Browsing by Author "Kurokawa, Ko"

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    Development of an evaluation program for automotive instrument panel design
    Kurokawa, Ko (Virginia Tech, 1990-10-31)
    This dissertation research was a part of a multi-year research effort, objectives of which were (I) to characterize attentional demands of drivers performing automoltive instrument panel (IP) tasks, (2) to develop a methodology to quantify the driver attentional demands, (3) to examine a variety of factors which influence the visual attentional demand (VAD) and concurrent manual demand (CMD) through a comprehensive review of previous studies and a series of experiments, and (4) to develop a computer program to evaluate contemporary and future automotive IP designs on the basis of their attentional demands. In the first part of this dissertation, an extensive literature review of methodologies and findings concerning automotive IP task performance is presented. Most of the earlier studies reported task completion times (also referred to as response times and transaction times), which did not provide a precise detail of the operation of an instrument. More recent studies, on the other hand, recorded the driver's eye movements while performing an IP task, and measures of VAD were analyzed. Among the variety of methodologies to measure eye movements, the limbus and pupil tracking technique using a commercially available video cassette recorder (VCR) represents an ideal compromise among precision, cost, and size/weight. Combined with the traditional response time measure, the number and average length of glances, which are determined by a frame-by-frame analysis of the eye movement recording tape, allow a quantitative evaluation of driver IP task performance. A series of three experiments conducted in the moving-base driving simulator in the Vehicle Analysis and Simulation Laboratory forms the second part of this dissertation. The objectives of these experiments were (1) to validate the use of the driving simulator for collecting driver performance data on IP tasks, (2) to examine factors which influence the simulated driving workload, e.g., introduction of random crosswind and road curvature, (3) to expand the existing database on conventional IP tasks, (4) to examine the effects of IP macro- and micro-clutter on driver task performance, and (5) to investigate the issues related to control labelling, i.e., random versus sequential labelling and label abbreviation. Some of the important findings from the simulator experiments were (1) the driver IP task performance data collected under the zero crosswind and straight road conditions were found to be acceptably close to those in the in-car, on-road study during the first phase of this research program (Hayes, Kurokawa, and Wierwille, 1988), (2) IP macroclutter, represented by the number of instruments in the IP, was linearly related to the complexity of an IP task, reflected in the number of glances to IP, (3) IP microclutter, represented by the number of controls within an instrument, was linearly related to both complexity (number of glances to IP) and difficulty (average length of glances to IP) of an IP task, and (4) concise and distinct labels were more desirable as they required fewer glances and were located more quickly than their fully spelled counterparts. In the third part of this dissertation, a computer program (IPanalyzer) which was developed to aid automotive IP designers in evaluation of an IP design is discussed. Users of IPanalyzer can obtain driver IP task performance estimates (1) empirically from the existing experimental data, (2) by assessing the difficulty, complexity, and manual demand of a given task, or (3) by decomposing a task of interest into elements and categorizing them by their behavioral characteristics. Instructions for using IPanalyzer are supplemented by detailed descriptions and discussions of the data on which the driver IP task performance estimates are based. Finally, limitations of the current evaluation program are discussed, and a direction for future research and development are suggested.
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    The effect of rotation on legibility of dot-matrix characters
    Kurokawa, Ko (Virginia Tech, 1988-09-05)
    When dot-matrix characters are rotated, as might be the case in a moving map display, their dot-matrix patterns are distorted and their legibility is thus affected. In this experiment, 16 subjects performed a random search task, in which they were asked to look for a target in a random character pattern. The independent variables were the direction (clockwise or counterclockwise) and the angle of stimulus image rotation, and the target character's distance from the center of screen, which was also the center of rotation; the dependent variables were response time and response correctness. Significant effects were found in the angle of rotation, the target character's distance from the center, and the target character. The results indicate that (1) no angle-dependent mechanism is involved in performing this task and the angle of rotation influences recognition mainly through the distortion of dot-matrix patterns, (2) the target character's (radial) distance from the center of screen is the determining factor for search time, while the x and y coordinates of the target contributed to dot-matrix pattern distortion, and (3) the target characters interacted differently with the angle and distance factors to determine the extent of distortion and their legibility. Means to quantify the extent of distortion were discussed and the direction for future research is offered.
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    An On-Road Assessment of Driver Secondary Task Engagement and Performance during Assisted & Automated Driving
    Britten, Nicholas (Virginia Tech, 2021-12-15)
    Increasingly, many of today’s vehicles offer Society of Automotive Engineers (SAE) partially automated driving (PAD) and a limited number of SAE conditionally automated vehicles (CAD) are being developed. Vehicles with PAD systems support the driver through longitudinal and lateral control inputs. However, during PAD the driver must be prepared to take control of the vehicle at any time, requiring them to monitor the environment and PAD system. In contrast, during CAD the driver is not required to monitor the environment or system but must take control when prompted by the system. Given the ability of CAD vehicles to operate in PAD and manual driving, it is important to consider drivers’ mode awareness, that is, their ability to follow the state of the automated system and predict the implications of this status for vehicle control and monitoring responsibilities. In addition, since CAD does not require drivers to keep their visual or attentional resources on the driving task or environment, drivers are allowed to perform secondary tasks (i.e., non-driving related tasks (NDRTs)). Given that drivers will freely choose what types of tasks they do during CAD it is important to build an understanding of whether drivers will choose to engage in NDRTs in the CAD state, and drivers’ ability to perform NDRTs during CAD. To investigate driver’s mode awareness after transitions between modes, their willingness to engage in NDRTs, and their ability to perform scheduled smartphone NDRTs, an on-road experiment was conducted using the Wizard-of-Oz (WoZ) method to simulate a vehicle capable of Assisted Driving (similar to PAD) and Automated Driving (similar to CAD). A total of 36 drivers completed the on-road experiment, and experienced stable periods of manual driving, Assisted driving, and Automated driving, as well as transitions between these modes. After each transition, participants’ mode awareness was measured. Drivers’ performance of NDRTs and behavioral adaptation during Automated Driving was assessed by asking them to complete scheduled tasks on their smartphones. To measure driver willingness to engage in unscripted NDRTs during automated driving, participants were allowed to freely choose to engage in smartphone NDRTs between the scheduled tasks. It was hypothesized that drivers’ mode awareness of Assisted and Automated Driving and their willingness to engage and perform NDRTs during Automated Driving would increase with system exposure over the five planned activation periods of Automated Driving. Results from a mixed-model ANOVA showed that participants’ mode awareness of their role in Automated Driving statistically significantly increased from the first activation to the subsequent activations. There was no statistically significant effect of activation period on drivers’ willingness to engage in NDRTs, as measured by the mean percentage of time drivers chose to engage in NDRTs during Automated Driving, or driver’s ability to perform tasks, as measured by the mean task completion time of the experimenter administered smartphone NDRTs. The mean number of participants who chose to engage in an NDRT (73.8%) and the percentage of time spent on NDRTs per Automated Driving activation period (M=20.37%; SD=23.9), indicated that drivers were willing to engage in NDRTs during Automated Driving. In addition, drivers showed a high level of task performance, completing 95% of the scheduled NDRTs correctly. Altogether, these results suggest that drivers are willing to engage in and perform NDRTs during Automated Driving and that driver behavior during Automated Driving is consistent and stable during a two-hour exposure period. Finally, the findings indicate that requiring the participant to control the vehicle manually for a brief period prior to transitioning to a level of automation that allows the driver to take their visual and attentional resources away from the roadway environment results in statistically significantly less NDRT engagement compared to when participants transition directly to this level of automation. Overall, the findings from this study have methodological and potential system design implications that can help guide the future research on and design of automated driving systems.