Mobility and Aging: Older Drivers’ Visual Searching, Lane Keeping and Coordination

This thesis examined older drivers’ mobility and behaviour through comprehensive measurements of driver-vehicle-environment interaction and investigated the associations between driving behaviour and cognitive functions. Data were collected and analysed for 50 older drivers using eye tracking, GNSS tracking, and GIS. Results showed that poor selective attention, spatial ability and executive function in older drivers adversely affect lane keeping, visual search and coordination. Visual-motor coordination measure is sensitive and effective for driving assessment in older drivers

Exploring Predictors of Older Adults\u27 Performance on a Novel Driving Simulator Task

On a per-mile driven basis, older adults are at increased risk of being involved in an automobile accident. The development and implementation of driving assessment tools is necessary to inform decisions about driving reduction and cessation. Driving simulators are one method of assessing driving performance and safety, however many simulators are cost-prohibitive for most researchers and clinicians. Additionally, while driving performance has been previously explored with respect to clinical populations (e.g., Alzheimer’s Disease), less work has evaluated this topic in a cognitively healthy sample. The present study sought to determine whether a novel, cost-effective driving simulator (Assetto Corsa (AC)) might be useful in the evaluation of driving performance in a sample of cognitively healthy older adults. A total of 53 participants completed a battery of paper-and-pencil and computerized cognitive performance measures and self-reports regarding their driving safety and behaviors, and a subset of participants (n = 35) completed the driving simulator task. Hierarchical regressions revealed that paper-and-pencil measures of simple attention and executive functioning and a computerized measure of processing speed were associated with aspects of driving simulator performance. Pearson correlation coefficients revealed that lower self-rated driving was associated with slower completion of the simulator task, and decrements in several cognitive domains were associated with greater self-reported difficulty driving in various conditions, greater aberrant driving behaviors, and higher likelihood of having legal difficulties as a result of driving (e.g., traffic tickets). Implications for future work are discussed

Efficacy, national/international practices and motivational factors of lifelong driver education for the aging population

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006.Includes bibliographical references (leaves 144-152).In a society facing a significant increase in its aging population, older driver education/training is emerging as a potential solution to help seniors drive more safely, thus maintaining their mobility and quality of life. Nonetheless, sufficient evidence has not emerged directly linking such programs to significant reductions in accident rates for seniors. But older driver education has produced certain outcomes that have been indirectly linked to driver safety. Among these secondary outcomes that led to significant reductions in crash rates are increases in self-regulation (i.e., modifying driving behavior to compensate for certain physical limitations) and a measure of visual/perceptual ability called Useful Field of View (UFOV). Despite some questions surrounding the efficacy of these programs, older driver courses are offered in the United States and internationally among various countries of the Organization for Economic Cooperation and Development (OECD). Many of the courses address similar core topics such as the effects on driving from certain physiological, cognitive changes that accompany aging. On the other hand, the programs can vary more on structural/administrative factors. For example, many courses in Europe offer behind the wheel training to supplement the material learned in the classroom.(cont.) For other courses, including those in the United States, the teaching approach is confined more to lectures. Often, the primary incentive for attending the domestic classes is a reduction in auto insurance premiums upon graduating. But in other states like Massachusetts such a discount is not offered. To more closely examine the motivations of older drivers without such an incentive, a survey was conducted on two sets of adults: one group who enrolled in a senior driver course in Massachusetts and another that chose not to take the course. An analysis of the survey findings indicated the primary reason the first group took the course was to be a "safer driver". In addition, that there are significant differences between the two groups on several fronts, including levels, types of motivation, systematic factors such as demographics and health conditions, and their attitudes about driving and older driver edlucation in general. Based upon the findings on motivation, efficacy, and domestic/international programs in older driver education, some ideas have been formulated for potentially improving the safety benefit of older driver education.(cont.) The proposals cover both structural/administrative (e.g., incentives offered to students, instruction fees), institutional (e.g., formation of public/private partnerships) and curriculum based enhancements. The effectiveness of such courses with these recommended features in reducing crash rates, directly or indirectly through the secondary measures, is a topic for additional research.by Richard Israels.S.M

THE EFFECTIVENESS OF USING AN INTERACTIVE DRIVING SIMULATOR TO IMPROVE DRIVING SKILLS AND ABILITIES FOR TEENS AND YOUNG ADULTS WITH AUTISM SPECTRUM DISORDER WITHIN THE CONTEXT OF A DRIVING BOOTCAMP

Purpose: The purpose of this study was to determine the effectiveness of the interactive driving simulator as an intervention tool for teens and young adults with Autism Spectrum Disorder. Method: A pretest/post-test design was used on an assessment drive on the interactive driving simulator which took place within the context of a Driving Bootcamp. Eight participants with self-reported Autism Spectrum Disorder completed pretesting on the interactive driving simulator on day two of the camp. This was followed by the intervention periods on the interactive driving simulator including: three consecutive days of 30 minutes and six weeks of follow-up sessions two times a week for 30 minutes. Individualized intervention sessions were used to target client-centered driving deficits. Post-testing was completed on the last day of the follow-up sessions. Drives were scored using both the performance measures from the simulator output data and a standardized observational assessment tool (P-Drive). Results: Simulator output data revealed a significant difference between pre and post testing on one measure, total collisions. No significant differences were found between pre and post testing on measures related to: object collisions, pedestrian collisions, sign tickets, times over speed, percentage of time out of lane, and percentage of pedal reaction time. P-Drive average raw scores and calibrated scores demonstrated significant differences between pre and post testing among the participants and had very good interrater reliable between four trained raters. Conclusions: With limited significant differences, simulator output data may not be an effective measure of overall driving performance, although it may be due to the low number of participants. Significant differences on the P-Drive average raw score and calibrated scores suggests the interactive driving simulator to be an effective intervention tool for teens and young adults with Autism Spectrum Disorder. Further, the P-Drive proved to be a useful observational assessment tool to use when examining performance on the interactive driving simulator

Ergonomic criterion in the design of roadside information : letters size methodology

PURPOSE: The purpose of the paper was to develop original letters size methodology for roadside information.METHOD: During moving on the roads, addressed to drivers and passengers is, among others, text information on information boards and signs including in the form of advertisements. Based on ergonomic principles and population data, optimisation can be made in the design of communicative messages. The author reviewed the issues concerning the visual process, optimal visual field, visual field defects, visual acuity characteristics and reading speed. This made it possible to select input data for the design of text information in terms of location in the visual field and the number of words on the sign board.FINDINGS: Based on these analyses, computational algorithms were built to evaluate a proposed or already implemented information message. The developed formulae enabled the preparation of a prototype computer application to support the analysis of information systems.PRACTICAL IMPLICATIONS: The developed methodology makes it possible to integrate ergonomic criteria into the design process of text information. It can increase the effectiveness of information transfer and road safety. The most important areas of application are the design or evaluation of information systems: signs and road signs with text information, advertising for drivers.ORIGINALITY/VALUE: The author has developed a method to evaluate the design and placement of text information systems dedicated to drivers (e.g., text-information signs, advertisements including text), which is based on quantitative data such as the size of the letters, the distance from the word board, the location of the text in the field of vision, the speed of the vehicle.peer-reviewe

NEW VISUAL ASSESSMENT USING STEADY-STATE VISUAL EVOKED POTENTIAL (SSVEP) AND EYE TRACKING

Master'sMASTER OF ENGINEERIN

Eye movements and driving : insights into methodology, individual differences and training

Driving is a complex visuomotor task, and the study of eye movements can provide interesting and detailed insights into driving behaviour. The aim of this thesis was to understand (a) what methods are useful to assess driving behaviour, (b) the reasons we observe differences in eye movements when driving, and (c) offer a possible visual training method. The first experiment compared drivers’ eye movements and hazard perception performance in an active simulated driving task and a passive video driving task. A number of differences were found, including an extended horizontal and vertical visual search and faster response to the hazards in the video task. It was concluded that when measuring driving behaviour in an active task, vision, attention and action interact in a complex manner that is reflected in a specific pattern of eye movements that is different to when driving behaviour is measured using typical video paradigms. The second experiment investigated how cognitive functioning may influence eye movement behaviour when driving. It was found that those with better cognitive functioning exhibited more efficient eye movement behaviour than those with poorer cognitive functioning. The third experiment compared the eye movement and driving behaviour of an older adult population and a younger adult population. There were no differences in the eye movement behaviour. However, the older adults drove significantly slower, suggesting attentional compensation. The final experiment investigated the efficacy of using eye movement videos as a visual training tool for novice drivers. It was found that novice drivers improved their visual search strategy when driving after viewing videos of an expert driver’s eye movements. The results of this thesis helps to provide insights into how the visual system is used for a complex behaviour such as driving. It also furthers the understanding of what may contribute to, and what may prevent, road accidents

Attentional refocusing between time and space in older adults:investigation of neural mechanisms and relation to driving

Older adults have a disproportionately high risk of causing collisions at intersections and causing collisions by failing to notice surrounding road signs or signals. Collisions caused by older drivers seem to result from attentional failures. There is limited research exploring the ability to refocus from orienting attention to events changing in time (i.e. temporal attention) to distributing attention spatially (i.e. spatial attention), a process that is particularly important while driving and, if impaired,could cause collisions. The aims of the project were firstly to assess whether the ability to refocus attention from time to space changes throughout the adult lifespan when assessed with a computer based task and in an ecologically valid scenario during simulated driving, secondly, to use magnetoencephalography (MEG) to identify changes to neural mechanism that might explain difficulties in attentional refocusing, and finally, use mobile electroencephalography to explore the neural mechanisms involved in attentional refocusing while driving. Results demonstrated age related declines in the ability to refocus attention from time to space both in a computer-based task and during simulated driving. MEG recorded in a computer-based attention refocusing task revealed that, compared to younger adults, older and middle-aged adults displayed task-related theta deficits in lower level visual processing areas, and instead, displayed compensatory increases in theta power and phase-related connectivity across frontal regions. Increased frontal lobe recruitment likely reflects enhanced top-down attention to cope with impaired lower level attention mechanisms,supporting compensatory recruitment models of ageing. During simulated driving, older participants displayed slower driving speeds and weaker beta desynchronization in preparation to read a road sign, instead displaying a stronger theta power increase in response to the road sign, further demonstrating neural and behavioural compensatory strategies that are only partially successful.Findings warrant the development of a training programme to improve attentional refocusing between time and space while driving

Crash/Near-Crash: Impact of Secondary Tasks and Real-Time Detection of Distracted Driving

The main goal of this dissertation is to investigate the problem of distracted driving from two different perspectives. First, the identification of possible sources of distraction and their associated crash/near-crash risk. That can assist government officials toward more informed decision-making process, allowing for optimized allocation of available resources to reduce roadway crashes and improve traffic safety. Second, actively counteracting the distracted driving phenomenon by quantitative evaluation of eye glance patterns. This dissertation research consists of two different parts. The first part provides an in-depth analysis for the increased crash/near-crash risk associated with different secondary task activities using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data mining techniques are developed to analyze the distracted driving and crash risk. More specifically, two different models were employed to quantify the increased risk associated with each secondary task: a baseline-category logit model, and a rule mining association model. The baseline-category logit model identified the increased risk in terms of odds ratios, while the A-priori association algorithm detected the associated risks in terms of rules. Each rule was then evaluated based on the lift index. The two models succeeded in ranking all the secondary task activities according to the associated increased crash/near-crash risk efficiently. To actively counteract to the distracted driving phenomenon, a new approach was developed to analyze eye glance patterns and quantify distracted driving behavior under safety and non-Safety Critical Events (SCEs). This approach is then applied to the Naturalistic Engagement in Secondary Tasks (NEST) dataset to investigate how drivers allocate their attention while driving, especially while distracted. The analysis revealed that distracted driving behavior can be well characterized using two new distraction risk indicators. Additional statistical analyses showed that the two indicators increase significantly for SCE compared to normal driving events. Consequently, an artificial neural network (ANN) model was developed to test the SCEs predictability power when accounting for the two new indicators. The ANN model was able to predict the SCEs with an overall accuracy of 96.1%. This outcome can help build reliable algorithms for in-vehicle driving assistance systems to alert drivers before SCEs