729 research outputs found
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Assessing the Impact of “Brain Training” on Changes in Driving Performance, Visual Behavior, and Neuropsychological Measures
No full textAs the population has become both older and more technologically literate, a new class of “brain training” computer programs have gained in popularity. Though these programs have attracted substantial attention from scientists and consumers, the extent of their benefits, if any, remain unclear. Here we employ neuropsychological tests and behavioral metrics collected during periods of real-world driving (with and without manipulations of cognitive load) to evaluate the effects of training with Posit Science’s DriveSharp software. We find that DriveSharp’s training effects appear in in-lab measures of Useful Field of View but did not translate to changes in actual driving performance or changes in visual behavior in consistent or quantifiable ways in the sample assessed. The implications of these results and relevant limitations of the present research are discussed
Can We Predict Steering Control Performance from a 2D Shape Detection Task?
No full textResearch has shown the importance of spatial and temporal integration of visual information in motion perception and steering control under reduced visibility conditions. The current study examined the relationship between a 2D shape detection task and a steering control task under reduced visibility conditions for younger drivers. In the 2D shape detection task, the spatial and temporal characteristics, and the contrast of the stimuli were manipulated by varying the number, the lifetime, and the contrast of the random dots. In the steering task, the visibility of the driving scene was manipulated by varying the quantity and quality of the optical flow information. We found that the correlation between shape detection task and steering control task under low contrast conditions depended on temporal integration. These results suggest that under reduced visibility conditions, temporal integration of visual information may play a larger role than spatial integration
Problems with Sleep Do Not Predict Self-Reported Driving Factors and Perception in Older Drivers: Evidences from the Candrive II Prospective Cohort
No full textGiven that sleep problems and serious motor vehicle collisions are increasingly prevalent in older adults, even minor drowsiness could potentially contribute to driving patterns in older drivers. To date, it is unknown whether less serious problems with sleep influence driving frequency and ability in older adults. We investigated the influence of everyday sleep disturbances on driving practices and driver perceptions in a large cohort of healthy older drivers. Selfreported measures of sleep problems were used to investigate the influence of sleep disturbance on self-reported driving practices and perceived driving abilities. On two measures of self-reported driving outcomes, participants with problems with rated themselves more poorly. However, this relationship disappeared when health and demographic variables were entered prior in hierarchical regression analyses. Our results show that the relationship between sleep problems, driving frequency and perceived abilities is better explained by mediating demographic, health, and cognitive factors
What Makes a Good Passenger? From Teen Drivers’ Perspectives
No full textAn exploratory study was designed to examine male and female teenage drivers’ perceptions and expectations of peer passengers. Qualitative methods were used to interview and survey 16- and 17-year-old licensed drivers. 10 interviewees and 96 survey respondents were included in the analysis. Consistent with previous studies, teenage drivers were concerned about passenger-related distractions. There were noticeable differences between males and females in their perceptions of peer behaviors: females most expected passengers to be non-distracting and polite and males most expected passengers to behave maturely. Future studies should focus on social factors and the psychosocial function of driving for better understanding of the peer passenger interactions, and ultimately the development of passenger-related crash prevention efforts
Two-Minute Peripheral Motion Contrast Threshold Test Predicts Older Drivers’ Collisions and Hazardous Driving in Simulator
No full textOlder drivers’ contrast thresholds for low spatial frequency drifting Gabor stimuli at 15 degrees eccentricity were measured with a previously validated 10-minute forced-choice test and a 2-minute increasing contrast detection test (implemented on an iMac and a PC). Older drivers’ contrast thresholds significantly predict collisions, near collisions, hazardous lane excursions, and speeding, during a simulated drive with surprising hazard encounters and highway merging tasks. The 2-minute tests also correlate with each other and with the 10-minute test. The 2-minute tests are potentially suitable for use in an operational driver assessment setting
Assessment of the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving
No full textWe 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
Driver Simulation-Based Training of Heavy Vehicle Operators: Targeted Task Analysis and Considerations for Training Design
No full textThe use of simulation for training operators of heavy vehicles is gaining momentum. However, there still exists a gap in knowledge about the appropriate skills to target, and in particular, with regards to skill areas of a nontechnical nature. By taking a first-principles approach, we first sought to conduct a targeted analysis of the heavy vehicle operator task and, in turn, to assess which of the goal-based tasks identified through the task analysis would be most appropriate for simulation-based training. In general, simulation provides a safe and efficient option for training critical skills that could otherwise be trained on road (e.g., gear shifting). Simulation also provides the opportunity to train critical skills in a structured and formal way that could otherwise not be achieved in a real heavy vehicle, except on an opportunistic or incidental basis (e.g., hazard perception). Nonetheless, the challenge for training system design still remains: what constitutes the appropriate balance between simulator-based and real truckbased practical training, and for which curriculum components and skill sets
Driving and Speaking: Revelations by the Head-Mounted Detection Response Task
No full textThe cognitive workload of speech-related activity needs to be examined in an economic and simple way. This is especially important as invehicle technology is becoming more cognitive with, for example, the use of speech-interaction and industry will need a way to keep pace with new technologies. One proposed way to measure cognitive workload is the detection response task (DRT) method. In this study, the DRT was used to assess different speech-related cognitive tasks. Three conversation tasks and the n-back task were performed together with a simulated driving task and a head-mounted DRT (HDRT). The aim was to evaluate the conversation and n-back tasks with the HDRT and to quantify the respective cognitive workload. Results show an increase in HDRT reaction times when additional cognitive tasks are performed relative to baseline measurements. In line with other research methods, the HDRT provided a reliable measurement of additional workload
Headway Time and Crashes Among Novice Teens and Experienced Adult Drivers in a Simulated Lead Truck Braking Scenario
No full textDriving simulators can be used to evaluate driving performance under controlled, safe conditions. Teen drivers are at particular risk for motor vehicle crashes and simulated driving can provide important information on performance. We developed a new simulator protocol, the Simulated Driving Assessment (SDA), with the goal of providing a new tool for driver assessment and a common outcome measure for evaluation of training programs. As an initial effort to examine the validity of the SDA to differentiate performance according to experience, this analysis compared driving behaviors and crashes between novice teens (n=20) and experienced adults (n=17) on a high fidelity simulator for one common crash scenario, a rear-end crash. We examined headway time and crashes during a lead truck with sudden braking event in our SDA. We found that 35% of the novice teens crashed and none of the experienced adults crashed in this lead truck braking event; 50% of the teens versus 25% of the adults had a headway time time < 3 seconds at the time of truck braking. Among the 10 teens with < 3 seconds headway time , 70% crashed. Among all participants with a headway time of 2-3 seconds, further investigation revealed descriptive differences in throttle position and brake pedal force when comparing teens who crashed, teens who did not crash and adults (none of whom crashed). Even with a relatively small sample, we found statistically significant differences in headway time for adults and teens, providing preliminary construct validation for our new SDA
Vehicle Detection Using Android Smartphones
No full textRear-end collisions are the most common traffic accidents. Technologies, such as collision warning systems, are developed to reduce the risks of rear-end collisions. This study presents a mobile technology using smartphones to detect the leading vehicle, allowing the possibility to make collision warning systems more affordable and portable. This technology uses the rear camera of an Android smartphone to capture images of driving scenes, and then uses advanced computer vision algorithms to detect and track the leading vehicle. This study may have important applications to improve driving safety