11,316 research outputs found

    A preliminary safety evaluation of route guidance comparing different MMI concepts

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    Enhanced Accessibility for People with Disabilities Living in Urban Areas

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    [Excerpt] People with disabilities constitute a significant proportion of the poor in developing countries. If internationally agreed targets on reducing poverty are to be reached, it is critical that specific measures be taken to reduce the societal discrimination and isolation that people with disabilities continue to face. Transport is an important enabler of strategies to fight poverty through enhancing access to education, employment, and social services. This project aims to further the understanding of the mobility and access issues experienced by people with disabilities in developing countries, and to identify specific steps that can be taken to start addressing problems. A major objective of the project is to compile a compendium of guidelines that can be used by government authorities, advocacy groups, and donor/loan agencies to improve the access of people with disabilities to transport and other services in urban areas

    Enhancing environmental engagement with natural language interfaces for in-vehicle navigation systems

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    Four on-road studies were conducted in the Clifton area of Nottingham, UK, aiming to explore the relationships between driver workload and environmental engagement associated with โ€˜activeโ€™ and โ€˜passiveโ€™ navigation systems. In a between-subjects design, a total of 61 experienced drivers completed two experimental drives comprising the same three routes (with overlapping sections), staged one week apart. Drivers were provided with the navigational support of a commercially-available navigation device (โ€˜satnavโ€™), an informed passenger (a stranger with expert route knowledge), a collaborative passenger (an individual with whom they had a close, personal relationship) or a novel interface employing conversational natural language NAV-NLI). The NAV-NLI was created by curating linguistic intercourse extracted from the earlier conditions, and delivering this using a Wizard-of-Oz technique. The different navigational methods were notable for their varying interactivity and the preponderance of environmental landmark information within route directions. Participants experienced the same guidance on each of the two drives to explore changes in reported and observed behaviour. Results show that participants who were more active in the navigation task (collaborative passenger or NAV-NLI) demonstrated enhanced environmental engagement (landmark recognition, route-learning and survey knowledge) allowing them to reconstruct the route more accurately post-drive, compared to drivers using more passive forms of navigational support (SatNav or informed passenger). Workload measures (TDT, NASA-TLX) indicated no differences between conditions, although satnav users and collaborative passenger drivers reported lower workload during their second drive. The research demonstrates clear benefits and potential for a navigation system employing two-way conversational language to deliver instructions. This could help support a long-term perspective in the development of spatial knowledge, enabling drivers to become less reliant on the technology and begin to re-establish associations between viewing an environmental feature and the related navigational manoeuvre

    'ํƒ‘์Šน์ž'์˜ ๊ด€์ ์˜ ์‹œ๊ฐ„, ์œ„์น˜ ๊ธฐ๋ฐ˜ ์ฐจ๋Ÿ‰ ํด๋Ÿฌ์Šคํ„ฐ UI ๋””์ž์ธ ํ”„๋ ˆ์ž„ ์ œ์•ˆ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋ฏธ์ˆ ๋Œ€ํ•™ ๋””์ž์ธํ•™๋ถ€ ๋””์ž์ธ์ „๊ณต,2019. 8. ์ •์˜์ฒ .One important design issue is the examination of how the user interface (UI) supports the new user role in future mobility. However, there are few design studies on the passengers cognitive needs and behavior in Autonomous Vehicles (AVs) based on empirical data. There is no doubt that autonomous mobility technologies are growing. The technology is already aiding the driving experience, and it will change the mobility culture and the transition of driver into passenger. This study is based on the premise that future AV is capable of performing all driving tasks. It proposes a set of passenger-centered automotive cluster UI designs for future mobility employing two factors: time and path. A set of empirical data is provided to understand the passengers perspective. In this study, a solid set of empirical data on the cognitive needs of passengers is collected. Human cognitive characteristics and driving tasks are investigated from various viewpoints to understand the passengers iii perspective. The cognitive relationship in the driving environment is analyzed through a literature review on situation awareness (SA) and structuring of the data flow framework. The framework is further explored by connecting the technological role transformation to the passenger. To construct the empirical database on the passenger, three sets of user tests and in-depth interviews were undertaken. The user tests were designed employing the Wizard of Oz method, and the results were summarized using descriptive and exploratory analysis. Based on these insights, a set of UI designs from the perspective of the passenger was proposed, and usability tests were conducted to verify its effectiveness and usability. The results of the tests demonstrate that a major percentage of the information request was related to time (current time and duration) and path (vehicle location and surroundings). Based on the data, a UI framework was built. Two usage scenarios were designed, time-full and time-less, for better in-situation comprehension. Time- and path-based UI were proposed to flow with the scenarios. A usability test was conducted, and a passengers cognitive framework was defined. There are two aspects to this study: the data flow frameworks of the driver/passenger, and the UI design proposal. Situational precision from the perspective of the driver was analyzed to understand the relationship between the user, the vehicle and the road conditions. Further, the cognitive framework of the passenger was proposed based on the data. This study provides a solid understanding of drivers emerging needs when they are relieved of the cognitive burden of driving tasks. The UI features for AV are introduced based on the empirical data and research related to the provision of better situation awareness, focusing on time and location. This study contributes to the extant literature by observing the iv perspective of passengers in Autonomous vehicles based on a qualitative study. The proposed UI design will be further explored as a communication method between the system and the passive user in future mobility.์‚ฌ์šฉ์ž ์ธํ„ฐํŽ˜์ด์Šค๊ฐ€ (UI) ๋ฏธ๋ž˜ ์ด๋™์„ฑ์—์„œ ์ƒˆ๋กœ์šด ์‚ฌ์šฉ์ž ์—ญํ• ์„ ์ง€์ง€ํ•˜๋Š” ๋””์ž์ธ ๋„์ถœ์€ ๋ฏธ๋ž˜ ์ด๋™์„ฑ ๋ถ„์•ผ์—์„œ ์ค‘์š”ํ•œ ๋””์ž์ธ ์ด์Šˆ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‚ฌ์šฉ์ž ์‹คํ—˜์— ๊ทผ๊ฑฐํ•˜์—ฌ ์ž์œจ์ฃผํ–‰์ฐจ๋Ÿ‰ (AV) ์˜ ํƒ‘์Šน์ž์ธ์ง€ ์š•๊ตฌ์™€ ํ–‰๋™์— ๋Œ€ํ•œ ๋””์ž์ธ ์—ฐ๊ตฌ๋Š” ๋ฏธ๋ฏธํ•˜๋‹ค. ์ž์œจ์ฃผํ–‰์ด ๊ธฐ์ˆ ์˜ ๋ฐœ์ „๊ณผ ๊ทธ ์˜์—ญ์€ ์ ์ฐจ ๋„“์–ด์ง€๊ณ  ์žˆ๋‹ค. ํ•ด๋‹น ๊ธฐ์ˆ ์€ ์ด๋ฏธ ์šด์ „ ํ™˜๊ฒฝ์— ์ ์šฉ๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋กœ ์ธํ•ด ๋ฏธ๋ž˜ ์ด๋™๋ฌธํ™”์—์„œ ์‚ฌ์šฉ์ž์˜ ์—ญํ• ์€ '์šด์ „์ž'์—์„œ 'ํƒ‘์Šน์ž'๋กœ ๋ณ€ํ™”ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฏธ๋ž˜ ์ž์œจ์ฃผํ–‰์ฐจ๋Ÿ‰์ด ๋ชจ๋“  ์šด์ „ ์ƒํ™ฉ์— ๋Œ€์ฒ˜ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์ „์ œ๋กœ ํ•œ๋‹ค. ์‚ฌ์šฉ์ž ์‹คํ—˜์„ ํ†ตํ•ด ํƒ‘์Šน์ž์˜ ๊ด€์ ์— ๋Œ€ํ•œ ๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€๊ณ , ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ฏธ๋ž˜ ๋ชจ๋นŒ๋ฆฌํ‹ฐ ํ™˜๊ฒฝ์— ์ ์šฉ๋  ์‚ฌ์šฉ์ž ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋””์ž์ธ์€ ์šด์ „์ž ์ค‘์‹ฌ์˜ ์ƒํ™ฉ์ธ์ง€์—์„œ ๋ฒ—์–ด๋‚˜ ํƒ‘์Šน์ž ์ค‘์‹ฌ ์ธ์ง€ ์ •๋ณด ์š”์†Œ๋ฅผ ๋ถ„์„ํ•˜์˜€๊ณ , ์‹œ๊ฐ„๊ณผ ๊ฒฝ๋กœ ๋‘ ๊ฐ€์ง€ ์š”์†Œ๋ฅผ ๊ฐ•์กฐํ•œ UI ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ํƒ‘์Šน์ž์˜ ์ธ์ง€ ์ •๋ณด ์š”๊ตฌ์— ๋Œ€ํ•œ ์‹คํ—˜์  ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜์˜€๋‹ค. ํƒ‘์Šน์ž์˜ ๊ด€์ ์„ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ๊ด€์ ์—์„œ ์ธ๊ฐ„์˜ ์ธ์ง€์  ํŠน์„ฑ ๋ฐ ์šด์ „ ํƒœ์Šคํฌ๋ฅผ ๊ด€์ฐฐํ•˜์˜€๊ณ , ์ƒํ™ฉ์ธ์ง€ (SA) ์— ๊ด€ํ•œ ๋ฌธํ—Œ ์—ฐ๊ตฌ์™€ ๋ฐ์ดํ„ฐ ํ”„๋ ˆ์ž„์›Œํฌ ๊ตฌ์กฐํ™”๋ฅผ ํ†ตํ•ด ์šด์ „ ํ™˜๊ฒฝ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ธ์ง€์  ์š”์†Œ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ํ”„๋ ˆ์ž„์›Œํฌ๋Š” ๊ธฐ์ˆ  ๋ณ€ํ™”์— ๋”ฐ๋ผ ์šด์ „์ž๊ฐ€ ํƒ‘์Šน์ž๋กœ ๋ณ€ํ™”๋˜์—ˆ์„ ๋•Œ ์šด์ „ ํ™˜๊ฒฝ์—์„œ์˜ ๋ฐ์ดํ„ฐ ๊ด€๊ณ„ ๋ณ€ํ™”๋ฅผ ์‹œ๊ฐ์ ์œผ๋กœ ๊ตฌ์กฐํ™”ํ•˜์—ฌ ์‹ฌ์ธต์ ์œผ๋กœ ํƒ๊ตฌ๋˜์—ˆ๋‹ค. ํƒ‘์Šน์ž์˜ ์ธ์ง€ ๋‹ˆ์ฆˆ ๋Œ€ํ•œ ์‹คํ—˜์  ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ ์ˆ˜์ง‘ํ•˜๊ธฐ ์œ„ํ•ด ์ด 3 ์„ธํŠธ์˜ ์œ ์ € ํ…Œ์ŠคํŠธ์™€ ์‹ฌ์ธต ์ธํ„ฐ๋ทฐ๊ฐ€ ์ˆ˜๋ฐ˜๋˜์—ˆ๋‹ค. ์œ ์ € ํ…Œ์ŠคํŠธ๋Š” Wizard of Oz ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ์„ค๊ณ„๋˜์—ˆ์œผ๋ฉฐ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ์งˆ์  ์—ฐ๊ตฌ๋ฐฉ๋ฒ•๋ก ์˜ ๋ถ„์„ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๋ถ„์„๋˜์—ˆ๋‹ค. ์‹คํ—˜์„ ํ†ตํ•ด ์–ป์€ ์ธ์‚ฌ์ดํŠธ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํƒ‘์Šน์ž ๊ด€์ ์—์„œ UI ๋””์ž์ธ์„ ์ œ์•ˆํ•˜๊ณ  ์‚ฌ์šฉ์„ฑ ํ…Œ์ŠคํŠธ๋ฅผ ํ†ตํ•ด ํšจ์œจ์„ฑ๊ณผ ์œ ์šฉ์„ฑ์„ 5 ์  ๋ฆฌ ์ปคํŠธ ์Šค์ผ€์ผ๋กœ์จ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด ํƒ‘์Šน์ž๊ฐ€ ์š”์ฒญํ•œ ์ธ์ง€ ์ •๋ณด๋Š” ์‹œ๊ฐ„ (ํ˜„์žฌ ์‹œ๊ฐ ๋ฐ ๊ธฐ๊ฐ„)๊ณผ ๊ฒฝ๋กœ (์ฐจ๋Ÿ‰ ์œ„์น˜ ๋ฐ ์ฃผ๋ณ€ ํ™˜๊ฒฝ)์— ์ง‘์ค‘๋œ ๊ฒƒ์„ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด์™€ ๊ฐ™์€ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ UI ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ์ƒํ™ฉ ์†์˜ ์‚ฌ์šฉ๋ก€๋ฅผ ์ œ์‹œํ•˜๊ธฐ ์œ„ํ•˜์—ฌ๋„ ๊ฐ€์ง€ time-full ๊ณผ time-less ์˜ ์‚ฌ์šฉ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ตฌ์ถ•ํ•˜๊ณ , ์ œ์•ˆ๋œ ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋”ฐ๋ผ ์‹œ๊ฐ„๊ณผ ์œ„์น˜์— ๊ธฐ๋ฐ˜ํ•œ UI ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ UI ์— ๋Œ€ํ•œ ์‚ฌ์šฉ์„ฑ ํ…Œ์ŠคํŠธ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๊ณ , ํƒ‘์Šน์ž ๊ด€์ ์—์„œ์˜ ์šด์ „์ƒํ™ฉ ์ธ์ง€ ์›Œํฌ ํ”„๋ ˆ์ž„์„ ์™„์„ฑํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฐ€์น˜๋Š” ๋‘ ๊ฐ€์ง€๋กœ ์ •๋ฆฌ๋  ์ˆ˜ ์žˆ๋‹ค. ํ•˜๋‚˜๋Š” ์šด์ „์ž / ํƒ‘์Šน์ž์˜ ๋ฐ์ดํ„ฐ ํ”Œ๋กœ์šฐ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค๋Š” ๊ฒƒ๊ณผ ๋‘ ๋ฒˆ์งธ๋Š” ํƒ‘์Šน์ž์˜ ๊ด€์ ์„ ์ง€์ง€ํ•˜๋Š” UI ๋””์ž์ธ ์ œ์•ˆ์— ์žˆ๋‹ค. ์šด์ „์ž์˜ ๊ด€์ ์—์„œ์˜ ์šด์ „ ์ƒํ™ฉ์„ ๋ถ„์„ํ•˜์—ฌ ์‚ฌ์šฉ์ž, ์ฐจ๋Ÿ‰, ๊ทธ๋ฆฌ๊ณ  ๋„๋กœ ์ƒํƒœ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์‹œ๊ฐํ™”ํ•˜์˜€๊ณ , ์ด๋Š” ํƒ‘์Šน์ž์ธ์ง€ ํ”Œ๋กœ์šฐ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•˜๋Š”๋ฐ ๊ธฐ์กฐ์ ์ธ ํ‹€๋กœ์จ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์šด์ „ ํƒœ์Šคํฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๋ฐ์— ํ•„์š”ํ–ˆ๋˜ ์ธ์ง€ ๋ถ€๋‹ด์—์„œ ๋ฒ—์–ด๋‚ฌ์„ ๋•Œ์˜ ์šด์ „์ž๊ฐ€ ํ•„์š”๋กœ ํ•˜๋Š” ๋ณตํ•ฉ์ ์ธ ๋‹ˆ์ฆˆ์— ๋Œ€ํ•ด ๊ด€์ฐฐํ•˜๊ณ  ๋ฏธ๋ž˜ ๋ชจ๋นŒ๋ฆฌํ‹ฐ ํ™˜๊ฒฝ์— ์ ํ•ฉํ•œ UI ์˜ ๋””์ž์ธ ์š”์†Œ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋…ผ๋ฌธ์ด๋‹ค. ๋ฏธ๋ž˜ ์ž์œจ์ฃผํ–‰์ฐจ๋Ÿ‰ ์•ˆ์˜ ์‚ฌ์šฉ์ž ์ธํ„ฐํŽ˜์ด์Šค๊ฐ€ ๊ฐ–์ถ”์–ด์•ผ ํ•˜๋Š” ์š”์†Œ๋ฅผ ์‹คํ—˜์  ๋ฐ์ดํ„ฐ์— ๊ทผ๊ฑฐํ•˜์—ฌ ์ œ์‹œํ•˜๋ฉฐ, ์‹œ๊ฐ„๊ณผ ๋ฃจํŠธ๋ฅผ ๊ฐ•์กฐํ•˜์—ฌ ํ–ฅ์ƒ๋œ ์ƒํ™ฉ ์ธ์ง€๋ฅผ ์ œ๊ณตํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์‹ฌ๋„์žˆ๋Š” ๊ด€์ฐฐ์„ ๊ธฐ๋กํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์งˆ์  ์—ฐ๊ตฌ์— ๊ธฐ์ดˆํ•œ ์ž์œจ ์ฐจ๋Ÿ‰์˜ ํƒ‘์Šน์ž ๊ด€์ ์„ ๊ด€์ฐฐํ•จ์œผ๋กœ์จ ๊ธฐ์กด ์ž์œจ์ฃผํ–‰์ด ๋””์ž์ธ ์—ฐ๊ตฌ์— ๊ธฐ์—ฌํ•  ๊ฒƒ์ด๋‹ค. ์ œ์•ˆ๋œ UI ๋””์ž์ธ ๋ฏธ๋ž˜ ์ด๋™ ์„ฑ์•ˆ์—์„œ ์‹œ์Šคํ…œ๊ณผ ํƒ‘์Šน์ž ๊ฐ„์˜ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋กœ์จ ๊ทธ ์˜์˜๊ฐ€ ์žˆ๋‹ค.ABSTRACT ...................................................................................................................... II CHAPTER 1. INTRODUCTION......................................................................................... ๏ผ‘ 1.1. BACKGROUND ..............................................................................................................๏ผ‘ 1.2. PURPOSE .....................................................................................................................๏ผ— 1.3. RESEARCH QUESTION.....................................................................................................๏ผ˜ CHAPTER 2. LITERATURE REVIEW ..............................................................................๏ผ‘๏ผ‘ 2.1. SITATION AWARENESS (SA) ........................................................................................๏ผ‘๏ผ‘ 2.2. HUMAN INFORMATION PROCESSING MODEL..................................................................๏ผ‘๏ผ• 2.3. DRIVING SITUATION AWARENESS AND PERSPECTIVE.........................................................๏ผ’๏ผ 2.4. DRIVING TASK AND SENSORY INTERACTION ....................................................................๏ผ’๏ผ’ CHAPTER 3. COGNITIVE NEEDS IN AUTONOMOUS.....................................................๏ผ’๏ผ— 3.1. DRIVING BEHAVIOR TRANSFORMATION AND CLUSTER UI..................................................๏ผ’๏ผ— 3.2. COGNITIVE FRAMEWORK TRANSFORMATION ..................................................................๏ผ“๏ผ“ CHAPTER 4. USER TESTS ............................................................................................๏ผ“๏ผ– 4.1. WIZARD OF OZ PROTOTYPING .....................................................................................๏ผ“๏ผ˜ 4.2. PILOT TEST 1............................................................................................................๏ผ”๏ผ 4.2.1. Experiment Design & Laboratory Setting.................................................๏ผ”๏ผ 4.2.2. Persona Scenario & Task Design ..............................................................๏ผ”๏ผ’ 4.2.3. Preparation of Driving situation...............................................................๏ผ”๏ผ• 4.2.4. Procedure.................................................................................................๏ผ”๏ผ— 4.2.5. Data Analysis & Insight............................................................................๏ผ”๏ผ˜ 4.3. PILOT TEST 2............................................................................................................๏ผ•๏ผ‘ 4.3.1. Amendment: Experiment Design & Laboratory Setting ...........................๏ผ•๏ผ’ 4.3.2. Amendment: Task Scenario & Command Cue..........................................๏ผ•๏ผ” 4.3.3. Amendment: Perform Role and preparation of driving situation ............๏ผ•๏ผ— 4.3.4. Amendment: Procedure ...........................................................................๏ผ•๏ผ™ 4.3.5. Data Analysis & Insight............................................................................๏ผ–๏ผ’ 4.4. MAIN TEST ..............................................................................................................๏ผ–๏ผ• 4.4.1. Experiment Design & Laboratory setting .................................................๏ผ–๏ผ– 4.4.2. Task Design ..............................................................................................๏ผ–๏ผ™ 4.4.3. Procedure.................................................................................................๏ผ—๏ผ‘ 4.4.4. Result Analysis & Insight..........................................................................๏ผ—๏ผ” CHAPTER 5. UI CONCEPT DEVELOPMENT...................................................................๏ผ˜๏ผ‘ 5.1. UI DESIGN METHOD..................................................................................................๏ผ˜๏ผ‘ 5.2. DESIGN PROPOSAL ....................................................................................................๏ผ˜๏ผ” 5.3. USER SCENARIOS ......................................................................................................๏ผ˜๏ผ– 5.3.1 Scenario 1. Time-less: Late for a morning meeting..................................๏ผ˜๏ผ– 5.3.2 Scenario 2.Time-full: Leisure driving on weekends ..................................๏ผ™๏ผ“ CHAPTER 6. USABILITY TEST ......................................................................................๏ผ™๏ผ˜ 6.1. USABILITY TEST GUIDE ...............................................................................................๏ผ™๏ผ˜ 6.2. ASSESSMENT USABILITY TEST ..................................................................................๏ผ‘๏ผ๏ผ 6.2.1 Test planning........................................................................................๏ผ‘๏ผ๏ผ 6.2.2 Laboratory setting................................................................................๏ผ‘๏ผ๏ผ’ 6.2.3 Test conduct and debriefing.................................................................๏ผ‘๏ผ๏ผ– 6.3. RESULT ANALYSIS ..................................................................................................๏ผ‘๏ผ๏ผ– CHAPTER 7. CONCLUSION......................................................................................๏ผ‘๏ผ๏ผ— APPENDIX 1...........................................................................................................๏ผ‘๏ผ‘๏ผ APPENDIX 2...........................................................................................................๏ผ‘๏ผ‘๏ผ‘ APPENDIX 3...........................................................................................................๏ผ‘๏ผ‘๏ผ“ APPENDIX 4...........................................................................................................๏ผ‘๏ผ’๏ผ‘ APPENDIX 5...........................................................................................................๏ผ‘๏ผ’๏ผ” APPENDIX 6...........................................................................................................๏ผ‘๏ผ’๏ผ˜ APPENDIX 7...........................................................................................................๏ผ‘๏ผ“๏ผ“ BIBLIOGRAPHY ......................................................................................................๏ผ‘๏ผ“๏ผ– ๊ตญ๋ฌธ ์ดˆ๋ก ............................................................................................................๏ผ‘๏ผ”๏ผ“Maste

    Inventory of ATT system requirements for elderly and disabled drivers and travellers

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    This Inventory of ATT System Requirements for Elderly and Disabled Drivers and Travellers is the product of the TELSCAN projectโ€™s Workpackage 3: Identification and Updating of User Requirements of Elderly and Disabled Travellers. It describes the methods and tools used to identify the needs of elderly and disabled (E&D) travellers. The result of this investigation is a summary of the requirements of elderly and disabled travellers using different modes of transport, including private cars, buses/trams, metros/trains, ships and airplanes. It provides a generic user requirements specification which can guide the design of all transport telematics systems. However, it is important to stress that projects should also capture a more detailed definition of user requirements for their specific application area or system

    Performance Measures to Assess Resiliency and Efficiency of Transit Systems

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    Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jerseyโ€™s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster

    Cockpit task management: A preliminary, normative theory

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    Cockpit task management (CTM) involves the initiation, monitoring, prioritizing, and allocation of resources to concurrent tasks as well as termination of multiple concurrent tasks. As aircrews have more tasks to attend to due to reduced crew sizes and the increased complexity of aircraft and the air transportation system, CTM will become a more critical factor in aviation safety. It is clear that many aviation accidents and incidents can be satisfactorily explained in terms of CTM errors, and it is likely that more accidents induced by poor CTM practice will occur in the future unless the issue is properly addressed. The first step in understanding and facilitating CTM behavior was the development of a preliminary, normative theory of CTM which identifies several important CTM functions. From this theory, some requirements for pilot-vehicle interfaces were developed which are believed to facilitate CTM. A prototype PVI was developed which improves CTM performance and currently, a research program is under way that is aimed at developing a better understanding of CTM and facilitating CTM performance through better equipment and procedures
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