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    ์ฐจ๋Ÿ‰์šฉ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด ์„ค๊ณ„์— ๊ด€ํ•œ ์ธ๊ฐ„๊ณตํ•™ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2020. 8. ๋ฐ•์šฐ์ง„.Head-up display (HUD) systems were introduced into the automobile industry as a means for improving driving safety. They superimpose safety-critical information on top of the drivers forward field of view and thereby help drivers keep their eyes forward while driving. Since the first introduction about three decades ago, automotive HUDs have been available in various commercial vehicles. Despite the long history and potential benefits of automotive HUDs, however, the design of useful automotive HUDs remains a challenging problem. In an effort to contribute to the design of useful automotive HUDs, this doctoral dissertation research conducted four studies. In Study 1, the functional requirements of automotive HUDs were investigated by reviewing the major automakers' automotive HUD products, academic research studies that proposed various automotive HUD functions, and previous research studies that surveyed drivers HUD information needs. The review results indicated that: 1) the existing commercial HUDs perform largely the same functions as the conventional in-vehicle displays, 2) past research studies proposed various HUD functions for improving driver situation awareness and driving safety, 3) autonomous driving and other new technologies are giving rise to new HUD information, and 4) little research is currently available on HUD users perceived information needs. Based on the review results, this study provides insights into the functional requirements of automotive HUDs and also suggests some future research directions for automotive HUD design. In Study 2, the interface design of automotive HUDs for communicating safety-related information was examined by reviewing the existing commercial HUDs and display concepts proposed by academic research studies. Each display was analyzed in terms of its functions, behaviors and structure. Also, related human factors display design principles, and, empirical findings on the effects of interface design decisions were reviewed when information was available. The results indicated that: 1) information characteristics suitable for the contact-analog and unregistered display formats, respectively, are still largely unknown, 2) new types of displays could be developed by combining or mixing existing displays or display elements at both the information and interface element levels, and 3) the human factors display principles need to be used properly according to the situation and only to the extent that the resulting display respects the limitations of the human information processing, and achieving balance among the principles is important to an effective design. On the basis of the review results, this review suggests design possibilities and future research directions on the interface design of safety-related automotive HUD systems. In Study 3, automotive HUD-based take-over request (TOR) displays were developed and evaluated in terms of drivers take-over performance and visual scanning behavior in a highly automated driving situation. Four different types of TOR displays were comparatively evaluated through a driving simulator study - they were: Baseline (an auditory beeping alert), Mini-map, Arrow, and Mini-map-and-Arrow. Baseline simply alerts an imminent take-over, and was always included when the other three displays were provided. Mini-map provides situational information. Arrow presents the action direction information for the take-over. Mini-map-and-Arrow provides the action direction together with the relevant situational information. This study also investigated the relationship between drivers initial trust in the TOR displays and take-over and visual scanning behavior. The results indicated that providing a combination of machine-made decision and situational information, such as Mini-map-and-Arrow, yielded the best results overall in the take-over scenario. Also, drivers initial trust in the TOR displays was found to have significant associations with the take-over and visual behavior of drivers. The higher trust group primarily relied on the proposed TOR displays, while the lower trust group tended to more check the situational information through the traditional displays, such as side-view or rear-view mirrors. In Study 4, the effect of interactive HUD imagery location on driving and secondary task performance, driver distraction, preference, and workload associated with use of scrolling list while driving were investigated. A total of nine HUD imagery locations of full-windshield were examined through a driving simulator study. The results indicated the HUD imagery location affected all the dependent measures, that is, driving and task performance, drivers visual distraction, preference and workload. Considering both objective and subjective evaluations, interactive HUDs should be placed near the driver's line of sight, especially near the left-bottom on the windshield.์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด๋Š” ์ฐจ๋‚ด ๋””์Šคํ”Œ๋ ˆ์ด ์ค‘ ํ•˜๋‚˜๋กœ ์šด์ „์ž์—๊ฒŒ ํ•„์š”ํ•œ ์ •๋ณด๋ฅผ ์ „๋ฐฉ์— ํ‘œ์‹œํ•จ์œผ๋กœ์จ, ์šด์ „์ž๊ฐ€ ์šด์ „์„ ํ•˜๋Š” ๋™์•ˆ ์ „๋ฐฉ์œผ๋กœ ์‹œ์„ ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋„์™€์ค€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์šด์ „์ž์˜ ์ฃผ์˜ ๋ถ„์‚ฐ์„ ์ค„์ด๊ณ , ์•ˆ์ „์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š”๋ฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด ์‹œ์Šคํ…œ์€ ์•ฝ 30๋…„ ์ „ ์šด์ „์ž์˜ ์•ˆ์ „์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ์ˆ˜๋‹จ์œผ๋กœ ์ž๋™์ฐจ ์‚ฐ์—…์— ์ฒ˜์Œ ๋„์ž…๋œ ์ด๋ž˜๋กœ ํ˜„์žฌ๊นŒ์ง€ ๋‹ค์–‘ํ•œ ์ƒ์šฉ์ฐจ์—์„œ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ์•ˆ์ „๊ณผ ํŽธ์˜ ์ธก๋ฉด์—์„œ ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด์˜ ์‚ฌ์šฉ์€ ์ ์  ๋” ์ฆ๊ฐ€ํ•  ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด์˜ ์ž ์žฌ์  ์ด์ ๊ณผ ๋ฐœ์ „ ๊ฐ€๋Šฅ์„ฑ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์œ ์šฉํ•œ ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด๋ฅผ ์„ค๊ณ„ํ•˜๋Š” ๊ฒƒ์€ ์—ฌ์ „ํžˆ ์–ด๋ ค์šด ๋ฌธ์ œ์ด๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ , ๊ถ๊ทน์ ์œผ๋กœ ์œ ์šฉํ•œ ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด ์„ค๊ณ„์— ๊ธฐ์—ฌํ•˜๊ณ ์ž ์ด 4๊ฐ€์ง€ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด์˜ ๊ธฐ๋Šฅ ์š”๊ตฌ ์‚ฌํ•ญ๊ณผ ๊ด€๋ จ๋œ ๊ฒƒ์œผ๋กœ์„œ, ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด ์‹œ์Šคํ…œ์„ ํ†ตํ•ด ์–ด๋–ค ์ •๋ณด๋ฅผ ์ œ๊ณตํ•  ๊ฒƒ์ธ๊ฐ€์— ๋Œ€ํ•œ ๋‹ต์„ ๊ตฌํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด์— ์ฃผ์š” ์ž๋™์ฐจ ์ œ์กฐ์—…์ฒด๋“ค์˜ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด ์ œํ’ˆ๋“ค๊ณผ, ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด์˜ ๋‹ค์–‘ํ•œ ๊ธฐ๋Šฅ๋“ค์„ ์ œ์•ˆํ•œ ํ•™์ˆ  ์—ฐ๊ตฌ, ๊ทธ๋ฆฌ๊ณ  ์šด์ „์ž์˜ ์ •๋ณด ์š”๊ตฌ ์‚ฌํ•ญ๋“ค์„ ์ฒด๊ณ„์  ๋ฌธํ—Œ ๊ณ ์ฐฐ ๋ฐฉ๋ฒ•๋ก ์„ ํ†ตํ•ด ํฌ๊ด„์ ์œผ๋กœ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด์˜ ๊ธฐ๋Šฅ์  ์š”๊ตฌ ์‚ฌํ•ญ์— ๋Œ€ํ•˜์—ฌ ๊ฐœ๋ฐœ์ž, ์—ฐ๊ตฌ์ž, ์‚ฌ์šฉ์ž ์ธก๋ฉด์„ ๋ชจ๋‘ ๊ณ ๋ คํ•œ ํ†ตํ•ฉ๋œ ์ง€์‹์„ ์ „๋‹ฌํ•˜๊ณ , ์ด๋ฅผ ํ†ตํ•ด ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด์˜ ๊ธฐ๋Šฅ ์š”๊ตฌ ์‚ฌํ•ญ์— ๋Œ€ํ•œ ํ–ฅํ›„ ์—ฐ๊ตฌ ๋ฐฉํ–ฅ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์•ˆ์ „ ๊ด€๋ จ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋Š” ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด์˜ ์ธํ„ฐํŽ˜์ด์Šค ์„ค๊ณ„์™€ ๊ด€๋ จ๋œ ๊ฒƒ์œผ๋กœ, ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด ์‹œ์Šคํ…œ์„ ํ†ตํ•ด ์•ˆ์ „ ๊ด€๋ จ ์ •๋ณด๋ฅผ ์–ด๋–ป๊ฒŒ ์ œ๊ณตํ•  ๊ฒƒ์ธ๊ฐ€์— ๋Œ€ํ•œ ๋‹ต์„ ๊ตฌํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์‹ค์ œ ์ž๋™์ฐจ๋“ค์˜ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด ์‹œ์Šคํ…œ์—์„œ๋Š” ์–ด๋–ค ๋””์Šคํ”Œ๋ ˆ์ด ์ปจ์…‰๋“ค์ด ์‚ฌ์šฉ๋˜์—ˆ๋Š”์ง€, ๊ทธ๋ฆฌ๊ณ  ํ•™๊ณ„์—์„œ ์ œ์•ˆ๋œ ๋””์Šคํ”Œ๋ ˆ์ด ์ปจ์…‰๋“ค์—๋Š” ์–ด๋–ค ๊ฒƒ๋“ค์ด ์žˆ๋Š”์ง€ ์ฒด๊ณ„์  ๋ฌธํ—Œ ๊ณ ์ฐฐ ๋ฐฉ๋ฒ•๋ก ์„ ํ†ตํ•ด ๊ฒ€ํ† ํ•˜์˜€๋‹ค. ๊ฒ€ํ† ๋œ ๊ฒฐ๊ณผ๋Š” ๊ฐ ๋””์Šคํ”Œ๋ ˆ์ด์˜ ๊ธฐ๋Šฅ๊ณผ ๊ตฌ์กฐ, ๊ทธ๋ฆฌ๊ณ  ์ž‘๋™ ๋ฐฉ์‹์— ๋”ฐ๋ผ ์ •๋ฆฌ๋˜์—ˆ๊ณ , ๊ด€๋ จ๋œ ์ธ๊ฐ„๊ณตํ•™์  ๋””์Šคํ”Œ๋ ˆ์ด ์„ค๊ณ„ ์›์น™๊ณผ ์‹คํ—˜์  ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋“ค์„ ํ•จ๊ป˜ ๊ฒ€ํ† ํ•˜์˜€๋‹ค. ๊ฒ€ํ† ๋œ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์•ˆ์ „ ๊ด€๋ จ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋Š” ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด์˜ ์ธํ„ฐํŽ˜์ด์Šค ์„ค๊ณ„์— ๋Œ€ํ•œ ํ–ฅํ›„ ์—ฐ๊ตฌ ๋ฐฉํ–ฅ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์„ธ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด ๊ธฐ๋ฐ˜์˜ ์ œ์–ด๊ถŒ ์ „ํ™˜ ๊ด€๋ จ ์ธํ„ฐํŽ˜์ด์Šค ์„ค๊ณ„์™€ ํ‰๊ฐ€์— ๊ด€ํ•œ ๊ฒƒ์ด๋‹ค. ์ œ์–ด๊ถŒ ์ „ํ™˜์ด๋ž€, ์ž์œจ์ฃผํ–‰ ์ƒํƒœ์—์„œ ์šด์ „์ž๊ฐ€ ์ง์ ‘ ์šด์ „์„ ํ•˜๋Š” ์ˆ˜๋™ ์šด์ „ ์ƒํƒœ๋กœ ์ „ํ™˜์ด ๋˜๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ฐ‘์ž‘์Šค๋Ÿฐ ์ œ์–ด๊ถŒ ์ „ํ™˜ ์š”์ฒญ์ด ๋ฐœ์ƒํ•˜๋Š” ๊ฒฝ์šฐ, ์šด์ „์ž๊ฐ€ ์•ˆ์ „ํ•˜๊ฒŒ ๋Œ€์ฒ˜ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋น ๋ฅธ ์ƒํ™ฉ ํŒŒ์•…๊ณผ ์˜์‚ฌ ๊ฒฐ์ •์ด ํ•„์š”ํ•˜๊ฒŒ ๋˜๊ณ , ์ด๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ๋„์™€์ฃผ๊ธฐ ์œ„ํ•œ ์ธํ„ฐํŽ˜์ด์Šค ์„ค๊ณ„์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•  ํ•„์š”์„ฑ์ด ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ž๋™์ฐจ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด ๊ธฐ๋ฐ˜์˜ ์ด 4๊ฐœ์˜ ์ œ์–ด๊ถŒ ์ „ํ™˜ ๊ด€๋ จ ๋””์Šคํ”Œ๋ ˆ์ด(๊ธฐ์ค€ ๋””์Šคํ”Œ๋ ˆ์ด, ๋ฏธ๋‹ˆ๋งต ๋””์Šคํ”Œ๋ ˆ์ด, ํ™”์‚ดํ‘œ ๋””์Šคํ”Œ๋ ˆ์ด, ๋ฏธ๋‹ˆ๋งต๊ณผ ํ™”์‚ดํ‘œ ๋””์Šคํ”Œ๋ ˆ์ด)๋ฅผ ์ œ์•ˆํ•˜์˜€๊ณ , ์ œ์•ˆ๋œ ๋””์Šคํ”Œ๋ ˆ์ด ๋Œ€์•ˆ๋“ค์€ ์ฃผํ–‰ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ ์‹คํ—˜์„ ํ†ตํ•ด ์ œ์–ด๊ถŒ ์ „ํ™˜ ์ˆ˜ํ–‰ ๋Šฅ๋ ฅ๊ณผ ์•ˆ๊ตฌ์˜ ์›€์ง์ž„ ํŒจํ„ด, ๊ทธ๋ฆฌ๊ณ  ์‚ฌ์šฉ์ž์˜ ์ฃผ๊ด€์  ํ‰๊ฐ€ ์ธก๋ฉด์—์„œ ํ‰๊ฐ€๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์ œ์•ˆ๋œ ๋””์Šคํ”Œ๋ ˆ์ด ๋Œ€์•ˆ๋“ค์— ๋Œ€ํ•ด ์šด์ „์ž๋“ค์˜ ์ดˆ๊ธฐ ์‹ ๋ขฐ๋„ ๊ฐ’์„ ์ธก์ •ํ•˜์—ฌ ๊ฐ ๋””์Šคํ”Œ๋ ˆ์ด์— ๋”ฐ๋ฅธ ์šด์ „์ž๋“ค์˜ ํ‰๊ท  ์‹ ๋ขฐ๋„ ์ ์ˆ˜์— ๋”ฐ๋ผ ์ œ์–ด๊ถŒ ์ „ํ™˜ ์ˆ˜ํ–‰ ๋Šฅ๋ ฅ๊ณผ ์•ˆ๊ตฌ์˜ ์›€์ง์ž„ ํŒจํ„ด, ๊ทธ๋ฆฌ๊ณ  ์ฃผ๊ด€์  ํ‰๊ฐ€๊ฐ€ ์–ด๋–ป๊ฒŒ ๋‹ฌ๋ผ์ง€๋Š”์ง€ ๋ถ„์„ํ•˜์˜€๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ, ์ œ์–ด๊ถŒ ์ „ํ™˜ ์ƒํ™ฉ์—์„œ ์ž๋™ํ™”๋œ ์‹œ์Šคํ…œ์ด ์ œ์•ˆํ•˜๋Š” ์ •๋ณด์™€ ๊ทธ์™€ ๊ด€๋ จ๋œ ์ฃผ๋ณ€ ์ƒํ™ฉ ์ •๋ณด๋ฅผ ํ•จ๊ป˜ ์ œ์‹œํ•ด ์ฃผ๋Š” ๋””์Šคํ”Œ๋ ˆ์ด๊ฐ€ ๊ฐ€์žฅ ์ข‹์€ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๋˜ํ•œ ๊ฐ ๋””์Šคํ”Œ๋ ˆ์ด์— ๋Œ€ํ•œ ์šด์ „์ž์˜ ์ดˆ๊ธฐ ์‹ ๋ขฐ๋„ ์ ์ˆ˜๋Š” ๋””์Šคํ”Œ๋ ˆ์ด์˜ ์‹ค์ œ ์‚ฌ์šฉ ํ–‰ํƒœ์™€ ๋ฐ€์ ‘ํ•œ ๊ด€๋ จ์ด ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์‹ ๋ขฐ๋„ ์ ์ˆ˜์— ๋”ฐ๋ผ ์‹ ๋ขฐ๋„๊ฐ€ ๋†’์€ ๊ทธ๋ฃน๊ณผ ๋‚ฎ์€ ๊ทธ๋ฃน์œผ๋กœ ๋ถ„๋ฅ˜๋˜์—ˆ๊ณ , ์‹ ๋ขฐ๋„๊ฐ€ ๋†’์€ ๊ทธ๋ฃน์€ ์ œ์•ˆ๋œ ๋””์Šคํ”Œ๋ ˆ์ด๋“ค์ด ๋ณด์—ฌ์ฃผ๋Š” ์ •๋ณด๋ฅผ ์ฃผ๋กœ ๋ฏฟ๊ณ  ๋”ฐ๋ฅด๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์—ˆ๋˜ ๋ฐ˜๋ฉด, ์‹ ๋ขฐ๋„๊ฐ€ ๋‚ฎ์€ ๊ทธ๋ฃน์€ ๋ฃธ ๋ฏธ๋Ÿฌ๋‚˜ ์‚ฌ์ด๋“œ ๋ฏธ๋Ÿฌ๋ฅผ ํ†ตํ•ด ์ฃผ๋ณ€ ์ƒํ™ฉ ์ •๋ณด๋ฅผ ๋” ํ™•์ธ ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€๋‹ค. ๋„ค ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์ „๋ฉด ์œ ๋ฆฌ์ฐฝ์—์„œ์˜ ์ธํ„ฐ๋ž™ํ‹ฐ๋ธŒ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด์˜ ์ตœ์  ์œ„์น˜๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๊ฒƒ์œผ๋กœ์„œ ์ฃผํ–‰ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ ์‹คํ—˜์„ ํ†ตํ•ด ๋””์Šคํ”Œ๋ ˆ์ด์˜ ์œ„์น˜์— ๋”ฐ๋ผ ์šด์ „์ž์˜ ์ฃผํ–‰ ์ˆ˜ํ–‰ ๋Šฅ๋ ฅ, ์ธํ„ฐ๋ž™ํ‹ฐ๋ธŒ ๋””์Šคํ”Œ๋ ˆ์ด ์กฐ์ž‘ ๊ด€๋ จ ๊ณผ์—… ์ˆ˜ํ–‰ ๋Šฅ๋ ฅ, ์‹œ๊ฐ์  ์ฃผ์˜ ๋ถ„์‚ฐ, ์„ ํ˜ธ๋„, ๊ทธ๋ฆฌ๊ณ  ์ž‘์—… ๋ถ€ํ•˜๊ฐ€ ํ‰๊ฐ€๋˜์—ˆ๋‹ค. ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด์˜ ์œ„์น˜๋Š” ์ „๋ฉด ์œ ๋ฆฌ์ฐฝ์—์„œ ์ผ์ •ํ•œ ๊ฐ„๊ฒฉ์œผ๋กœ ์ด 9๊ฐœ์˜ ์œ„์น˜๊ฐ€ ๊ณ ๋ ค๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ํ™œ์šฉ๋œ ์ธํ„ฐ๋ž™ํ‹ฐ๋ธŒ ๋””์Šคํ”Œ๋ ˆ์ด๋Š” ์Œ์•… ์„ ํƒ์„ ์œ„ํ•œ ์Šคํฌ๋กค ๋ฐฉ์‹์˜ ๋‹จ์ผ ๋””์Šคํ”Œ๋ ˆ์ด์˜€๊ณ , ์šด์ „๋Œ€์— ์žฅ์ฐฉ๋œ ๋ฒ„ํŠผ์„ ํ†ตํ•ด ๋””์Šคํ”Œ๋ ˆ์ด๋ฅผ ์กฐ์ž‘ํ•˜์˜€๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ, ์ธํ„ฐ๋ž™ํ‹ฐ๋ธŒ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด์˜ ์œ„์น˜๊ฐ€ ๋ชจ๋“  ํ‰๊ฐ€ ์ฒ™๋„, ์ฆ‰ ์ฃผํ–‰ ์ˆ˜ํ–‰ ๋Šฅ๋ ฅ, ๋””์Šคํ”Œ๋ ˆ์ด ์กฐ์ž‘ ๊ณผ์—… ์ˆ˜ํ–‰ ๋Šฅ๋ ฅ, ์‹œ๊ฐ์  ์ฃผ์˜ ๋ถ„์‚ฐ, ์„ ํ˜ธ๋„, ๊ทธ๋ฆฌ๊ณ  ์ž‘์—… ๋ถ€ํ•˜์— ์˜ํ–ฅ์„ ๋ฏธ์นจ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ชจ๋“  ํ‰๊ฐ€ ์ง€ํ‘œ๋ฅผ ๊ณ ๋ คํ–ˆ์„ ๋•Œ, ์ธํ„ฐ๋ž™ํ‹ฐ๋ธŒ ํ—ค๋“œ์—… ๋””์Šคํ”Œ๋ ˆ์ด์˜ ์œ„์น˜๋Š” ์šด์ „์ž๊ฐ€ ๋˜‘๋ฐ”๋กœ ์ „๋ฐฉ์„ ๋ฐ”๋ผ๋ณผ ๋•Œ์˜ ์‹œ์•ผ ๊ตฌ๊ฐ„, ์ฆ‰ ์ „๋ฉด ์œ ๋ฆฌ์ฐฝ์—์„œ์˜ ์™ผ์ชฝ ์•„๋ž˜ ๋ถ€๊ทผ์ด ๊ฐ€์žฅ ์ตœ์ ์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค.Abstract i Contents v List of Tables ix List of Figures x Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Research Objectives and Questions 8 1.3 Structure of the Thesis 11 Chapter 2 Functional Requirements of Automotive Head-Up Displays: A Systematic Review of Literature from 1994 to Present 13 2.1 Introduction 13 2.2 Method 15 2.3 Results 17 2.3.1 Information Types Displayed by Existing Commercial Automotive HUD Systems 17 2.3.2 Information Types Previously Suggested for Automotive HUDs by Research Studies 28 2.3.3 Information Types Required by Drivers (users) for Automotive HUDs and Their Relative Importance 35 2.4 Discussion 39 2.4.1 Information Types Displayed by Existing Commercial Automotive HUD Systems 39 2.4.2 Information Types Previously Suggested for Automotive HUDs by Research Studies 44 2.4.3 Information Types Required by Drivers (users) for Automotive HUDs and Their Relative Importance 48 Chapter 3 A Literature Review on Interface Design of Automotive Head-Up Displays for Communicating Safety-Related Information 50 3.1 Introduction 50 3.2 Method 52 3.3 Results 55 3.3.1 Commercial Automotive HUDs Presenting Safety-Related Information 55 3.3.2 Safety-Related HUDs Proposed by Academic Research 58 3.4 Discussion 74 Chapter 4 Development and Evaluation of Automotive Head-Up Displays for Take-Over Requests (TORs) in Highly Automated Vehicles 78 4.1 Introduction 78 4.2 Method 82 4.2.1 Participants 82 4.2.2 Apparatus 82 4.2.3 Automotive HUD-based TOR Displays 83 4.2.4 Driving Scenario 86 4.2.5 Experimental Design and Procedure 87 4.2.6 Experiment Variables 88 4.2.7 Statistical Analyses 91 4.3 Results 93 4.3.1 Comparison of the Proposed TOR Displays 93 4.3.2 Characteristics of Drivers Initial Trust in the four TOR Displays 102 4.3.3 Relationship between Drivers Initial Trust and Take-over and Visual Behavior 104 4.4 Discussion 113 4.4.1 Comparison of the Proposed TOR Displays 113 4.4.2 Characteristics of Drivers Initial Trust in the four TOR Displays 116 4.4.3 Relationship between Drivers Initial Trust and Take-over and Visual Behavior 117 4.5 Conclusion 119 Chapter 5 Human Factors Evaluation of Display Locations of an Interactive Scrolling List in a Full-windshield Automotive Head-Up Display System 121 5.1 Introduction 121 5.2 Method 122 5.2.1 Participants 122 5.2.2 Apparatus 123 5.2.3 Experimental Tasks and Driving Scenario 123 5.2.4 Experiment Variables 124 5.2.5 Experimental Design and Procedure 126 5.2.6 Statistical Analyses 126 5.3 Results 127 5.4 Discussion 133 5.5 Conclusion 135 Chapter 6 Conclusion 137 6.1 Summary and Implications 137 6.2 Future Research Directions 139 Bibliography 143 Apeendix A. Display Layouts of Some Commercial HUD Systems Appendix B. Safety-related Displays Provided by the Existing Commercial HUD Systems Appendix C. Safety-related HUD displays Proposed by Academic Research ๊ตญ๋ฌธ์ดˆ๋ก 187Docto

    User expectations of partial driving automation capabilities and their effect on information design preferences in the vehicle

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    Partially automated vehicles present interface design challenges in ensuring the driver remains alert should the vehicle need to hand back control at short notice, but without exposing the driver to cognitive overload. To date, little is known about driver expectations of partial driving automation and whether this affects the information they require inside the vehicle. Twenty-five participants were presented with five partially automated driving events in a driving simulator. After each event, a semi-structured interview was conducted. The interview data was coded and analysed using grounded theory. From the results, two groupings of driver expectations were identified: High Information Preference (HIP) and Low Information Preference (LIP) drivers; between these two groups the information preferences differed. LIP drivers did not want detailed information about the vehicle presented to them, but the definition of partial automation means that this kind of information is required for safe use. Hence, the results suggest careful thought as to how information is presented to them is required in order for LIP drivers to safely using partial driving automation. Conversely, HIP drivers wanted detailed information about the system's status and driving and were found to be more willing to work with the partial automation and its current limitations. It was evident that the drivers' expectations of the partial automation capability differed, and this affected their information preferences. Hence this study suggests that HMI designers must account for these differing expectations and preferences to create a safe, usable system that works for everyone. [Abstract copyright: Copyright ยฉ 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

    Motorcycle safety research project: Interim summary report 3: training and licensing interventions for risk taking and hazard perception for motorcyclists

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    Motorcycle trauma is a serious road safety issue in Queensland and throughout Australia. In 2009, Queensland Transport (later Transport and Main Roads or TMR) appointed CARRS-Q to provide a three-year program of Road Safety Research Services for Motorcycle Rider Safety. Funding for this research originated from the Motor Accident Insurance Commission. This program of research was undertaken to produce knowledge to assist TMR to improve motorcycle safety by further strengthening the licensing and training system to make learner riders safer by developing a pre-learner package (Deliverable 1), and by evaluating the QRide CAP program to ensure that it is maximally effective and contributes to the best possible training for new riders (Deliverable 2). The focus of this report is Deliverable 3 of the overall program of research. It identifies potential new licensing components that will reduce the incidence of risky riding and improve higher-order cognitive skills in new riders

    A multidisciplinary research approach for experimental applications in road-driver interaction analysis

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    This doctoral dissertation represents a cluster of the research activities conducted at the DICAM Department of the University of Bologna during a three years Ph.D. course. In relation to the broader research topic of โ€œroad safetyโ€, the presented research focuses on the investigation of the interaction between the road and the drivers according to human factor principles and supported by the following strategies: 1) The multidisciplinary structure of the research team covering the following academic disciplines: Civil Engineering, Psychology, Neuroscience and Computer Science Engineering. 2) The development of several experimental real driving tests aimed to provide investigators with knowledge and insights on the relation between the driver and the surrounding road environment by focusing on the behaviour of drivers. 3) The use of innovative technologies for the experimental studies, capable to collect data of the vehicle and on the user: a GPS data recorder, for recording the kinematic parameters of the vehicle; an eye tracking device, for monitoring the driversโ€™ visual behaviour; a neural helmet, for the detection of driversโ€™ cerebral activity (electroencephalography, EEG). 4) The use of mathematical-computational methodologies (deep learning) for data analyses from experimental studies. The outcomes of this work consist of new knowledge on the casualties between driversโ€™ behaviour and road environment to be considered for infrastructure design. In particular, the ground-breaking results are represented by: - the reliability and effectiveness of the methodology based on human EEG signals to objectively measure driverโ€™s mental workload with respect to different road factors; - the successful approach for extracting latent features from multidimensional driving behaviour data using a deep learning technique, obtaining driving colour maps which represent an immediate visualization with potential impacts on road safety

    Augmented Reality HUDs: Warning Signs and Driversโ€™ Situation Awareness

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    Drivers must search dynamic and complex visual environments to perceive relevant environmental elements such as warning signs, pedestrians and other vehicles to select the appropriate driving maneuver. The objective of this research was to examine how an Augmented Reality Head Up Display (AR HUD) for warning signs affects driver Situation Awareness (SA) and attention. Participants viewed videos of real driving scenes with an AR HUDs or no display and were asked to report what elements in the driving scene attracted their attention. At the completion of the first driving video participants were given a warning sign recognition test. Participants then watched a second video and the Situation Awareness Global Assessment Technique (SAGAT), a measure of global SA was administered. Participants eye movements were recorded when watching the videos to investigate how drivers interacting with an AR HUD attend to the environment compared to drivers with no AR HUD. AR HUDs for warning signs are effective in making warning signs more attentionally conspicuous to drivers in both low and high clutter driving environments. The HUD did not lead to increased fixation duration or frequency to warning signs in many situations. However when two driving items were in sight (sign and car) and participants needed to decide where to attend, they experienced attentional tunneling. In complex driving situations participants spent a significantly longer proportion of time looking at warning signs in the HUD. In simple driving situations, AR HUDs appear to make warning signs more salient and conspicuous. However, in complex situations in high clutter driving environments AR HUDs may lead to attentional tunneling

    Factors that influence visual attention and their effects on safety in driving: an eye movement tracking approach

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    Statistics show that a high percentage of road related accidents are due to factors that cause impaired driving. Since information extraction in driving is predominantly a visual task, visual distraction and its implications are therefore important safety issues. The main objective of this research is to study some of the implications of demands to humanโ€™s attention and perception and how it affects performance of tasks such as driving. Specifically, the study aims to determine the changes that occur in the visual behavior of drivers with different levels of driving experience by tracking the movement of the eye; examine the effects of different levels of task complexity on visual fixation strategies and visual stimulus recognition; investigate the effects of secondary task on attentional and visual focus and its impact on driving performance; and evaluate the implications of the use of information technology device (cellular phone) while driving on road safety. Thirty-eight students participated in the study consisting of two experiments. In the first experiment, the participants performed two driving sessions while wearing a head mounted eye tracking device. The second experiment involved driving while engaging in a cellular phone conversation. Fixation location, frequency, duration and saccadic path, were used to analyze eye movements. The study shows that differences in visual behavior of drivers exist; wherein drivers with infrequent driving per week fixated more on the dashboard area than on the front view (F(3,26) = 3.53, p\u3c0.05), in contrast to the driver with more frequent use of vehicle per week where higher fixations were recorded in the front/center view (F(3,26) = 4.26). The degree of visual distraction contributes to the deterioration of driving resulting to 55% more driving errors committed. Higher time where no fixation was detected was observed when driving with distraction (from 96% to 91% for drivers with less frequency of vehicle use and 55% to 44% for drivers with more frequent use of vehicle). The number of pre-identified errors committed increased from 64 to 81, due to the effect of visual tunneling. This research presents objective data that strengthens the argument on the detrimental effects of distraction in driving
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