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Did You See That? A Study of Change Blindness
Get PDFOBJECTIVES Increasing concern has been expressed regarding the safety implications of drivers attempting to use in-vehicle devices other than cellular phones while driving. To address this issue, the effect of cognitive load on visual attention merits investigation. Strayer and Johnston (2001) found invehicle system use, specifically cell-phone use, disrupts performance by diverting attention to an engaging cognitive context other than the one immediately associated with driving. Recarte and Nunes (2000) demonstrated that scanning patterns and visual attention can be disrupted by cognitive load. In-vehicle systems can increase the cognitive load of the driver, making it more difficult for the driver to direct attention to the visual scene. The change blindness phenomenon may be useful way to investigate how cognitive load affects attention. Change blindness is the inability to detect scene change when the change occurs in conjunction with an action such as a blink (O’Regan, Deubel, Clark, and Rensink, 2000), eye movement (Wallis and Bulthoff, 2000), or an image that masks the onset of the change (Simons and Levin, 1998). The aim of this experiment was to study the effect of cognitive loading on individuals’ ability to detect change in their visual environment using a speech-based email task. METHODS Twenty participants completed a series of five conditions. In two of these conditions the participants did just one task: either the e-mail task or the visual search task. In the other three conditions participants completed the e-mail and the visual search tasks concurrently. In one email task condition the speech recognition system worked perfectly. In a second condition, speech recognition errors caused the wrong menu item to be selected. In a third condition, speech recognition errors caused the user to be displaced to the wrong menu. In three of the visual search task conditions participants were asked to identify changes that occurred in visual scenes, using the Rensink, et al. (1997) flicker paradigm while navigating a speech-based e-mail system. The visual task presented a series of four displays. These were: an unaltered image (300ms), a gray screen (1150ms), a second image (300ms), and a gray screen (1150ms). The second image was either the unaltered image or an image altered by the addition or removal of an element. The remaining conditions for both the e-mail task and visual search task were labeled as baseline. The baseline measurements were used to evaluate the effects of cognitive load on detection of scene changes. RESULTS Analyses showed that detection of scene changes took significantly longer when participants were cognitively loaded with the e-mail task (mean 5.05) compared to when they were not (mean 4.35), F(3,67)=11.13, p< 0.0001. Analyses also showed participants took significantly longer to determine that the scene had not changed (mean 5.99) than to detect a change had occurred (mean 3.72), F(2,67)=271.95, p <0.0001. Scene detection accuracy was significantly reduced when participants were cognitively loaded with the e-mail task, F(3,67)=5.47, p0.0010. Speech recognition errors introduced by the researcher had little effect on times to determine scene changes as well as time to detect meaningful and non-meaningful scene changes. CONCLUSION The results of this study demonstrate that change detection is sensitive to cognitive load and that endogenous control of visual attention may have been affected by the introduction of the e-mail system. It also shows that the paradigm of scenes with and without changes seems to be a promising and sensitive tool for measuring the effects of cognitive load on an individual’s ability to detect change. REFERENCES O'Regan, J.K., H. Deubel, J.J. Clark, & R.A. Rensink. (2000). Picture changes during blinks: Looking without seeing and seeing without looking. Visual Cognition 7(1-3), 191-211. Recarte, M.A. & Nunes, L.M. (2000). Effects of verbal and spatial imagery tasks to eye fixations while driving. Journal of Experimental Psychology: Applied,6, 31-43 Rensink, R.A., Oregan, J.K., & Clark, J.J. (1997). To see or not see: The need for attention to perceive changes in scenes. Psychological Sciences, 8, 368-373 Simons, D.J. and D.T. Levin. (1998). Failure to detect changes to people during a real-world interaction. Psychonomic Bulletin & Review 5(4), 644-649. Strayer, D.L. & Johnston, W.A. (2001). Driven to distraction: Dual-task studies of simulated driving and conversing on a cellular phone. Psychological Science, 12, 462-466 Wallis, G. and H. Bulthoff. (2000). What’s scene and not seen: Influences of movement and task upon what we see. Visual Cognition 7(1-3), 175-190
Driver Behavior as a Function of Ambient Light and Road Geometry
Get PDFOBJECTIVES To determine how ambient light (day versus night) and road geometry affect driving behavior,especially the speeds that drivers choose when not constrained by lead vehicles.METHODSRecently, it has become technically easier to observe how people drive b offering them longtermuse of highly instrumented vehicles. Much of this type of work has been done in connectionwith large-scale field operational tests (FOTs) of various innovative vehicle systems. Theinformation obtained is in many ways complementary to information from observation of traffic.Traffic observation often provides information about a large number of drivers, but at a relativelycoarse level and in a spatially and temporally limited context (i.e., observing how a large numberof drivers negotiate a particular intersection). In contrast, long-term use of highly instrumentedvehicles is more restricted in terms of how many drivers can be observed, although the feasiblenumbers are now reasonably high. On the positive side, data from instrumented vehicles canoffer very detailed information about driving behavior over many miles and many days.In this paper, we present results from a database of driving behavior that was derived from arecent FOT for an adaptive cruise control (ACC) system (although the data used here are all fromphases of the study that involved only normal vehicle equipment). The FOT involved tenidentical cars that were instrumented for a variety of types of data. The most important data forpresent purposes were: speed, yaw rate, location from the Global Positioning System (GPS), andpresence or absence of a lead vehicle within about 100 m based on the forward-looking sensorsof the ACC system. The instrumented cars were driven by a total of 108 participants, each ofwhom was given a car to use as his or her own vehicle in normal driving for either two or fiveweeks. The participants were sampled from licensed drivers in southeastern Michigan, andrepresented a wide range of age and driving experience.RESULTSResults will be reported in terms of speed as a function of horizontal road curvature in light anddark conditions, and as a function of driver age and gender, all for situations in which there is nolead vehicle within about 100 m. CONCLUSIONSCurrent evidence about headlighting suggests that drivers’ ability to see and negotiate theroadway is virtually unaffected by differences in ambient light, although their ability to perceiveand avoid objects on the road, such as pedestrians, is greatly reduced when headlamps are themain source of light. There is also evidence that drivers do not markedly reduce their speed inconditions of low ambient light. The current analysis allows us to determine how drivers react tospecific road geometries in light and dark conditions. This has implications for how well drivers’perceptual abilities match their driving behavior, and also for assessing the potential benefit of avariety of innovative headlighting systems that are currently being designed to adapt in variousways to vehicle speed and road geometry
Inattentional Blindness While Driving
Get PDFMack and Rock’s research (1999), suggests that we perceive only those objects and events to which we directly attend. This means that the majority of unexpected visual information goes unnoticed, no matter how dramatic or important it may be. Not noticing unexpected objects in direct view because your attention is on other driving events is a potentially deadly but common phenomenon. Most drivers have experienced these brief moments of “functional blindness” and not perceived events or objects directly and obviously centered in their field of vision. This usually produces astonishment, alarm, and possible over-reaction when awareness returns. The driver may be telling the truth to the officer after the crash, “I did not see that stop sign!” Other experiments (Simons and Chabris, 1999) have shown that “InAttentional Blindness” is exhibited by a majority of viewers when an unexpected object and action take place clearly, slowly, and within inches of objects being attended to. Wickens, et al. (1998) examined how pilots in flight simulators perform using head-up displays. The research showed that when experimenters put something unexpected, but important, in pilots’ field of vision, such as an airplane on the runway, pilots often land right on top of them. This paper will describe what we believe to be the first experiment explicitly designed to test this phenomenon while driving in a simulator. Our study is unlike the normal driver distraction study in that we did not ask the driver to divert attention to an off-the-road secondary task, but to keep attention focused solely on the road and the objects on it. Subjects drove on a two-lane road in a city environment generated by a 4-channel Class II GlobalSim driving simulator. We manipulated drivers’ attention by asking them to count the number of a specific type of pedestrian randomly interspersed with other pedestrians strolling along the right side of the road. Meanwhile various expected and unexpected critical driving events—stop signs at intersections (in the line of pedestrians), random lead car braking, and barriers in the roadway—were presented. The reaction times, as sensed by brake pedal pressure, to the critical driving events were recorded and compared to a baseline condition where the drivers were asked to merely follow normal driving procedures. Compared to the baseline, the pedestrian-monitoring task increased reaction times to all types of critical driving events. Subjective observations of drivers and implications for road safety will be discussed
Can High-risk Older Drivers be Identified in a DMV Setting with a Brief Battery of Functional Tests?
Get PDFRecent research has indicated that some laboratory measures of functional ability may discriminate between crash-involved and crash-free older adults. However, the ability of these tests to provide the same level of discriminability in a real-world setting such as a Department of Motor Vehicles remains to be established. Therefore, in conjunction with the Maryland Department of Motor Vehicles and the National Highway Traffic Safety Administration, a brief battery of tests was developed and evaluated. The battery contained a number of cognitive tests (e.g., UFOV® subtest 2, the closure subtest of the Motor Free Visual Perception Test [MVPT], Trails A and B, etc.) and physical measures (e.g., Rapid Pace Walk, Head and Neck Rotation, etc.) that prior literature had indicated might be related to crash risk in older adults. Motor Vehicle Administration staff were trained to administer the test battery. Older adults (N=4,173, mean age =69 years) were approached by the staff after license renewal and asked to help evaluate the brief battery. Of the 4,173 older adults approached at the field sites, 2,112 individuals aged 55-96 years of age participated. The primary outcome of interest for this study was the occurrence of an at-fault Motor Vehicle Collision (MVC) following assessment. For members of this sample, the outcome period ranged from 2-3 years. Rate Ratios were determined for each functional variable based upon at-fault crashes adjusted for driving exposure over this period. Univariate analyses revealed that five variables (Age, Walk Time, MVPT, Trails A and UFOV®) were significantly related to crash frequency. These significant variables overlapped with one another to a certain degree, indicating that impaired older drivers score poorly on multiple cognitive assessments. The UFOV® subtest 2 appears to be the most strongly associated within this analysis (RR=3.78, p< .05) and Rapid Pace Walk (RR=1.96, p < .05) remained uniquely related to the frequency of state-reported, at-fault crashes. The role of such a screening battery in field settings such as a DMV will be discussed
Evaluating Workload Associated with Telematic Devices via a Secondary Task Protocol
Get PDFThere are a variety of driver distractions that negatively affect driver workload and performance. These distractions range attempting to light a cigarette, and putting on make-up, to eating or drinking, tuning the radio, using a cellular phone, or using an in-vehicle navigation system. Of particular present interest are the distracting effects of telematic devices, which include traffic information systems, telecommunication, intelligent aid and control, and navigational systems. These devices can now be found on-board various types of U.S. and foreign automobiles. Despite having many potential benefits, there are also several behavioral problems resulting from poor use of these devices. The present research was designed to investigate the deleterious effects of telematics on driver performance. It was hypothesized that all the telematic systems used in this study would degrade driver performance and increase workload. A mixed-model factorial design (2x3) was used, with telematics being a between-subject factor and allocation phase a within-subject factor (repeated measures). All participants were required to drive three, four-minute simulated (pre, during, and post) allocation phases. In the preallocation phase, participants were required to drive while performing a secondary counting task, (counting and responding to a series of randomly presented visual signals). During the allocation phase, participants were required to drive and perform the secondary counting task while either talking on the phone or tuning a radio (distractibility task). In the post-allocation phase, participants were required to drive while performing the secondary counting task. Data from the counting task (number of correct, wrong, and misses) and driving errors (collisions, crossing the median, leaving the road, maintaining the speed limit, and lane deviations) were recorded and statistically analyzed. Thirty-four participants (nine males and 25 females) from the University of Central Florida participated in this study. A series of analyses of variance (ANOVA) were conducted to test for the effects of telematics and workload on each of the dependent measures. A significant main effect of phase on lane deviations was observed, F(2, 64) = 10.58, p < .001, indicating that more lane deviations were made during the cell phone and radio tuning use (M = 9.14) than during both of the pre-allocation (M = 4.14) and post-allocation (M = 5.88) phases. ANOVA also yielded a significant main effect of phase on crossing the median, F(3, 68) = 4.63, p < .05, indicating that more crossings were made during the allocation phase (M = 5.05) than during the pre-allocation (M = 3.05) and post-allocation (M = 4.47) phases. Similarly, the results also showed a significant effect of phase on the distraction task performance, F(2, 64) = 5.70, p < .01, indicating that more errors were made during the allocation phase (M = 6.50) than during the pre-allocation (M = 4.50) and the post-allocation (M = 3.38) phases. The present findings indicate that both cellular phone and radio systems are capacity demanding. The counting task results demonstrate the increased level of workload associated with these telematic devices. In addition, driving performance errors were also higher for both the cellular phone and the radio systems. Our findings suggest the need to regulate the use of such devices in order to avoid overloading the driver’s attentional spare capacity
In-Vehicle Navigation Systems: Interface Characteristics and Industry Trends
Get PDFA review and inventory of in-vehicle navigation systems was conducted in order to better understand the current state of practice and trends relating to their design and implementation. The review focused on human factors characteristics and interface features using accepted human factors practices, principles, and guidelines as a basis for assessing likely impacts on driver distraction. The inventory examined market-ready in-vehicle products, and identified a range of interface design features, noting aspects and dimensions that have implications for potential driver distraction. Results indicated that devices tend to incorporate a large number of features and options, making it a potential challenge for drivers to learn all of the capabilities of a system and resulting in lengthy manuals. Although devices also tended to provide large amounts of information, some designs may allow for increased information presentation without necessarily sacrificing performance. Warnings or cautions against interacting with systems while driving were common; however, relatively few systems disable equipment when vehicles are in operation
Steering a Driving Simulator Using the Queueing Network-Model Human Processor (QN-MHP)
Get PDFThe Queueing Network-Model Human Processor (QN-MHP) is a computational architecture that combines the mathematical theories and simulation methods of queueing networks (QN) with the symbolic and procedure methods of GOMS analysis and the Model Human Processor (MHP). QN-MHP has been successfully used to model reaction time tasks and visual search tasks (Feyen and Liu, 2001a,b). This paper describes our work of using QN-MHP to model vehicle steering and to steer a driving simulator as a step toward modeling more complex driving scenarios. The steering model was implemented in Promodel, a commercially available simulation program. A network of 20 servers represents different functional modules of the human perceptual, cognitive, and motor information processing system. Entities carrying information on vehicle location and orientation arrive at and flow through the visual, cognitive and motor sub-networks of the system and are processed independently and concurrently by the servers. The QN-MHP steering model was interfaced with a driving simulator (DriveSafety) using an Ethernet protocol and several custom-built software modules. Heading and location information were received in real-time from the simulator and processed through the servers. Whenever the model made a hand movement, the corresponding position of the steering wheel was transferred to the simulator, thus steering the simulated vehicle. The model demonstrated realistic steering behavior. It steered the driving simulator within the lane boundaries of straight sections and curves of varying curvature. This work showed the potential strength of QN-MHP as a model of driving behavior. Ongoing work will further develop the model by expanding the scope of the driving task and by adding secondary in-vehicle tasks
The Effects of Lead-Vehicle Size on Driver Following Behavior: Is Ignorance Truly Bliss?
Get PDFThe objective of this study was to examine whether size of a lead vehicle (passenger car or light truck) affects the distance at which following vehicles travel. Naturalistic following data were collected from drivers using instrumented passenger cars in place of their own vehicles. The results show that these drivers followed light trucks at shorter distances than they followed other passenger cars by an average of 5.6 m, or .19 s in headway time margin, but at the same velocities and range-rates. This result is discussed in the context of a passenger car driver’s ability to see beyond a lead vehicle to assess, and respond to, the status of traffic downstream. The results of this study suggest that knowing the state of traffic beyond the lead vehicle, even by only one additional vehicle, affects gap length. Specifically, it appears that when dimensions of lead vehicles permit other drivers to see through, over, or around them, drivers maintain significantly longer (i.e., safer) distances
Demographic and Driving Performance Factors in Simulator Adaptation Syndrome
Get PDFSimulation is an important option for testing at-risk drivers with medical impairments. Simulator Adaptation Syndrome (SAS), characterized by autonomic symptoms, presents a drawback to testing. This study investigated new issues regarding susceptibility of neurologically impaired drivers to SAS, scenario situations most likely to cause SAS, and effects of SAS on driver performance. Subjects were 164 drivers enrolled in larger ongoing studies of at-risk older drivers. Eighteen had Alzheimer’s disease (AD), 44 stroke, and 102 were neurologically normal controls. Experimental drives were conducted using a fixed-base high-fidelity simulator with a 150º forward field of view. Each driver completed a questionnaire immediately after driving in the simulator, rating any feelings of discomfort along nine dimensions; an overall discomfort score was calculated. Of the 164 drivers, 130 completed the full drive and 34 ended the drive early. Drivers with higher overall discomfort scores were more likely to drop out before completing a drive. Specific symptoms strongly predicted dropping out, namely dizziness, nervousness, light-headedness, body temperature increase, and nausea. Simulator dropout rates and reported discomfort scores were significantly greater in women than men, but did not differ between drivers with AD or stroke and neurologically normal drivers. Comparisons between 32 Dropouts and 32 Non-Dropouts (matched by age, gender, neurological impairment, and scenario driven) showed no evidence that higher levels of discomfort cause a driver to perform atypically before the point of dropout. We could relate dropout to specific segments and events in the drive that required abrupt braking
