1,131 research outputs found

    The psychology of driving automation: A discussion with Professor Don Norman

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    Introducing automation into automobiles had inevitable consequences for the driver and driving. Systems that automate longitudinal and lateral vehicle control may reduce the workload of the driver. This raises questions of what the driver is able to do with this 'spare' attentional capacity. Research in our laboratory suggests that there is unlikely to be any spare capacity because the attentional resources are not 'fixed'. Rather, the resources are inextricably linked to task demand. This paper presents some of the arguments for considering the psychological aspects of the driver when designing automation into automobiles. The arguments are presented in a conversation format, based on discussions with Professor Don Norman. Extracts from relevant papers to support the arguments are presented

    Development of rear-end collision avoidance in automobiles

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    The goal of this work is to develop a Rear-End Collision Avoidance System for automobiles. In order to develop the Rear-end Collision Avoidance System, it is stated that the most important difference from the old practice is the fact that new design approach attempts to completely avoid collision instead of minimizing the damage by over-designing cars. Rear-end collisions are the third highest cause of multiple vehicle fatalities in the U.S. Their cause seems to be a result of poor driver awareness and communication. For example, car brake lights illuminate exactly the same whether the car is slowing, stopping or the driver is simply resting his foot on the pedal. In the development of Rear-End Collision Avoidance System (RECAS), a thorough review of hardware, software, driver/human factors, and current rear-end collision avoidance systems are included. Key sensor technologies are identified and reviewed in an attempt to ease the design effort. The characteristics and capabilities of alternative and emerging sensor technologies are also described and their performance compared. In designing a RECAS the first component is to monitor the distance and speed of the car ahead. If an unsafe condition is detected a warning is issued and the vehicle is decelerated (if necessary). The second component in the design effort utilizes the illumination of independent segments of brake lights corresponding to the stopping condition of the car. This communicates the stopping intensity to the following driver. The RECAS is designed the using the LabVIEW software. The simulation is designed to meet several criteria: System warnings should result in a minimum load on driver attention, and the system should also perform well in a variety of driving conditions. In order to illustrate and test the proposed RECAS methods, a Java program has been developed. This simulation animates a multi-car, multi-lane highway environment where car speeds are assigned randomly, and the proposed RECAS approaches demonstrate rear-end collision avoidance successfully. The Java simulation is an applet, which is easily accessible through the World Wide Web and also can be tested for different angles of the sensor

    PFL-LSTR: A privacy-preserving framework for driver intention inference based on in-vehicle and out-vehicle information

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    Intelligent vehicle anticipation of the movement intentions of other drivers can reduce collisions. Typically, when a human driver of another vehicle (referred to as the target vehicle) engages in specific behaviors such as checking the rearview mirror prior to lane change, a valuable clue is therein provided on the intentions of the target vehicle's driver. Furthermore, the target driver's intentions can be influenced and shaped by their driving environment. For example, if the target vehicle is too close to a leading vehicle, it may renege the lane change decision. On the other hand, a following vehicle in the target lane is too close to the target vehicle could lead to its reversal of the decision to change lanes. Knowledge of such intentions of all vehicles in a traffic stream can help enhance traffic safety. Unfortunately, such information is often captured in the form of images/videos. Utilization of personally identifiable data to train a general model could violate user privacy. Federated Learning (FL) is a promising tool to resolve this conundrum. FL efficiently trains models without exposing the underlying data. This paper introduces a Personalized Federated Learning (PFL) model embedded a long short-term transformer (LSTR) framework. The framework predicts drivers' intentions by leveraging in-vehicle videos (of driver movement, gestures, and expressions) and out-of-vehicle videos (of the vehicle's surroundings - frontal/rear areas). The proposed PFL-LSTR framework is trained and tested through real-world driving data collected from human drivers at Interstate 65 in Indiana. The results suggest that the PFL-LSTR exhibits high adaptability and high precision, and that out-of-vehicle information (particularly, the driver's rear-mirror viewing actions) is important because it helps reduce false positives and thereby enhances the precision of driver intention inference.Comment: Submitted for presentation only at the 2024 Annual Meeting of the Transportation Research Boar

    Driver Assistance System and Feedback for Hybrid Electric Vehicles Using Sensor Fusion

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    abstract: Transportation plays a significant role in every human's life. Numerous factors, such as cost of living, available amenities, work style, to name a few, play a vital role in determining the amount of travel time. Such factors, among others, led in part to an increased need for private transportation and, consequently, leading to an increase in the purchase of private cars. Also, road safety was impacted by numerous factors such as Driving Under Influence (DUI), driverโ€™s distraction due to the increase in the use of mobile devices while driving. These factors led to an increasing need for an Advanced Driver Assistance System (ADAS) to help the driver stay aware of the environment and to improve road safety. EcoCAR3 is one of the Advanced Vehicle Technology Competitions, sponsored by the United States Department of Energy (DoE) and managed by Argonne National Laboratory in partnership with the North American automotive industry. Students are challenged beyond the traditional classroom environment in these competitions, where they redesign a donated production vehicle to improve energy efficiency and to meet emission standards while maintaining the features that are attractive to the customer, including but not limited to performance, consumer acceptability, safety, and cost. This thesis presents a driver assistance system interface that was implemented as part of EcoCAR3, including the adopted sensors, hardware and software components, system implementation, validation, and testing. The implemented driver assistance system uses a combination of range measurement sensors to determine the distance, relative location, & the relative velocity of obstacles and surrounding objects together with a computer vision algorithm for obstacle detection and classification. The sensor system and vision system were tested individually and then combined within the overall system. Also, a visual and audio feedback system was designed and implemented to provide timely feedback for the driver as an attempt to enhance situational awareness and improve safety. Since the driver assistance system was designed and developed as part of a DoE sponsored competition, the system needed to satisfy competition requirements and rules. This work attempted to optimize the system in terms of performance, robustness, and cost while satisfying these constraints.Dissertation/ThesisMasters Thesis Electrical Engineering 201

    ์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

    Driver lane change intention inference for intelligent vehicles: framework, survey, and challenges

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    Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver status since ADAS share the vehicle control authorities with the human driver. This study provides an overview of the ego-vehicle driver intention inference (DII), which mainly focus on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consists of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference (LCII) system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted

    Mitigating blind spot collision utilizing ultrasonic gap perimeter sensor

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    Failure to identify the vehicle by the side of the vehicle or in other word as blind spot area, especially larger vehicles are one of the causes of the accident. For some drivers, the simple solution is to place an additional side mirror. However, it is not the best solution because this additional side mirrors do not provide an accurate picture of actual or estimated distance to the object or another vehicle. The objective of this project is to identify the causes of automobile collisions, notably the side collision impact causes by the blind spot, to develop a system that can detect the presence vehicles on the side and to develop a system that are affordable for normal car users. To achieve this objective, flow chart was designed to help write coding using Arduino 1.0.2 and design hardware. This system can detect the obstacle within range 2cm to 320cm from the edge of the project vehicle. Before this system developed, the survey was conducted to determine what the driver wants. After that, the design process is carried out. The input to this system is Ping ultrasonic sensor, LCD, LED, and siren for the output part. LCD and LED were displaying the distance from the vehicle and the siren will be switched on to warn the driver when have obstacle in the blind spot area. As a conclusion, the Mitigating Blind Spot Collision Utilizing Ultrasonic Gap Perimeter Sensor System has successfully completed. This system able to detect the presence of other vehicles on the side of the project vehicle, especially in the blind spot area and will alert the driver when the vehicle is nearby when the alarm system is operated. The efficiency of this system to detect objects in the blind spot area is 79.82%. Others, it will give the display value less than one second after obstacle exists in front of the sensor. This operating time is most important because if the system is slow, the main function of this system to detect the obstacle in the blind spot area is not achieved
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