The Effect of Age on Decision Making During Unprotected Turns Across Oncoming Traffic

The present study examined whether age-related differences in quantitative measures of left-tum performance could explain older drivers\u27 increased susceptibility to crashing while making unprotected left turns across traffic. Older and younger adults made left turns across traffic in a driving simulator. Time to decide to turn, time to negotiate the turn, the size of the accepted gap, gap clearance, and time to collision with an oncoming vehicle were measured. Significant effects of age were found in decision time, turn time and gap size. A significant interaction between age group and the speed of oncoming traffic was obtained for decision time. Implications for older adult\u27s safety and future directions are discussed

Assessment of the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving

We attempted to model attention allocation of experienced drivers using the SEEV model. Unlike previous attempts, the present work looked at attention to entities (vehicles, signs, traffic control devices) in the outside world rather than considering the outside world as a unitary construct. Model parameters were generated from rankings of entities by experienced drivers. Experienced drivers drove a scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Eye movements were monitored during the driving session. The results of fitting the observed eye movement data to our SEEV model were poor, and were no better than fitting the data to a randomized SEEV model. A number of explanations for this are discussed

Comparison of Novice and Experienced Drivers Using the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving

We compared the eye movements of novice drivers and experienced drivers while they drove a simulated driving scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Cassavaugh, Bos, McDonald, Gunaratne, & Backs (2013) attempted to model attention allocation of experienced drivers using the SEEV model. Here we compared two SEEV model fits between those experienced drivers and a sample of novice drivers. The first was a simplified model and the second was a more complex intersection model. The observed eye movement data was found to be a good fit to the simplified model for both experienced (R2 = 0.88) and novice drivers (R2 = 0.30). Like the previous results of the intersection model for the experienced drivers, the fit of the observed eye movement data to the intersection model for novice drivers was poor, and was no better than fitting the data to a randomized SEEV model. We concluded based on the simplified SEEV model, fixation count and fixation variance that experienced drivers were found to be more efficient at distributing their visual search compared to novice drivers

Comparison of Novice and Experienced Drivers Using the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving

We compared the eye movements of novice drivers and experienced drivers while they drove a simulated driving scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Cassavaugh, Bos, McDonald, Gunaratne, & Backs (2013) attempted to model attention allocation of experienced drivers using the SEEV model. Here we compared two SEEV model fits between those experienced drivers and a sample of novice drivers. The first was a simplified model and the second was a more complex intersection model. The observed eye movement data was found to be a good fit to the simplified model for both experienced (R2 = 0.88) and novice drivers (R2 = 0.30). Like the previous results of the intersection model for the experienced drivers, the fit of the observed eye movement data to the intersection model for novice drivers was poor, and was no better than fitting the data to a randomized SEEV model. We concluded based on the simplified SEEV model, fixation count and fixation variance that experienced drivers were found to be more efficient at distributing their visual search compared to novice drivers

Transfer of Skill From a Computer -Based Training Program to Driving in a Simulated Environment

140 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.Drivers over the age of about 70 years are at higher risk than other drivers for fatal crash involvement. As the U.S. population ages and the number of drivers over 70 increases, older drivers' safety becomes more of an issue for them and the drivers on the road with them. Working memory, visual attention, decision making, manual control and other cognitive abilities are involved in safe driving. Age-related declines in these abilities may contribute to this risk as driving is a complex task which taps these and other abilities. Training improves performance on these abilities and has been shown to transfer from a training protocol to a real-world task. The present research developed a computer-based multi-task training program aimed at improving performance in selective attention, working memory, manual tracking and taskcoordination ability. Older adults' driving performance was first assessed in a driving simulator. Participants were asked to follow a lead vehicle and perform working-memory and attention side-tasks. Eight days of multi-task variable priority training was then provided to a randomlyassigned training group. The control group played computer card games for eight days. Drivers' performance in the simulator was then reassessed. Driving performance was predicted to improve more in the training group than in the control group. Analysis of the training data showed that the training was effective in improving performance on the trained tasks. No significant transfer of training effects were found for driving performance. Some transfer of training effects were found in the side-task measures (e.g. visual selective attention and 1-back working memory tasks), suggesting that there was some transfer from the training tasks to the driving tasks, particularly in more difficult conditions. Regression analysis demonstrated that a participant's training effect size (difference between performance in the first vs. last training session) was a significant predictor of some measures of driving performance (e.g. lane position, following distance).U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Development of a 3D immersive videogame to improve arm-postural coordination in patients with TBI

Abstract Background Traumatic brain injury (TBI) disrupts the central and executive mechanisms of arm(s) and postural (trunk and legs) coordination. To address these issues, we developed a 3D immersive videogame-- Octopus. The game was developed using the basic principles of videogame design and previous experience of using videogames for rehabilitation of patients with acquired brain injuries. Unlike many other custom-designed virtual environments, Octopus included an actual gaming component with a system of multiple rewards, making the game challenging, competitive, motivating and fun. Effect of a short-term practice with the Octopus game on arm-postural coordination in patients with TBI was tested. Methods The game was developed using WorldViz Vizard software, integrated with the Qualysis system for motion analysis. Avatars of the participant's hands precisely reproducing the real-time kinematic patterns were synchronized with the simulated environment, presented in the first person 3D view on an 82-inch DLP screen. 13 individuals with mild-to-moderate manifestations of TBI participated in the study. While standing in front of the screen, the participants interacted with a computer-generated environment by popping bubbles blown by the Octopus. The bubbles followed a specific trajectory. Interception of the bubbles with the left or right hand avatar allowed flexible use of the postural segments for balance maintenance and arm transport. All participants practiced ten 90-s gaming trials during a single session, followed by a retention test. Arm-postural coordination was analysed using principal component analysis. Results As a result of the short-term practice, the participants improved in game performance, arm movement time, and precision. Improvements were achieved mostly by adapting efficient arm-postural coordination strategies. Of the 13 participants, 10 showed an immediate increase in arm forward reach and single-leg stance time. Conclusion These results support the feasibility of using the custom-made 3D game for retraining of arm-postural coordination disrupted as a result of TBI.</p