between priority-controlled intersections and right-hand priority intersections: behavioral analysis of vehicle–vehicle interactions. Transportation research record, 2365(1), 39-48
26.De Clercq, K., Dietrich, A., Núñez Velasco, J. P., De Winter, J., & Happee, R. (2019). External human-machine interfaces on automated vehicles: effects on pedestrian crossing decisions. Human factors, 61(8), 1353-1370.
27.Deb, S., Strawderman, L. J., & Carruth, D. W. (2018). Investigating pedestrian suggestions for external features on fully autonomous vehicles: A virtual reality experiment. Transportation research part F: traffic psychology and behaviour, 59, 135-149.
28.Dey, D., Habibovic, A., Pfleging, B., Martens, M., & Terken, J. (2020, April). Color and animation preferences for a light band eHMI in interactions between automated vehicles and pedestrians. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-13).
29.Dey, D.; Habibovic, A.; Löcken, A.; Wintersberger, P.; Pfleging, B.; Riener, A.; Martens, M.; Terken, J. Taming the EHMI Jungle: A Classification Taxonomy to Guide, Compare, and Assess the Design Principles of Automated Vehicles’ External Human-Machine Interfaces. Transp. Res. Interdiscip. Perspect. 2020a, 7, 100174–100198
30.Dey, D.; Walker, F.; Martens, M.; Terken, J. Gaze Patterns in Pedestrian Interaction with Vehicles: Towards Effective Design of External Human-Machine Interfaces for Automated Vehicles. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2019, Utrecht, The Netherlands, 21–25 September 2019; ACM: New York, NY, USA, 2019; pp. 369–378.
DOT HS 812 (2017), 329
31.Eisma, Y. B., Reiff, A., Kooijman, L., Dodou, D., & De Winter, J. C. F. (2021). External human-machine interfaces: Effects of message perspective. Transportation research part F: traffic psychology and behaviour, 78, 30-41.
32.Endsley, M. R. (1995). Measurement of situation awareness in dynamic systems. Human factors, 37(1), 65-84.
33.Endsley, M. R. (2018, August). Situation awareness in future autonomous vehicles: Beware of the unexpected. In Congress of the International Ergonomics Association (pp. 303-309). Cham: Springer International Publishing.
34.Faas, S. M., & Baumann, M. (2019, November). Light-based external human machine interface: Color evaluation for self-driving vehicle and pedestrian interaction. In Proceedings of the human factors and ergonomics society annual meeting (Vol. 63, No. 1, pp. 1232-1236). Sage CA: Los Angeles, CA: Sage Publications.
35.Faas, S. M., Mathis, L. A., & Baumann, M. (2020). External HMI for self-driving vehicles: Which information shall be displayed?. Transportation research part F: traffic psychology and behaviour, 68, 171-186.
36.Färber B. (2016) Communication and Communication Problems Between Autonomous Vehicles and
37.Färber, B., 2016. Communication and communication problems between autonomous vehicles and human drivers, in: Autonomous driving. Springer, pp. 125–144.
38.Färber, B., 2016. Communication and communication problems between autonomous vehicles and human drivers, in: Autonomous driving. Springer, pp. 125–144.
39.Fuest, T., Sorokin, L., Bellem, H., Bengler, K. (2018). Taxonomy of traffic situations for the interaction between automated vehicles and human road users. In: International Conference on Applied Human Factors and Ergonomics (pp. 708-719). Springer. https://doi.org/10.1007/978-3-319-60441-1_68.
40.General Motors Corporation & Delphi-Delco Electronic Systems. (2002).Automotive collision avoidance system field operation test, warning cueimplementation summary report (Report No. DOT HS 809 462). Washington, DC:National Highway Traffic Safety Administration.
41.George, J. M., & Dane, E. (2016). Affect, emotion, and decision making. Organizational Behavior and Human Decision Processes, 136, 47-55.
42.Guo, F., Lyu, W., Ren, Z., Li, M., & Liu, Z. (2022). A Video-Based, Eye-Tracking Study to Investigate the Effect of eHMI Modalities and Locations on Pedestrian–Automated Vehicle Interaction. Sustainability, 14(9), 5633.
43.Habibovic A, Andersson J, Nilsson M, Lundgren VM, Nilsson J (2016) Evaluating interactions with non-existing automated vehicles: three Wizard of Oz approaches. In: Intelligent vehicles symposium (IV), IEEE, pp 32–37
44.Hancock*, P. A., & Weaver, J. L. (2005). On time distortion under stress. Theoretical issues in ergonomics science, 6(2), 193-211.
45.Horrey, W. J., Wickens, D. D., & Alexander, A. L. (2003). The effects of head-up display clutter and in-vehicle display separation on concurrent driving performance. Proceedings of the Human Factors and Ergonomics Society 47th Annual Meeting. 1880-1884.
https://static1.squarespace.com/static/5efaed43294db25b18168717/t/627e752a8d7775630d2ea94a/1652454782434/SAFE-UP_D2_6_Use%2Bcase%2Bdefinitions%2Band%2Binitial%2Bsafety-critical%2Bscenarios_.pdf
46.Human Drivers. Autonomous Driving, 2016, pp125-144. doi:10.1007/978-3-662-48847-8_7
47.Hydén, C. (1987). The development of a method for traffic safety evaluation: The Swedish Traffic Conflicts Technique. Bulletin Lund Institute of Technology, Department, (70).
48.Imbsweiler, J., Ruesch, M., Weinreuter, H., Puente Leon, ´ F., Deml, B., 2018. Cooperation behaviour of road users in t-intersections during deadlock situations. Transp. Res. Part F: Traffic Psychol. Behav. 58, 665–677. https://doi.org/10.1016/j. trf.2018.07.006.
49.ISO 15008. (2009). Road vehicles—Ergonomic aspects of transport information
50.J. Harding, G. Powell, R. Yoon, J. Fikentscher, C. Doyle, D. Sade, M. Lukuc, J. Simons, J. Wang, et al., “Vehicle-to-vehicle communications: readiness of v2v technology for application.,” tech. rep., United States. National Highway Traffic Safety Administration, 2014
51.J. Imbsweiler, T. Stoll, M. Ruesch, M. Baumann, and B. Deml,“Insight into cooperation processes for traffic scenarios: modelling with naturalistic decision making,” Cognition, Technology & Work, vol. 20, no. 4, pp. 621–635, 2018
52.Jandhyala (2017).Visual Learning: 6 Reasons Why Visuals Are the Most Powerful Aspect Of eLearning
53.Jayaraman, S. K., Creech, C., Tilbury, D. M., Yang, X. J., Pradhan, A. K., Tsui, K. M., & Robert Jr, L. P. (2019). Pedestrian trust in automated vehicles: Role of traffic signal and AV driving behavior. Frontiers in Robotics and AI, 6, 117.
54.Jerome, C., Monk, C., & Campbell, J. (2015, June). Driver vehicle interface design assistance for vehicle-to-vehicle technology applications. In Proceedings of the 24th International Technical Conference on the Enhanced Safety of Vehicles (ESV), Gothenburg, Sweden (pp. 8-11).
55.Jonas Andersson, Azra Habibovic, Maria Klingegård, Cristofer Englund, and Victor Malmsten-Lundgren. 2017. Hello Human, can you read my mind? ERCIM News (109) (2017), 36–37. http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-29618, International Organization for Standardization (ISO)/TR 23049. 2018. TECHNICAL REPORT ISO: Road Vehicles – Ergonomic aspects of external visual communication from automated vehicles to other road users. Technical Report. https://www.iso.org/obp/ui/#iso:std:iso:tr:23049:ed-1:v1:en
56.K. Bengler, M. Rettenmaier, N. Fritz, and A. Feierle, “From HMI to HMIs: Towards an HMI framework for automated driving,” Information, vol. 11, no. 2, p. 61, 2020
57.Kadali, B. R., & Vedagiri, P. (2013). Effect of vehicular lanes on pedestrian gap acceptance behaviour. Procedia-Social and Behavioral Sciences, 104, 678-687.
58.Kiefer, R., LeBlanc, D., Palmer, M., Salinger, J., Deering, R., & Shulman, M.(1999). Development and validation of functional definitions and evaluation procedures for collision warning/avoidance systems (Report No. DOT HT 808964).Washington, DC: National Highway Traffic Safety Administration.
59.Kitazaki, S., & Myhre, M. J. (2015, June). Effects of non-verbal communication cues on decisions and confidence of drivers at an uncontrolled intersection. In Driving Assesment Conference (Vol. 8, No. 2015). University of Iowa.
60.Lee, S. E., R. R. Knipling, M. C. DeHart, M. A. Perez, G. T. Holbrook, S. B. Brown, S. R. Stone, and R. L. Olson. Vehicle-Based Countermeasures for Signal and Stop Sign Violation. DOT HS 809 423. Virginia Tech Transportation Institute, Blacksburg, Va., 2004./ Parker, D., R. West, S. Stradling, and A. S. R. Manstead. Behavioural Characteristics and Involvement in Different Types of Traffic Accidents.
61.Lee, Y. M., Madigan, R., Garcia, J., Tomlinson, A., Solernou, A., Romano, R., ... & Uttley, J. (2019, September). Understanding the messages conveyed by automated vehicles. In Proceedings of the 11th international conference on automotive user interfaces and interactive vehicular applications (pp. 134-143).
62.Lerner, N. D., Kotwal, B. M., Lyons, R. D., & Gardner-Bonneau, D. J. (1996, January). Preliminary human factors guidelines for crash avoidance warning devices (Report No. DOT HS 808 342). Washington, DC: National Highway Traffic Safety Administration.
63.Li, Y., Cheng, H., Zeng, Z., Deml, B., & Liu, H. (2023). An AV-MV negotiation method based on synchronous prompt information on a multi-vehicle bottleneck road. Transportation Research Interdisciplinary Perspectives, 20, 100845.
64.Li, Y., Cheng, H., Zeng, Z., Liu, H., & Sester, M. (2021, September). Autonomous vehicles drive into shared spaces: ehmi design concept focusing on vulnerable road users. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) (pp. 1729-1736). IEEE.
65.Li, Y., Liu, H., & Deml, B. (2022, January). Hmi-based communication methods for negotiation between a manually driven vehicle driver and an autonomous vehicle in an ambiguous traffic scenario. In 2022 IEEE/SICE International Symposium on System Integration (SII) (pp. 244-249). IEEE.
66.Lind, H. (2007). An efficient visual forward collision warning display for vehicles. SAE World Congress. doi:10.4271/2007-01-1105
67.Liu, H., Hirayama, T., Watanabe, M., 2021. Importance of instruction for pedestrian-automated driving vehicle interaction with an external human machine interface: Effects on pedestrians’ situation awareness, trust, perceived risks and decision making, in: IEEE Intelligent Vehicles Symposium, pp. 748 754.
68.M. Rettenmaier, M. Pietsch, J. Schmidtler, and K. Bengler, “Passing through the bottleneck-the potential of external human-machine interfaces,” in 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 1687–1692, IEEE, 2019.
69.Markkula, G., Madigan, R., Nathanael, D., Portouli, E., Lee, Y. M., Dietrich, A., ... & Merat, N. (2020). Defining interactions: A conceptual framework for understanding interactive behaviour in human and automated road traffic. Theoretical Issues in Ergonomics Science, 21(6), 728-752.
70.McCormick, Ernest J.& Sanders, Mark S. 人因工程─工程與設計之人性因素(上冊)，吳水丕、許勝雄、彭游譯，美商麥格羅‧希爾國際公司，（1998）.
71.Miller, L., Leitner, J., Kraus, J., & Baumann, M. (2022). Implicit intention communication as a design opportunity for automated vehicles: Understanding drivers’ interpretation of vehicle trajectory at narrow passages. Accident Analysis & Prevention, 173, 106691.
72.Moore, D., Currano, R., Strack, G. E., & Sirkin, D. (2019, September). The case for implicit external human-machine interfaces for autonomous vehicles. In Proceedings of the 11th international conference on automotive user interfaces and interactive vehicular applications (pp. 295-307).
73.MUTCD, U.S. Department of Transportation, Federal Highway Administration.Manual on Uniform Traffic Control Devices for Streets and Highways (Revision 2). 2012. Retrieved from Federal Highway Administration website: http://mutcd.fhwa.dot.gov/pdfs/2009r1r2/mutcd2009r1r2edition.pdf
74.N. Merat, T. Louw, R. Madigan, M. Wilbrink, and A. Schieben, “Whatexternally presented information do vrus require when interacting with fully automated road transport systems in shared space?,” AccidentAnalysis & Prevention, vol. 118, pp. 244–252, 2018.
75.Najm, Wassim G., John D. Smith, and Mikio Yanagisawa. 2007. “Pre-Crash Scenario Typology for Crash Avoidance Research.” DOT HS 810 767. U.S. Department of Transportation.
76.National Highway Traffic Safety Administration and others. 2017. Federal
77.NHTSA (2015). Evaluation of Heavy-Vehicle Crash Warning Interfaces. DOT HS 812 191. https://www.nhtsa.gov/es/document/report-evaluation-heavy-vehicle-crash-warning-interfaces
78.Palmeiro, A. R., van der Kint, S., Vissers, L., Farah, H., de Winter, J. C., & Hagenzieker, M. (2018). Interaction between pedestrians and automated vehicles: A Wizard of Oz experiment. Transportation research part F: traffic psychology and behaviour, 58, 1005-1020.
79.Perkins S. R., and J. I. Harris. Traffic Conflict Characteristics: Accident Potential at Intersections. 1968
80.Petzoldt, T., Schleinitz, K., & Banse, R. (2018). Potential safety effects of a frontal brake light for motor vehicles. IET Intelligent Transport Systems, 12(6), 449-453.
81.Post, David, “Performance Requirements for Turn and Hazard Warning Signals,” National Highway Traffic Safety Administration, October 1975. [Online]. Available: http://deepblue.lib.umich.edu/bitstream/handle/2027.42/281/34157.0001.001.pdf?sequence=2
presentation. Geneva: International Organization for Standardization.
82.Rettenmaier, M., & Bengler, K. (2020, December). Modeling the interaction with automated vehicles in road bottleneck scenarios. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 64, No. 1, pp. 1615-1619). Sage CA: Los Angeles, CA: SAGE Publications.
83.Rettenmaier, M., Bengler, B. (2021). The Matter of How and When: Comparing Explicit and Implicit Communication Strategies of Automated Vehicles in Bottleneck Scenarios. In: IEEE Open Journal of Intelligent Transportation Systems, 2, 282-293. https://doi.org/10.1109/OJITS.2021.3107678.
84.Rettenmaier, M., Bengler, K., 2020. Modeling the interaction with automated vehicles in road bottleneck scenarios, in: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, SAGE Publications Sage CA: Los Angeles, CA. pp. 1615–1619.
85.Rettenmaier, M., Requena Witzig, C., & Bengler, K. (2020). Interaction at the bottleneck–a traffic observation. In Human Systems Engineering and Design II: Proceedings of the 2nd International Conference on Human Systems Engineering and Design (IHSED2019): Future Trends and Applications, September 16-18, 2019, Universität der Bundeswehr München, Munich, Germany (pp. 243-249). Springer International Publishing.
86.Rettenmaier, M., Witzig, C.R., Bengler, K. (2020b). Interaction at the bottleneck–a traffic observation. In: International Conference on Human Systems Engineering and Design: Future Trends and Applications (pp. 243-249). Springer. https://doi.org /10.1007/978-3-030-27928-8_37.
87.Rettenmaier, M., Witzig, C.R., Bengler, K. (2020b). Interaction at the bottleneck–a traffic observation. In: International Conference on Human Systems Engineering and Design: Future Trends and Applications (pp. 243-249). Springer. https://doi.org /10.1007/978-3-030-27928-8_37.
88.Rothenbücher, D., Li, J., Sirkin, D., Mok, B., & Ju, W. (2016, August). Ghost driver: A field study investigating the interaction between pedestrians and driverless vehicles. In 2016 25th IEEE international symposium on robot and human interactive communication (RO-MAN) (pp. 795-802). IEEE.
89.SAE Standards Works. 2018. J3134 Automated Driving System (ADS) Lamps Task Force. (2018). https://www.sae.org/works/committeeHome.do?comtID=TEVLCS5Z
90.SAE Technical Standards Board, “J3016b:taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles,” pp. 1–35, SAE International, 2018
91.Salamati, K., Schroeder, B., Rouphail, N. M., Cunningham, C., Long, R., & Barlow, J. (2011). Development and implementation of conflict-based assessment of pedestrian safety to evaluate accessibility of complex intersections. Transportation research record, 2264(1), 148-155. Schories, Lena Amann, Ganesh Baroda Sudhakaran, Pedro Huertas Leyva,
92.Schubert, W., & Kirschbaum, B. (2018). The Front Brake Light. Its conception and theoretical and experimental evidence for increasing traffic safety. Bonn: Bonner Institute for Forenscic and Traffic Psychology.
93.Stoll, T., Weihrauch, L., Baumann, M., 2020b. After you: merging at highway on- ramps. Proc. Hum. Factors Ergon. Soc. Annual Meeting 64 (1), 1105–1109. https://doi.org/10.1177/1071181320641266.
94.Šucha, M. (2014). Road users’ strategies and communication: driver-pedestrian interaction. Transport Research Arena (TRA).
95.Swain, J. (1987). Highway safety: The traffic conflict technique. Transport and Road Research Laboratory. Thomas Pallacci, Martin Östling, Daniel Schmidt, D., and Ron Schindler. 2021.
96.UNECE (United Nations Economic Commission for Europe). 2018. Autonomous Vehicle Signalling Requirements (AVSR) Taskforce. (2018). https://wiki.unece.org/pages/viewpage.action?pageId=73925596
Use case definitions and initial safety-critical scenarios. Report No. D2.6. Project SAFE-UP vehicles. In Proceedings of the 2017 conference on interaction design and
97.Werner, A. (2018). New colours for autonomous driving: An evaluation of chromaticities for the external lighting equipment of autonomous vehicles. Colour Turn, (1).
98.Wu, Z., Zhou, H., Xi, H., & Wu, N. (2021). Analysing public acceptance of autonomous buses based on an extended TAM model. IET Intelligent Transport Systems, 15(10), 1318-1330.