638 research outputs found

    A Comparative Analysis of Machine Learning Methods for Lane Change Intention Recognition Using Vehicle Trajectory Data

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    Accurately detecting and predicting lane change (LC)processes can help autonomous vehicles better understand their surrounding environment, recognize potential safety hazards, and improve traffic safety. This paper focuses on LC processes and compares different machine learning methods' performance to recognize LC intention from high-dimensionality time series data. To validate the performance of the proposed models, a total number of 1023 vehicle trajectories is extracted from the CitySim dataset. For LC intention recognition issues, the results indicate that with ninety-eight percent of classification accuracy, ensemble methods reduce the impact of Type II and Type III classification errors. Without sacrificing recognition accuracy, the LightGBM demonstrates a sixfold improvement in model training efficiency than the XGBoost algorithm.Comment: arXiv admin note: text overlap with arXiv:2304.1373

    Driverโ€™s Shy Away Effect in Urban Extra-Long Underwater Tunnel

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    For urban extra-long underwater tunnels, the obstacle space formed by the tunnel walls on both sides has an impact on the driver\u27s driving. The aim of this study is to investigate the shy away characteristics of drivers in urban extra-long underwater tunnels. Using trajectory offset and speed data obtained from real vehicle tests, the driving behaviour at different lanes of an urban extra-long underwater tunnel was investigated, and a theory of shy away effects and indicators of sidewall shy away deviation for quantitative analysis were proposed. The results show that the left-hand lane has the largest offset and driving speed from the sidewall compared to the other two lanes. In the centre lane there is a large fluctuation in the amount of deflection per 50 seconds of driving, increasing the risk of two-lane collisions. When the lateral clearances are increased from 0.5 m to 2.19 m on the left and 1.29 m on the right, the safety needs of drivers can be better met. The results of this study have implications for improving traffic safety in urban extra-long underwater tunnels and for the improvement of tunnel traffic safety facilities

    Impact of Temperament Types and Anger Intensity on Drivers\u27 EEG Power Spectrum and Sample Entropy: An On-road Evaluation Toward Road Rage Warning

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    "Road rage", also called driving anger, is becoming an increasingly common phenomenon affecting road safety in auto era as most of previous driving anger detection approaches based on physiological indicators are often unreliable due to the less consideration of drivers\u27 individual differences. This study aims to explore the impact of temperament types and anger intensity on drivers\u27 EEG characteristics. Thirty-two drivers with valid license were enrolled to perform on-road experiments on a particularly busy route on which a variety of provoking events like cutting in line of surrounding vehicle, jaywalking, occupying road of non-motor vehicle and traffic congestion frequently happened. Then, muti-factor analysis of variance (ANOVA) and post hoc analysis were utilized to study the impact of temperament types and anger intensity on drivers\u27 power spectrum and sample entropy of ฮธ and ฮฒ waves extracted from EEG signals. The study results firstly indicated that right frontal region of the brain has close relationship with driving anger. Secondly, there existed significant main effects of temperament types on power spectrum and sample entropy of ฮฒ wave while significant main effects of anger intensity on power spectrum and sample entropy of ฮธ and ฮฒ wave were all observed. Thirdly, significant interactions between temperament types and anger intensity for power spectrum and sample entropy of ฮฒ wave were both noted. Fourthly, with the increase of anger intensity, the power spectrum and sample entropy both decreased sufficiently for ฮธ wave while increased remarkably for ฮฒ wave. The study results can provide a theoretical support for designing a personalized and hierarchical warning system for road rage

    Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review

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    Ninety percent of the worldโ€™s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships

    Investigating the Roles of Time Perspective and Emerging Adulthood in Predicting Driving Behavior

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    In the U.S., motor vehicle crashes are the leading cause of death for individuals 2 to 34 years of age (National Highway Traffic Safety Administration, 2009c). Of particular interest are 18 to 25 year olds or emerging adults because of their increased crash risk. The prevalence of crashes attributable to the combination of driving inexperience and risky behaviors creates the necessity to identify predictors of crash likelihoods. While there are known personality variables that predict risky driving, time perspective as an additional one was suggested. Time perspective pertains to how the past, present, and future influence an individual\u27s actions. Zimbardo, Keough, and Boyd (1997) investigated the relationship between time perspective and risky driving behavior as part of a larger health behavior study. The current research focused on replicating and extending their initial endeavor. Proposed improvements included expanding the risky driving outcome questionnaire from Zimbardo\u27s five items to include scales more commonly used in the traffic research field (e.g., the Driver Behavior Questionnaire: Lajunen, Parker, & Summala, 2004; the Driving Anger Expression Inventory: Deffenbacher, Lynch, Getting, & Swaim, 2002). Second, the two separate present time perspective subscales of the Zimbardo Time Perspective Inventory (ZTPI), which have not been used in clriving risk research, were employed to better reflect differing driving behaviors and characteristics associated with fatalism and hedonism (Zimbardo & Boyd, 1999). Third, the role of positive driving behaviors seemed important given its recent focus in the literature. Thus, both risky and positive driving behaviors were investigated (ร–zkan & Lajunen, 2005). Based on previous research, hypotheses were tested among the various time perspective orientations and risky and positive driving behaviors. The utility of time perspective as a predictor of driving behavior is discussed. In addition to studying the relationship between time perspective and driving behaviors, the influence of emerging adulthood were explored. Today many individuals are extending the time between adolescence and adulthood (Arnett, 2000). This intermediate stage where individuals are taking more time to explore their options before making long-term commitments was tested for its unique contribution toward predicting driving behavior. Specifically, it was hypothesized that those who score lower on the emerging adult factor would display more risky driving behaviors and fewer positive driving behaviors. Emerging adulthood was also tested in an overall model of risky driving which includes the time perspectives of interest and the control variables of sensation seeking and anger

    Effects of the driving context on the usage of Automated Driver Assistance Systems (ADAS) -Naturalistic Driving Study for ADAS evaluation

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    Automated Driver Assistance Systems (ADAS) are designed to support the driver and enhance the driving experience. Due to ADAS limitations associated with the driving context, the intended use of ADAS functions is often non-transparent for the end-user. The system performance capabilities affected by the continuously changing driving context influence ADAS usage. However, the cumulative effect of the driving context on driver behavior and ADAS usage is insufficiently covered in the ongoing research. This paper aims to investigate and understand how the driving context affects the use of ADAS. Throughout this research, data from a Naturalistic Driving (ND) study was collected and analyzed. The analysis of the ND data helped to register how drivers use ADAS in different driving conditions and indicated several issues associated with ADAS usage. To be able to clarify the outcomes of quantitative sensor-based data analysis, an explanatory sequential mixed-method design was implemented. The method facilitated the subsequent design of qualitative in-depth interviews with the drivers. The combined data analysis allowed a holistic interpretation and evaluation of the findings regarding the effect of the driving context on ADAS usage. The findings warrant consideration of the driving context as a key factor enabling the effective development of ADAS functions.\ua0\ua9 2020 The Author

    International overview on the legal framework for highly automated vehicles

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    The evolution of Autonomous and automated technologies during the last decades has been constant and maintained. All of us can remember an old film, in which they shown us a driverless car, and we thought it was just an unreal object born of filmmakers imagination. However, nowadays Highly Automated Vehicles are a reality, even not in our daily lives. Hardly a day we donโ€™t have news about Tesla launching a new model or Google showing the new features of their autonomous car. But donโ€™t have to travel far away from our borders. Here in Europe we also can find different companies trying, with more or less success depending on with, not to be lagged behind in this race. But today their biggest problem is not only the liability of their innovative technology, but also the legal framework for Highly Automated Vehicles. As a quick summary, in only a few countries they have testing licenses, which not allow them to freely drive, and to the contrary most nearly ban their use. The next milestone in autonomous driving is to build and homogeneous, safe and global legal framework. With this in mind, this paper presents an international overview on the legal framework for Highly Automated Vehicles. We also present de different issues that such technologies have to face to and which they have to overcome in the next years to be a real and daily technology

    ์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

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