1,131 research outputs found
The psychology of driving automation: A discussion with Professor Don Norman
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
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
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User-centred car design and the role of feedback in driving
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A survey of car manufacturers reveals an impressive list of upcoming technologies, the combined effect of which is likely to have a profound impact upon feedback to the driver. Feedback is information that the situation provides back to the driver and is specified with reference to content, source, and timing. Feedback quality is achieved when the information requirements of the task, derived from a new task analysis of driving, are matched to the sources, content, and timing of feedback provided by the environment and the vehicle. An exploratory on-road study begins by observing that better quality feedback is
implicated in increasing driver's situational awareness (even though drivers have little self awareness of this fact), and optimising mental workload. The exploratory level of analysis builds into the experimental, whereby a highly controlled simulator study replicates and builds upon these findings. Feedback is again seen to positively influence situational awareness, where changes in driver's confidence ratings as to the presence or absence of feedback information in the simulation were observed, according to the modality of feedback presented. This was achieved with a probe recall paradigm, and using psychophysical techniques as a
useful extension to the Situational awareness Global Assessment Technique
(SAGAI). Similarly, an analysis of mental workload via the NASA TLX self report
questionnaire demonstrates that a combination of visual, steering force feedback and auditory feedback gives rise to lower mental workload, lower driver frustration, and lower, though possibly more realistic self ratings of performance. This knowledge can be discussed with reference to a feedback framework of driving that provides the theoretical backdrop to the key psychological variables implicated in driving task performance. Overall, the findings contribute to knowledge in terms of new and imaginative ways of designing future vehicle technologies in order to maximise safety, efficiency, and enjoyment.This research is funded by the Hamilton Research Studentship
PFL-LSTR: A privacy-preserving framework for driver intention inference based on in-vehicle and out-vehicle information
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
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), driverโs 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
์ฐจ๋์ฉ ํค๋์ ๋์คํ๋ ์ด ์ค๊ณ์ ๊ดํ ์ธ๊ฐ๊ณตํ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ฐ์
๊ณตํ๊ณผ, 2020. 8. ๋ฐ์ฐ์ง.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.์๋์ฐจ ํค๋์
๋์คํ๋ ์ด๋ ์ฐจ๋ด ๋์คํ๋ ์ด ์ค ํ๋๋ก ์ด์ ์์๊ฒ ํ์ํ ์ ๋ณด๋ฅผ ์ ๋ฐฉ์ ํ์ํจ์ผ๋ก์จ, ์ด์ ์๊ฐ ์ด์ ์ ํ๋ ๋์ ์ ๋ฐฉ์ผ๋ก ์์ ์ ์ ์งํ ์ ์๊ฒ ๋์์ค๋ค. ์ด๋ฅผ ํตํด ์ด์ ์์ ์ฃผ์ ๋ถ์ฐ์ ์ค์ด๊ณ , ์์ ์ ํฅ์์ํค๋๋ฐ ๋์์ด ๋ ์ ์๋ค. ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด ์์คํ
์ ์ฝ 30๋
์ ์ด์ ์์ ์์ ์ ํฅ์์ํค๊ธฐ ์ํ ์๋จ์ผ๋ก ์๋์ฐจ ์ฐ์
์ ์ฒ์ ๋์
๋ ์ด๋๋ก ํ์ฌ๊น์ง ๋ค์ํ ์์ฉ์ฐจ์์ ์ฌ์ฉ๋๊ณ ์๋ค. ์์ ๊ณผ ํธ์ ์ธก๋ฉด์์ ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด์ ์ฌ์ฉ์ ์ ์ ๋ ์ฆ๊ฐํ ๊ฒ์ผ๋ก ์์๋๋ค.
๊ทธ๋ฌ๋ ์ด๋ฌํ ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด์ ์ ์ฌ์ ์ด์ ๊ณผ ๋ฐ์ ๊ฐ๋ฅ์ฑ์๋ ๋ถ๊ตฌํ๊ณ , ์ ์ฉํ ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด๋ฅผ ์ค๊ณํ๋ ๊ฒ์ ์ฌ์ ํ ์ด๋ ค์ด ๋ฌธ์ ์ด๋ค. ์ด์ ๋ณธ ์ฐ๊ตฌ๋ ์ด๋ฌํ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ณ , ๊ถ๊ทน์ ์ผ๋ก ์ ์ฉํ ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด ์ค๊ณ์ ๊ธฐ์ฌํ๊ณ ์ ์ด 4๊ฐ์ง ์ฐ๊ตฌ๋ฅผ ์ํํ์๋ค.
์ฒซ ๋ฒ์งธ ์ฐ๊ตฌ๋ ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด์ ๊ธฐ๋ฅ ์๊ตฌ ์ฌํญ๊ณผ ๊ด๋ จ๋ ๊ฒ์ผ๋ก์, ํค๋์
๋์คํ๋ ์ด ์์คํ
์ ํตํด ์ด๋ค ์ ๋ณด๋ฅผ ์ ๊ณตํ ๊ฒ์ธ๊ฐ์ ๋ํ ๋ต์ ๊ตฌํ๊ณ ์ ํ์๋ค. ์ด์ ์ฃผ์ ์๋์ฐจ ์ ์กฐ์
์ฒด๋ค์ ํค๋์
๋์คํ๋ ์ด ์ ํ๋ค๊ณผ, ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด์ ๋ค์ํ ๊ธฐ๋ฅ๋ค์ ์ ์ํ ํ์ ์ฐ๊ตฌ, ๊ทธ๋ฆฌ๊ณ ์ด์ ์์ ์ ๋ณด ์๊ตฌ ์ฌํญ๋ค์ ์ฒด๊ณ์ ๋ฌธํ ๊ณ ์ฐฐ ๋ฐฉ๋ฒ๋ก ์ ํตํด ํฌ๊ด์ ์ผ๋ก ์กฐ์ฌํ์๋ค. ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด์ ๊ธฐ๋ฅ์ ์๊ตฌ ์ฌํญ์ ๋ํ์ฌ ๊ฐ๋ฐ์, ์ฐ๊ตฌ์, ์ฌ์ฉ์ ์ธก๋ฉด์ ๋ชจ๋ ๊ณ ๋ คํ ํตํฉ๋ ์ง์์ ์ ๋ฌํ๊ณ , ์ด๋ฅผ ํตํด ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด์ ๊ธฐ๋ฅ ์๊ตฌ ์ฌํญ์ ๋ํ ํฅํ ์ฐ๊ตฌ ๋ฐฉํฅ์ ์ ์ํ์๋ค.
๋ ๋ฒ์งธ ์ฐ๊ตฌ๋ ์์ ๊ด๋ จ ์ ๋ณด๋ฅผ ์ ๊ณตํ๋ ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด์ ์ธํฐํ์ด์ค ์ค๊ณ์ ๊ด๋ จ๋ ๊ฒ์ผ๋ก, ํค๋์
๋์คํ๋ ์ด ์์คํ
์ ํตํด ์์ ๊ด๋ จ ์ ๋ณด๋ฅผ ์ด๋ป๊ฒ ์ ๊ณตํ ๊ฒ์ธ๊ฐ์ ๋ํ ๋ต์ ๊ตฌํ๊ณ ์ ํ์๋ค. ์ค์ ์๋์ฐจ๋ค์ ํค๋์
๋์คํ๋ ์ด ์์คํ
์์๋ ์ด๋ค ๋์คํ๋ ์ด ์ปจ์
๋ค์ด ์ฌ์ฉ๋์๋์ง, ๊ทธ๋ฆฌ๊ณ ํ๊ณ์์ ์ ์๋ ๋์คํ๋ ์ด ์ปจ์
๋ค์๋ ์ด๋ค ๊ฒ๋ค์ด ์๋์ง ์ฒด๊ณ์ ๋ฌธํ ๊ณ ์ฐฐ ๋ฐฉ๋ฒ๋ก ์ ํตํด ๊ฒํ ํ์๋ค. ๊ฒํ ๋ ๊ฒฐ๊ณผ๋ ๊ฐ ๋์คํ๋ ์ด์ ๊ธฐ๋ฅ๊ณผ ๊ตฌ์กฐ, ๊ทธ๋ฆฌ๊ณ ์๋ ๋ฐฉ์์ ๋ฐ๋ผ ์ ๋ฆฌ๋์๊ณ , ๊ด๋ จ๋ ์ธ๊ฐ๊ณตํ์ ๋์คํ๋ ์ด ์ค๊ณ ์์น๊ณผ ์คํ์ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ค์ ํจ๊ป ๊ฒํ ํ์๋ค. ๊ฒํ ๋ ๊ฒฐ๊ณผ๋ฅผ ๋ฐํ์ผ๋ก ์์ ๊ด๋ จ ์ ๋ณด๋ฅผ ์ ๊ณตํ๋ ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด์ ์ธํฐํ์ด์ค ์ค๊ณ์ ๋ํ ํฅํ ์ฐ๊ตฌ ๋ฐฉํฅ์ ์ ์ํ์๋ค.
์ธ ๋ฒ์งธ ์ฐ๊ตฌ๋ ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด ๊ธฐ๋ฐ์ ์ ์ด๊ถ ์ ํ ๊ด๋ จ ์ธํฐํ์ด์ค ์ค๊ณ์ ํ๊ฐ์ ๊ดํ ๊ฒ์ด๋ค. ์ ์ด๊ถ ์ ํ์ด๋, ์์จ์ฃผํ ์ํ์์ ์ด์ ์๊ฐ ์ง์ ์ด์ ์ ํ๋ ์๋ ์ด์ ์ํ๋ก ์ ํ์ด ๋๋ ๊ฒ์ ์๋ฏธํ๋ค. ๋ฐ๋ผ์ ๊ฐ์์ค๋ฐ ์ ์ด๊ถ ์ ํ ์์ฒญ์ด ๋ฐ์ํ๋ ๊ฒฝ์ฐ, ์ด์ ์๊ฐ ์์ ํ๊ฒ ๋์ฒํ๊ธฐ ์ํด์๋ ๋น ๋ฅธ ์ํฉ ํ์
๊ณผ ์์ฌ ๊ฒฐ์ ์ด ํ์ํ๊ฒ ๋๊ณ , ์ด๋ฅผ ํจ๊ณผ์ ์ผ๋ก ๋์์ฃผ๊ธฐ ์ํ ์ธํฐํ์ด์ค ์ค๊ณ์ ๋ํด ์ฐ๊ตฌํ ํ์์ฑ์ด ์๋ค. ์ด์ ๋ณธ ์ฐ๊ตฌ์์๋ ์๋์ฐจ ํค๋์
๋์คํ๋ ์ด ๊ธฐ๋ฐ์ ์ด 4๊ฐ์ ์ ์ด๊ถ ์ ํ ๊ด๋ จ ๋์คํ๋ ์ด(๊ธฐ์ค ๋์คํ๋ ์ด, ๋ฏธ๋๋งต ๋์คํ๋ ์ด, ํ์ดํ ๋์คํ๋ ์ด, ๋ฏธ๋๋งต๊ณผ ํ์ดํ ๋์คํ๋ ์ด)๋ฅผ ์ ์ํ์๊ณ , ์ ์๋ ๋์คํ๋ ์ด ๋์๋ค์ ์ฃผํ ์๋ฎฌ๋ ์ดํฐ ์คํ์ ํตํด ์ ์ด๊ถ ์ ํ ์ํ ๋ฅ๋ ฅ๊ณผ ์๊ตฌ์ ์์ง์ ํจํด, ๊ทธ๋ฆฌ๊ณ ์ฌ์ฉ์์ ์ฃผ๊ด์ ํ๊ฐ ์ธก๋ฉด์์ ํ๊ฐ๋์๋ค. ๋ํ ์ ์๋ ๋์คํ๋ ์ด ๋์๋ค์ ๋ํด ์ด์ ์๋ค์ ์ด๊ธฐ ์ ๋ขฐ๋ ๊ฐ์ ์ธก์ ํ์ฌ ๊ฐ ๋์คํ๋ ์ด์ ๋ฐ๋ฅธ ์ด์ ์๋ค์ ํ๊ท ์ ๋ขฐ๋ ์ ์์ ๋ฐ๋ผ ์ ์ด๊ถ ์ ํ ์ํ ๋ฅ๋ ฅ๊ณผ ์๊ตฌ์ ์์ง์ ํจํด, ๊ทธ๋ฆฌ๊ณ ์ฃผ๊ด์ ํ๊ฐ๊ฐ ์ด๋ป๊ฒ ๋ฌ๋ผ์ง๋์ง ๋ถ์ํ์๋ค. ์คํ ๊ฒฐ๊ณผ, ์ ์ด๊ถ ์ ํ ์ํฉ์์ ์๋ํ๋ ์์คํ
์ด ์ ์ํ๋ ์ ๋ณด์ ๊ทธ์ ๊ด๋ จ๋ ์ฃผ๋ณ ์ํฉ ์ ๋ณด๋ฅผ ํจ๊ป ์ ์ํด ์ฃผ๋ ๋์คํ๋ ์ด๊ฐ ๊ฐ์ฅ ์ข์ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ฌ์ฃผ์๋ค. ๋ํ ๊ฐ ๋์คํ๋ ์ด์ ๋ํ ์ด์ ์์ ์ด๊ธฐ ์ ๋ขฐ๋ ์ ์๋ ๋์คํ๋ ์ด์ ์ค์ ์ฌ์ฉ ํํ์ ๋ฐ์ ํ ๊ด๋ จ์ด ์์์ ์ ์ ์์๋ค. ์ ๋ขฐ๋ ์ ์์ ๋ฐ๋ผ ์ ๋ขฐ๋๊ฐ ๋์ ๊ทธ๋ฃน๊ณผ ๋ฎ์ ๊ทธ๋ฃน์ผ๋ก ๋ถ๋ฅ๋์๊ณ , ์ ๋ขฐ๋๊ฐ ๋์ ๊ทธ๋ฃน์ ์ ์๋ ๋์คํ๋ ์ด๋ค์ด ๋ณด์ฌ์ฃผ๋ ์ ๋ณด๋ฅผ ์ฃผ๋ก ๋ฏฟ๊ณ ๋ฐ๋ฅด๋ ๊ฒฝํฅ์ด ์์๋ ๋ฐ๋ฉด, ์ ๋ขฐ๋๊ฐ ๋ฎ์ ๊ทธ๋ฃน์ ๋ฃธ ๋ฏธ๋ฌ๋ ์ฌ์ด๋ ๋ฏธ๋ฌ๋ฅผ ํตํด ์ฃผ๋ณ ์ํฉ ์ ๋ณด๋ฅผ ๋ ํ์ธ ํ๋ ๊ฒฝํฅ์ ๋ณด์๋ค.
๋ค ๋ฒ์งธ ์ฐ๊ตฌ๋ ์ ๋ฉด ์ ๋ฆฌ์ฐฝ์์์ ์ธํฐ๋ํฐ๋ธ ํค๋์
๋์คํ๋ ์ด์ ์ต์ ์์น๋ฅผ ๊ฒฐ์ ํ๋ ๊ฒ์ผ๋ก์ ์ฃผํ ์๋ฎฌ๋ ์ดํฐ ์คํ์ ํตํด ๋์คํ๋ ์ด์ ์์น์ ๋ฐ๋ผ ์ด์ ์์ ์ฃผํ ์ํ ๋ฅ๋ ฅ, ์ธํฐ๋ํฐ๋ธ ๋์คํ๋ ์ด ์กฐ์ ๊ด๋ จ ๊ณผ์
์ํ ๋ฅ๋ ฅ, ์๊ฐ์ ์ฃผ์ ๋ถ์ฐ, ์ ํธ๋, ๊ทธ๋ฆฌ๊ณ ์์
๋ถํ๊ฐ ํ๊ฐ๋์๋ค. ํค๋์
๋์คํ๋ ์ด์ ์์น๋ ์ ๋ฉด ์ ๋ฆฌ์ฐฝ์์ ์ผ์ ํ ๊ฐ๊ฒฉ์ผ๋ก ์ด 9๊ฐ์ ์์น๊ฐ ๊ณ ๋ ค๋์๋ค. ๋ณธ ์ฐ๊ตฌ์์ ํ์ฉ๋ ์ธํฐ๋ํฐ๋ธ ๋์คํ๋ ์ด๋ ์์
์ ํ์ ์ํ ์คํฌ๋กค ๋ฐฉ์์ ๋จ์ผ ๋์คํ๋ ์ด์๊ณ , ์ด์ ๋์ ์ฅ์ฐฉ๋ ๋ฒํผ์ ํตํด ๋์คํ๋ ์ด๋ฅผ ์กฐ์ํ์๋ค. ์คํ ๊ฒฐ๊ณผ, ์ธํฐ๋ํฐ๋ธ ํค๋์
๋์คํ๋ ์ด์ ์์น๊ฐ ๋ชจ๋ ํ๊ฐ ์ฒ๋, ์ฆ ์ฃผํ ์ํ ๋ฅ๋ ฅ, ๋์คํ๋ ์ด ์กฐ์ ๊ณผ์
์ํ ๋ฅ๋ ฅ, ์๊ฐ์ ์ฃผ์ ๋ถ์ฐ, ์ ํธ๋, ๊ทธ๋ฆฌ๊ณ ์์
๋ถํ์ ์ํฅ์ ๋ฏธ์นจ์ ์ ์ ์์๋ค. ๋ชจ๋ ํ๊ฐ ์งํ๋ฅผ ๊ณ ๋ คํ์ ๋, ์ธํฐ๋ํฐ๋ธ ํค๋์
๋์คํ๋ ์ด์ ์์น๋ ์ด์ ์๊ฐ ๋๋ฐ๋ก ์ ๋ฐฉ์ ๋ฐ๋ผ๋ณผ ๋์ ์์ผ ๊ตฌ๊ฐ, ์ฆ ์ ๋ฉด ์ ๋ฆฌ์ฐฝ์์์ ์ผ์ชฝ ์๋ ๋ถ๊ทผ์ด ๊ฐ์ฅ ์ต์ ์ธ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค.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
๊ตญ๋ฌธ์ด๋ก 187Docto
Driver lane change intention inference for intelligent vehicles: framework, survey, and challenges
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
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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