지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학위논문
- 저자정보
- 지도교수
- 이상헌
- 발행연도
- 2016
- 저작권
- 국민대학교 논문은 저작권에 의해 보호받습니다.
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A majority of recently developed advanced vehicles have been equipped with various automated driver assistance systems, such as adaptive cruise control (ACC) and lane-keeping (LK) system. In addition, autonomous vehicles which consist of several automated systems increase. These automated systems have several operational modes, and drivers can be unaware of the mode in which they are operating. Because mode confusion is a significant human error factor that contributes to traffic accidents, it is necessary to develop user interfaces for automated systems that can reduce mode confusion.
In this paper, we developed a new interface design methodology for automated systems and verified its effectiveness through user interface design and driver-in-the-loop experiments with ACC systems. In addition, for an autonomous vehicle, we proposed driver interfaces that can accurately describe the states of the autonomous vehicles with a number of ADASs including ACC. In addition, the driver-in-the-loop experiments were conducted to examine the possibility of mode confusion while driving in various automation levels.
In conclusion, to reduce the mode confusion, not only the user mental model should be precise and concise, but also the user interface should be designed with simplicity of driver-vehicle interaction and intuitiveness of operation and in consideration of consistency.
In this paper, we developed a new interface design methodology for automated systems and verified its effectiveness through user interface design and driver-in-the-loop experiments with ACC systems. In addition, for an autonomous vehicle, we proposed driver interfaces that can accurately describe the states of the autonomous vehicles with a number of ADASs including ACC. In addition, the driver-in-the-loop experiments were conducted to examine the possibility of mode confusion while driving in various automation levels.
In conclusion, to reduce the mode confusion, not only the user mental model should be precise and concise, but also the user interface should be designed with simplicity of driver-vehicle interaction and intuitiveness of operation and in consideration of consistency.
목차
Ⅰ. Introduction 1Ⅱ. Human-automation interaction design for adaptive cruise control (ACC) systems of ground vehicles 31. Introduction 32. Literature survey 73. Proposed methodology for the design and verification of user interfaces 114. Machine and interface models of ACC systems 164.1 Traditional machine model of ACC systems 164.2 Traditional interface models of ACC systems 185. Development of new interface models for ACC systems 215.1 Design of machine and interface models 215.2 Compatibility test of machine and interface models 235.3 Redesign of machine and interface models 256. Driver-in-the-loop experiments 286.1 Participants 286.2 Procedure 286.3 Apparatus 306.3.1 Driving simulator 306.3.2 Graphical user interface for ACC systems 336.4 Scenario 347. Results 387.1 Mode confusion rates 387.2 Questionnaire survey 457.2.1 Summary of questionnaire results after each experiment 457.2.2 Summary of questionnaire results after all experiments 468. Discussion 499. Conclusions 51Ⅲ. Design and evaluation of human-machine interface to reduce mode confusion for autonomous vehicles with multiple levels of automation 531. Introduction 532. Literature survey 583. Machine and interface models of autonomous vehicles with multiple levels of automation 623.1 Machine model of autonomous vehicle with multiple levels of automation 643.1.1 Machine and interface models of stepwise interface 643.1.2 Machine and interface models of independent interface 673.2 Compatibility test of machine and interface models 714. Driver-in-the-loop experiments 724.1 Participants 724.2 Procedure 724.3 Apparatus 734.3.1 Driving simulator 734.3.2 Graphical user interface for autonomous vehicle with multiple levels of automation 774.4 Scenario 795. Results 835.1 Mode confusion rates 835.2 Questionnaire survey 895.2.1 Summary of questionnaire results after each experiment 895.2.2 Summary of questionnaire results after all experiments 936. Discussion 957. Conclusions 967.1 Summary of study results 967.2 Future works 97References 98
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