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Sensitivity of the lane change test as a measure of in-vehicle system demandKristie L. Young*, Michael G. Lenn, Amy R. WilliamsonMonash University Accident Research Centre, Building 70, Monash University, Clayton, Victoria 3800, Australiaa r t i c l e i n f oArticle history:Received 29 October 2009Accepted 18 June 2010Keywords:Driver distractionLane change testIn-vehicle information systemsEvaluation methodsa b s t r a c tThe Lane Change Test (LCT) is one of the growing number of methods developed to quantify drivingperformance degradation brought about by the use of in-vehicle devices. Beyond its validity andreliability, for such a test to be of practical use, it must also be sensitive to the varied demands ofindividual tasks. The current study evaluated the ability of several recent LCT lateral control and eventdetection parameters to discriminate between visual-manual and cognitive surrogate In-VehicleInformation System tasks with different levels of demand. Twenty-seven participants (mean age 24.4years) completed a PC version of the LCT while performing visual search and math problem solvingtasks. A number of the lateral control metrics were found to be sensitive to task differences, but theevent detection metrics were less able to discriminate between tasks. The mean deviation and laneexcursion measures were able to distinguish between the visual and cognitive tasks, but were lesssensitive to the different levels of task demand. The other LCT metrics examined were less sensitive totask differences. A major factor influencing the sensitivity of at least some of the LCT metrics could bethe type of lane change instructions given to participants. The provision of clear and explicit lanechange instructions and further refinement of its metrics will be essential for increasing the utility ofthe LCT as an evaluation tool.? 2010 Elsevier Ltd and The Ergonomics Society. All rights reserved.1. IntroductionDriverdistractionisacknowledgedinternationallyasanimportant road safety issue (Regan et al., 2008). In particular, thepotential for in-vehicle and portable technologies, including,information, communication, entertainment and advanced driverassistance systems, to distract drivers from the driving task anddegrade performance has been the subject of intense research andpolicy initiatives worldwide (Collet et al., 2009; Wittmann et al.,2006). An important goal in the design of these systems is toensure that their use while driving does not interfere with thedriving task and undulycompromise safety. This is a challenge in anarea where the introduction of technology is largely commercial,rather than safety driven. The realisation of this goal is dependantupon the provision of widely accepted and scientifically robustmethods for informing the design and assessing the safety impli-cations of in-vehicle systems. Several such methods have beendeveloped, including the visual occlusion technique (Chiang et al.,2004; Gelau et al., 2009; Noy et al., 2004; Senders et al., 1967)and the peripheral detection task (Harms and Patten, 2003; Olssonand Burns, 2000,p. 8; van Winsum et al., 1999).As evidenced by its current development into an ISO standard(ISO, 2009), another candidate methodology that shows promise inthis area, is the Lane Change Test (LCT; Mattes, 2003; Mattes andHalln, 2008). To be useful as an evaluation tool, however, theLCT must be valid (i.e., it measures what it claims to measure) andreliable (i.e., the results obtained are consistent across adminis-trations), as well as having high sensitivity. The focus of this paperis on the LCT methods sensitivity. That is, its ability to distinguishthe differential effects of various types of distraction on drivingbehaviour.1.1. Driver distractionDriver distraction is commonly described as comprising a rangeof different, but not mutually exclusive, elements; for example,visual, cognitive, auditory and biomechanical (physical) (Ranneyet al., 2000). These distraction types, particularly visual andcognitive distraction, have been shown to impair different aspectsof driving performance, with lateral control and event detectionmetrics being particularly sensitive to different forms of distraction.For instance, visual load has been shown to increase lane keepingvariation (e.g., Greenberg et al., 2003; Zwahlen et al., 1988).Moderate levels of cognitive load, in contrast, have been shown tohave little effect on lane keeping performance and can even lead tomore precise lateral control (Brookhuis et al.,1991; Engstrom et al.,* Corresponding author. Tel.: 613 9905 1258; fax: 613 9905 4363.E-mail address: kristie.young.au (K.L. Young).Contents lists available at ScienceDirectApplied Ergonomicsjournal homepage: /locate/apergo0003-6870/$ e see front matter ? 2010 Elsevier Ltd and The Ergonomics Society. All rights reserved.doi:10.1016/j.apergo.2010.06.020Applied Ergonomics 42 (2011) 611e6182005; Jamson and Merat, 2005). Further, both cognitive and visualtasks can impair event detection (Klauer et al., 2006), but cognitivedistraction can also impair drivers ability to respond to eventsquickly and adequately (Consiglio et al., 2003; Recarte and Nunes,2003; Strayer et al., 2003).Given its current development into an ISO standard and itsincreasing use in distraction research, it is important that the LCT iscapable of measuring and distinguishing these diverse distractioneffects. This study therefore aimed to evaluate the sensitivity ofa range of LCT metrics in being able to distinguish between visual-manual and cognitive tasks with different levels of demand.1.2. The lane change testThe LCT is a PC-based driving simulation that is designed toquantitatively measure the level of degradation in driving perfor-mance induced by the simultaneous performance of a secondarytask. It has been widely used to assess driving performance withconcurrent use of a range of in-vehicle information systems (IVIS)which provide information that supports primary driving tasks(e.g., navigation), as well as Advanced Driver Assistance Systems(ADAS) that directly support the primary driving task (Burns et al.,2005, pp. 1980e1983; Mntyl et al., 2009).A number of studies have focused on validating the LCT. Many ofthese early validation tests were carried out as part of the AdvancedDriver Attention Metrics (ADAM) project, in which the LCT wasdeveloped, and suggest that the LCT is a valid, reliable and sensitivemeasure (see Mattes and Halln, 2008). Subsequent researchdemonstrated that the LCT could discriminate between secondarytasks with different workload levels (Burns et al., 2005, pp.1980e1983), with drivers demonstrating a greaterdeviation in lanechange path when performing a complex versus simple navigationtask while driving.More recently, work has continued on the LCT to expand itsdiagnostic power by proposing new performance metrics (Mattesand Halln, 2008). Given the complexity and multifaceted natureof distraction, it is important for any evaluation method to measuremultiple aspects of the driving task in order to draw conclusionsabout the safety effects of in-vehicle devices.A number of studies have examined the sensitivity of several ofthe LCT metrics in being able to distinguish between different typesof distraction (Bruyas et al., 2008; Engstrm and Markkula, 2007;Harbluk et al., 2009, 24e30 p.). Engstrm and Markkula (2007)have examined the sensitivity of two new LCT metrics e pathcontrol (high-pass filtered SDLP) and sign detection/recognition(Percent correct lane; PCL) e to distinguish visual and cognitivetasks. Results revealed that the two types of distraction eachimpaired LCT performance differently. The visual, but not cognitive,tasks led to reduced path control, while the cognitive, but notvisual, tasks affected detection and sign recognition and responses.Bruyas et al. (2008) found that the adapted mean deviation score,ratio of correct lane changes and Lane Change Initiation (LCI)metrics were capable of differentiating some visual-manual andauditory tasks, but not others. Finally, in order to take into accounttask duration, Harbluk et al. (2009, 24e30 p.) examined the LCTmean deviation per average task by dividing the mean deviationscore by the number of task completed per run. They found thatthis adapted measure was better able than the original meandeviation score to discriminate between navigation tasks withdifferent levels of complexity.These studies demonstrate that at least some of the proposedLCT performance metrics are sensitive to the disparate effects ofdifferent forms of distraction. However, there is still a need todetermine if other LCT metrics, that are increasingly being used byresearchers and policy and system developers to draw conclusionsabout the safetyand design aspects of IVIS systems (e.g., Maciej andVollrath, 2009), are also sensitive to task differences. This studyextends the findings of the previous research discussed above byexamining the sensitivity of a range of new and recently proposedLCT lateral control and event detection parameters (lane keepingvariation between signs, percent correct lane changes, number oflane excursions, LCI, and mean steering angle) in being able todistinguish between visual-manual and cognitive tasks withdifferent levels of demand. The findings of this study can be used toinform decisions regarding which LCT metrics are suitable for useand which ones may need further refinement. It will also add to thegrowing number of studies aimed at establishing the psychometricproperties of the LCT as part of its development into an ISOstandard.2. Method2.1. DesignThis study used a repeated-measures design, with one inde-pendent variable, task condition, which had five levels: a baseline(no secondary task) condition and four secondary task conditions:visual easy, visual hard, cognitive easy and cognitive hard. Partici-pants completed the four secondary task conditions while drivinga PC version of the LCT. Further details of the secondary taskconditions are contained in Section 2.3.2. This combination ofsecondary task conditions ensured that it was possible to examinethe ability of the LCT to distinguish between different levels ofdemand as well as different types of distraction.2.2. ParticipantsTwenty-seven drivers who held a valid drivers license partici-pated the study. Sixteen of the participants were male and 11 werefemale and the mean age for the group was 24.4 years (SD 3.0;range 21e31 years). All participants held a valid full driverslicense, apart from one who held a probationary license, which isissued for the first four years of solo driving and contains certainpassenger, mobile phone and vehicle power restrictions. The meanage at which their solo (probationary) license was obtained was19.3 years (SD 2.6), and the average time spent driving each weekwas 7.3 h (SD 6.6).Participants were recruited through campus notice boards andnewsletters, the Monash Careers Website and the local newspaper(Waverley Leader). Ethics approval for the study was granted by theMonash University Standing Committee on Ethics in ResearchInvolving Humans (SCERH). Participants were reimbursed for theirtime and travel expenses.2.3. Materials2.3.1. Driving taskDriving performance was measured using the LCT (version 1.2;Mattes, 2003). The LCT is a simple driving simulation consisting ofa 3000 m straight, three-lane road. Speed is limited to 60 km/h bythe system, which the test participants were asked to maintainthroughout the drive. No other traffic is present on the road. Thedrivers are instructed to change lanes via 18 signs that appear oneach side of the road every 150 m, on average. The signs are blankuntil 40 m before the sign, at which point the lane change infor-mation is given (Fig. 1a). Participants were instructed to changeslanes as soon and as quickly as possible after they see the infor-mation appear on the sign. Participants were not required to havecompleted their lane change before they reached the sign.K.L. Young et al. / Applied Ergonomics 42 (2011) 611e618612The LCT was run on a desktop PC. The set-up of the test followedthat set out in the Draft ISO standard (ISO, 2009). The visual scenewas presented on a single 1900LCD monitor. Control of the simu-lation was achieved through a Logitech MOMO force-feedbackgaming steering wheel with accelerator and brake foot pedals.Participants sat in a height adjustable chair, with the chair andpedals positioned to approximate that of a real vehicle.2.3.2. Secondary taskTwo surrogates visual-manual and cognitive IVIS tasks wereused as the secondary tasks, each with two levels of difficulty.Artificial or surrogate in-vehicle information system (IVIS) taskswere used so that the level of task demand could be systematicallymanipulated.2.3.3. Visual-manual taskThe Surrogate Reference Task (SuRT v.2.1) was employed as thevisual-manual task. The task requires participants to search fora larger target circle among visually similar, smaller distractercircles (visual demand), and to select, using the keyboard arrowkeys, the portion of screen containing the target (manual demand).Task difficulty (easy, hard) was manipulated by varying the size ofthe distracter circles compared to the target circle, and byincreasing thenumberof screensegments inwhich the target couldappear (easy 2 regions, hard 6 regions). For the easy condition,the target circle was twice the size of the distracter circles, while forthe hard condition the target circle was 15% larger than the dis-tracters. An example of the SuRT display is contained in Fig.1b. Thevisual task was semi self-paced, whereby the participants couldtake as much time as needed to make their selection, but thesoftware controlled when the next stimuli was presented. The SuRThas been used widely in IVIS research (e.g., Bruyas et al., 2008;Rognin et al., 2007) and has been found to be a valid measure ofIVIS task demand (Wynn and Richardson, 2008).The visual secondary task was presented on a laptop with 1400screen which was located on a table to the left of the driver, within30?(horizontal and vertical) of their normal field of view andwithin easy reach.2.3.4. Cognitive taskThe cognitive task comprised a math problem solving task,involving basic addition, again with two levels of difficulty e easyand hard. Random numbers were read aloud to the participant(through a headset) using DirectRT software. For the easy level, theparticipant was asked to add 5 to the number and say theirresponse out loud. For the difficult level, participants were requiredto add 7. The task was semi self-paced, whereby participants weregiven as much time as needed to respond to each problem (self-paced), but the system presented the next problem immediatelyafter the previous response was given (system-paced). This taskwas piloted extensively prior the experiment to ensure that therewas an adequate degree of difficultly and differentiation amongstthe easy and hard levels.2.4. ProcedureOn arrival at the session, participants completed a demographicquestionnaire. They were then given averbal explanation of the LCTand secondary tasks, followed by static (no LCT) practice andbaseline trials of the visual and cognitive secondary tasks. Partici-pants completed 1e2 practice drives on the LCT and then the sixtrial runs: baseline, visual easy, visual hard, cognitive easy, cogni-tive hard and a final baseline trial. Before each dual-task drive,participants were instructed to “concentrate your attention ondriving safely, but do not ignore the secondary tasks”.The order in which the visual and cognitive secondary taskswere presented was counter-balanced across participants toaccommodate any practice effects.2.5. Data analysisThe LCT driving performance examined can be broadly cat-egorised into lateral vehicle control measures and event detectionmeasures:2.5.1. Lateral control measures. Mean deviation. Drivers mean lateral deviation score wascompared to the LCT normative model that is automaticallycalculated by the LCT analysis software. The normative modelrepresents an ideal lane change path. Deviation scores werecalculated for each run over the entire length of the drive (standardmean deviation score), as well as for the straight sections betweenlane changes (10 m afterchange to 10 m before next change), whichprovides an additional measure of lateral control with the effect ofthe lane changes removed. Instances where the participants hadmade an incorrect lane change were excluded from the straightsection analysis as they over-inflated the deviation score. Mean steering angle. The mean steering angle in grads(equivalent to1/400of a full circle) was calculated for each entireLCT run. This measure was calculated automatically using thestandard LCT analysis software.Fig. 1. Primary and secondary tasks used. Panel A (left) shows the LCT driving scene with lane change sign (arrow). Panel B (right) shows the SuRT task visual display (easycondition).K.L. Young et al. / Applied Ergonomics 42 (2011) 611e618613. Lane excursions. Thenumberoflaneexcursionsmadeduringeach run was calculated by examining the lateral position traceschematics produced by the LCT. A lane excursionwas defined as anyinstance where the LCT actual deviation trace moved outside of thecorrect lane of travel. Lane excursions were examined for the entiredrive,includingduringlanechangemanoeuvresandstraightsections.2.5.2. Event detection measures. Lane change initiation (LCI). LCI represents the difference(in metres) between the distance at which the lane change infor-mation appears on a sign and the distance at which the driveractually initiated the lane change. Thus, LCI represents an eventdetection measure. Percentcorrectlanechanges. The percentage of lanechanges that were correctly executed in each run was calculated asa measure of the participants ability to correctly respond to the lanechangecommands.Inaddition,thetypesoflanechangeerrorsmadeby drivers were qualitatively examined. Two types of errors wereexamined:missedlanechanges(wherethedrivercontinuedstraightahead without changing lanes) and erroneous lane changes (wherethe driver executed the lane change, but into the wrong lane).2.5.3. Secondary task performanceMeantaskcompletiontime(inmilliseconds)wascalculatedforthevisualandcognitivesecondarytasksinordertoexamineanypotentialtrade-offs in performance across the secondary and LCT tasks.Analyses were carried out using SPSS version 15.0. One-wayrepeated measures analyses of variance (ANOVAs) were used tocompare mean scores for all LCT performance measures (exceptpercent correct lane changes). The results were corrected for sphe-ricity violations where necessary using the Greenhouse-Geissermodification. Greenhouse-modifications were made for the straightsection mean deviation and LCI analyses. Pairwise comparisons werecalculatedusingleastsignificantdifference(LSD).Thepercentcorrectlane change data were not normally distributed and, hence, wereanalysed using the non-parametric Friedman test. Descriptive anal-ysis of lane change errors (missed versus incorrect LC) was alsoundertaken. No significant differences were found between the twobaseline runs for any measure, thus these data were combined intoasinglebaselinecondition(p.05).Duetoacorrupteddatafile,meandeviation and LCI data were not able to be calculated for one driver.3. Results3.1. Lateral control measures3.1.1. Mean deviationThe mean deviation scores for the entire drive and the straightline segments are presented in Fig. 2. The mean deviation across theentire drive differed significantly across the task conditions (F(4,100) 6.53, p .001). Mean deviation was lower in the baselinecondition than in all dual-task conditions (p .05). Across thesecondary tasks, mean deviation scores were significantly higher inthe visual easy and hard conditions compared to the cognitive easycondition (p .05).Significant differences in straight line (between signs) meandeviation were found across tasks (F (3,73) 3.07, p .05).Although there was a trend for mean deviation scores to be higherwhen performing visual tasks compared to the cognitive tasks orbaseline condition, the only significant difference observed wasthat mean deviation scores were higher in the visual easycompared to the cognitive easy condition (p .05).3.1.3. Lane excursionsThe mean number of lane excursions for each driving conditionis displayed in Fig. 3. Significant differences in the number of laneexcursions made was found across conditions (F (4,104) 8.86,p .001), whereby all dual-task conditions contained a greaternumber of lane excursions than the baseline condition (p .01).The visual hard condition also contained a significantly highernumber of lane excursions than any of the other conditions(p .01). The number of lane excursions did not differ significantlyacross the other three dual-task conditions.3.2. Event detection and response measures3.2.1. Lane change initiationThe distance at which participants initiated a lane change fromthe appearance of the information on each sign was recorded andthe means displayed in Fig. 4. A significant effect of drivingcondition was found (F (3,77) 4.04, p .01), whereby the meandistance from the lane change sign presentation to the initiation ofthe lane change was shorter in the Cognitive Hard condition thanthe Visual Easy and Visual Hard conditions (p .05).1.81.92BaselineCog EasyCog HardVis EasyVis HardaveDnaeM0.9BaselineCog EasyCog HardVis EasyVis Hardb)sngisneewteb(veDnaeMFig. 2. Mean deviation in lane change path for each experimental condition. Panel A (left) shows the overall mean deviation. Panel B (right) shows the straight line mean deviation(between signs).K.L. Young et al. / Applied Ergonomics 42 (2011) 611e618614Descriptive analysis was performed to examine the type of lanechange errors made (missed or incorrect changes). The percentageof incorrect lane changes made in each condition that are attrib-utable to each error type is contained in Table 1. This analysisshowed that of the small percentage of lane change errors made,most consisted of missed lane changes, suggesting a failure todetect the sign rather than misread it. This error type was mostcommon in the hard task conditions and was slightly morecommon when performing cognitive tasks.3.3. Secondary task performanceTwo separate analyses were conducted to investigate taskcompletion times on the two secondary tasks statically (no driving)and when completing the LCT. Two, 2 (baseline, LCT) ? 2 (easy,hard) repeated-measures analysis of variance (ANOVA) were con-ducted for the visual task and the cognitive task separately. Due totechnical problems, visual task data was not recorded for oneparticipant.3.3.1. Visual taskMean completion times for each visual display for the baselineand LCT conditions are displayed in Fig. 5a. No significant inter-actionbetweentaskdifficultyandconditionwasfound(F (1,25) 2.19, p .05); however, significant main effects werefound for condition (F (1,25) 15.12, p .01) and difficulty level(F (1,25) 79.37, p .001). Drivers took longer to complete thecognitive stimuli when completing the LCT compared to whenperforming the cognitive task alone (baseline). Completion timeswere also significantly longer for the hard visual stimuli than theeasy stimuli.3.3.2. Cognitive taskMean completion times for the cognitive math task stimuli foreach task condition was calculated and compared, as displayed inFig. 5b. A significant main effect was found for difficulty level(F (1,26) 36.17, p .05), nor was there a significant main effect ofcondition (F (1,26) 0.09, p .05). This latter result is interesting,as it suggests that drivers did not trade-off performance on thecognitive task in favour of the driving task.4. DiscussionThe current study evaluated the ability of several recentlyproposed LCT lateral control and event detection parameters todiscriminate between visual-manual and cognitive surrogate IVIStasks with different levels of demand. A number of the lateralcontrol metrics were found to be sensitive to task differences, butthe event detection metrics were less able to discriminate betweentasks. These results have important implications for the battery ofmetrics that might be included in the ISO standard.Lateral control is a commonly used driving performancemeasure that has been shown to be sensitive to several forms ofdriver impairment (e.g., drug driving and distraction; Lenn et al.,2010; Young et al., 2008). In terms of the LCT lateral controlmetrics, the results for the overall mean lane deviation measureconfirm that this measure is sensitive to the differential effects ofvisual and cognitive distraction. As expected, overall mean devia-tion scores were highest when drivers were performing the visualsecondary tasks, and were significantly higher than the cognitiveeasy and baseline conditions. Mean deviation scores for the twocognitive tasks were significantly higher than the baseline condi-tion, although the impact of these tasks on lateral control was lessthan that of the visual tasks.In terms of task difficulty, there was a trend for mean deviationscores tobe higher in the hardtask conditions comparedtothe easyconditions; however, this difference was not significant. It is notcompletely clear why the mean deviation measure was not sensi-tive to differences in task demand. It is unlikely to be due to thelevels of task difficulty used, which were shown in extensivepiloting prior to conducting the experiment to have sufficientlydifferent perceived levels of demand. For the visual task at least, theinsensitivity may stem from the nature of the SuRT task used. Thedifferent levels of complexity for this task largely derive fromdifferences in task duration, rather than greater or lesser amountsof cognitive resources. As the participants were continuallyengaged in the SuRT task (i.e., the next stimulus was presentedimmediately after completion of another), it is not unexpected thatthe differences in individual task duration across the easy and hardconditions are not reflected in the driving scores.The mean deviation results are generally consistent with pastresearch in this area, which have shown that increased visual-manual load increases lane keeping variation, while cognitivesecondary tasks have less of an effect, or no effect, on lane keepingperformance (Engstrom et al., 2005; Greenberg et al., 2003;Wilschut et al., 2008). Reduced lane keeping performance duringperiods of visual-manual demand is believed to result from the15.017.019.021.023.025.027.029.0BaselineCog EasyCog HardVis EasyVis Hard)ser tem( ICLFig. 4. Mean Lane Change Initiation for each experimental condition.Table 1Percentage of incorrect lane changes (LC) attributable to each error type for taskcondition.Condition% Correct LC% Missed LC% Incorrect LCBaseline97.940.821.24Cognitive Easy97.722.060.22Cognitive Hard95.463.501.04Visual Easy97.522.060.42Visual Hard95.872.471.6600.511.522.533.544.5BaselineCog EasyCog HardVis EasyVis HardsnoisrucxEenaL.oNnaeMFig. 3. Number of lane excursions made in each experimental condition.K.L. Young et al. / Applied Ergonomics 42 (2011) 611e618615build up of steering errors. When visual attention is diverted fromthe road, drivers tend to maintain a fixed steering angle and makeless micro steering corrections, which can in turn lead to laneweaving and excursions. The manual demand associated with theSuRT task may have also contributed tothe increase in lane keepingvariation. Cognitive demand, however, is believed to have less of animpact on lane keeping due to the effects of visual tunnelling.Engstrom et al. (2005) found that whenperforming a cognitive taskdrivers visual scanning patterns changed such that they increasedtheir gaze concentration towards the centre of the road. This led toan improved tracking response and a decrease in lateral deviation.A similar pattern of results were found for the mean number oflane excursions. The highest number of lane excursions (where anypart of the vehicle was outside the correct lane) was found in thevisual hard condition, and this was significantly higher than for allother dual-task conditions and the baseline condition. The numberof lane excursions was also higher in the cognitive and visual easytasks compared to baseline. These results suggest that the laneexcursion measure is at least somewhat sensitive to differences intask type and level of demand.The other two lateral control metrics appeared to have limitedsensitivity to distinguish visual-manual and cognitive tasks anddifferent levels of demand. No significant differences were foundacross tasks for mean steering angle. Few differences were alsofound across tasks for the straight line mean deviation measure,with only the visual and cognitive easy tasks differing from eachother (although the pattern of trends was in the expecteddirection).Both event detection metrics examined also appeared to havelimited sensitivity to the different IVIS tasks. No significant differ-ences were found across tasks for percentage of correct lane changemeasures. The results for the lane change initiation measure werehighly unexpected. Not only were few differences found acrosstasks, LCI scores were longer in the baseline condition than in thedual-task conditions. This indicates that participants were takingless time to initiate their lane change when performing anothertask than when not distracted, a finding that contradicts other LCTstudies (Bruyas et al., 2008; Harbluk et al., 2007). Lane ChangeInitiation (LCI) represents an event detection measure and previousresearch has found that drivers ability to detect and respond toevents in a timely manner is degraded when performing secondarytasks (Greenberg et al., 2003; Strayer and Drews, 2004; Strayeret al., 2003).One explanation for the current LCI results may rest with thenature of the lane change events, which are expected and highlypredictable because they occur at regular and approximately equalintervals. Drivers may, therefore, be able to anticipate the lanechange events and regulate their behaviour with the secondarytask accordingly so that their response to these events is notdegraded, and may even be improved if they are assigning higherpriority to the driving task. Indeed, when observing the partici-pants complete the LCT, the experimenters noted that manyparticipants were regulating their secondary task engagement sothat they were only performing the task immediately after the lanechanges. Although other research has found that drivers havedifficulty responding to events when distracted, these events havetypically been unexpected or unanticipated (Klauer et al., 2006;Strayer et al., 2003). It may be that drivers ability to detect andrespond to events is less impaired by engagement in secondarytasks if these events are expected. This explanation may alsoaccount for why the percentage of correct lane changes was veryhigh in the dual-task conditions.The study data also have implications for the LCT instructions asstated in the ISO draft standard. An important factor that may haveinfluenced both the lateral control and eventdetection results is thelane change instructions given to participants. In line with the LCTISO draft standard (ISO, 2007), participants in the current experi-ment were instructed to change lanes as soon and as quickly aspossible after the lane change information appears on the sign.Unlike the instructions adopted in other LCT experiments (e.g.,Harbluk et al., 2007; 2009), the current participants were notinstructed to have completed their lane change by the time theyreach the lane change sign. While the instructions used in thecurrent study led to more gradual and, arguably, more naturalisticlane changes, it did have the effect of inducing much higher meandeviation values than have been found in other studies and,possibly, the inconsistent LCI scores (e.g., Bruyas et al., 2008;Harbluk et al., 2007, 2009, 24e30 p.). These more gradual lanechange manoeuvres may have reduced the level of attentionaldemand required by the LCT, making it less sensitive to the differ-ential effects of the secondary tasks. The use of different lanechange instructions to increase the level of LCT task demand mayincrease the sensitivity of the measures examined in this study.It is important to acknowledge that the 1.2 m mean deviationcriterion specified in the ISO standard was not met in this studybefore embarking on the experimental trials. Indeed, this criterionwas not reached for any of the trials, including the final baselinedrive (mean deviation 1.6 m). The relatively higher mean devi-ation scores found during the study are believed to be the result ofthe instructions given and not a lack of practice with the task. Ourresults highlight the potential implications of inconsistent inter-pretation of the LCT instructions and the need for the LCTinstructions to be made more explicit in the ISO draft standard,including whether lane changes are required to be completedbefore drivers reach the sign. Consideration should also be given towhether the 1.2 m criterion specified in the draft standard is toostrict, in that it represents a lane change manoeuvre that is nota realistic representation of what occurs in real driving.Another methodological issue concerns the use of the artificialsurrogate IVIS tasks. While these tasks allow the level of demand0100020003000400050006000700080009000BaselineLCT)sm(emiTnoi telpmoCaEasyHard050010001500200025003000BaselineLCT)sm(emiTnoi telpmoCbE asyHardFig. 5. Mean completion time (in milliseconds) for the a) visual and b) cognitive secondary tasks.K.L. Young et al. / Applied Ergonomics 42 (2011) 611e618616imposed by the secondary tasks to be easily and systematicallymanipulated, it can be argued that their forced-paced nature isdiscordant with real IVIS tasks, which are typically self-paced. Anattempt was made to overcome this limitation by making thesurrogate IVIS tasks semi-self paced, whereby drivers could take aslong as necessary to make their response, but the presentation ofnew tasks was controlled by the system. It is important, however,that the sensitivity of the LCT metrics are also examined using realIVIS tasks in order to see what effect self-paced interaction has ondrivers task allocation strategies and driving performance. Finally,this study used a relatively small and homogenous sample ofparticipants and the extent to which their LCT performance reflectsthat of the wider driving population is limited. Thus, furtherresearch examining the validity and sensitivity of the LCT mustemploy a wider sample of participants from a range of driving anddemographic backgrounds that may differ in their ability to copewith secondary task demands while driving.5. ConclusionsGiven its development as an ISO standard, the LCT is increasinglybeing used by researchers and policy makers to draw conclusionsabout the design of in-vehicle systems and the acceptability of usingthem when driving. While this is a positive step, it is important toestablish that the metrics used to draw such conclusions are sensi-tivetoarangeofdrivingandsecondarytaskdemands.ThisstudyhassoughttoestablishthesensitivityofseveralrecentLCTlateralcontroland event detection metrics to the differential effects of visual-manualandcognitivetaskswithvaryinglevelsofdemand.Anumberof the LCT metrics examined, particularly the event detectionmetrics,demonstratedpoorsensitivityinthisstudy.Thismaybedueto the expected nature of the LCT task, or could also be an artefact ofthe instructions used. Beyond evaluating LCTsensitivity, a contribu-tion of thispaperhasbeen todemonstratetheimportanceof theLCTinstructions and the manner in which they can alter the level ofdemand required by the task and, hence, the results obtained.Currently, the LCT instructions are unclear and open to interpreta-tion. Indeed, different researchers have used different lane changeinstructions, with noticeable effects on LCT results. Along with therefinements being made to the LCT metrics (e.g., using an adaptivepathtrajectorymodelratherthanthenormativemodelincalculatingmean deviation; ISO (2009), the provision of clear and explicit lanechange instructions will be essential for increasing the utility of theLCT as an IVIS design and safety evaluation tool.AcknowledgementsThis research was funded by the Commonwealth of Australia,through the Cooperative Research Centre for Advanced AutomotiveTechnology. The cognitive task used was adapted from a taskdeveloped byEveMitsopoulous-Rubensaspartof herPhDresearch.ReferencesBrookhuis, K.A., de Vries, G., de Waard, D., 1991. The effects of mobile telephoningon driving performance. Accident Analysis and Prevention 23, 309e316.Bruyas, M.P., Brusque, C., Tattegrain, H., Auriault, A., Aillerie, I., Duraz, M., 2008.Consistency and sensitivity of lane change test according to driving simulatorcharacteristics. IET Intelligent Transport Systems 2, 306e314.Burns, P.C., Trbovich, P.L., McCurdie, T., Harbluk, J.L., 2005. Measuring distraction:task duration and the lane-change test (LCT). In: Human Factors and Ergo-nomics Society 49th Annual Meeting.Chiang, D.P., Brooks, A.M., Weir, D.H.D.H., 2004. On the highway measures of driverglance behavior with an example automobile navigation system. AppliedErgonomics 35, 215e223.Collet, C., Clarion, A., Morel, M., Chapon, A., Petit, C., 2009. Physiological andbehavioural changes associated to the management of secondary tasks whiledriving. Applied Ergonomics 40, 1041e1046.Consiglio, W., Driscoll, P., Witte, M., Berg, W.P., 2003. Effect of cellular telephoneconversations and other potential interference on reaction time in a brakingresponse. Accident Analysis & Prevention 35, 495e500.Engstrom, J., Johansson, E., Ostlund, J., 2005. Effects of visual and cognitive load inreal and simulated motorway driving. Transportation Research Part F: TrafficPsychology and Behaviour 8, 97e120.Engstrm, J., Markkula, G., 2007. Effects of visual and cognitive distraction on lanechange test performance. In: Driving Assessment 2007. Stevenson, Washington.Gelau, C., Henning, M.J., Krems, J.F., 2009. On the reliability of the occlusion tech-nique as a tool for the assessment of the HMI of in-vehicle information andcommunication systems. Applied Ergonomics 40, 181e184.Greenberg, J., Tijerina, L., Curry, R., Artz, B., Cathey, L., Grant, P., Kochlar, D., Kozak, K.,Blommer, M., 2003. Evaluation of driver distraction using an event detectionparadigm. Journal of the Transportation Research Board 1843.Harbluk, J.L., Burns, P.C., Lochner, M., Trbovich, P.L., 2007. Using the lane-change test(LCT) to assess distraction: tests of visual-manual and speech-based operation ofnavigationSysteminterfaces.In:DrivingAssessment2007.Stevenson,Washington.Harbluk, J.L., Mitroi, J.S., Burns, P.C., 2009. Three navigation systems with threetasks: using the lane change test (LCT) to assess distraction demand. In: FifthInternational Driving Symposium on Human Factors in Driver Assessment,Training and Vehicle Design. Big Sky, Montana.Harms, L., Patten, C., 2003. Peripheral detection as a measure of driver distraction. Astudy of memory-based versus system-based navigation in a built-up area.Transportation Research Part F: Traffic Psychology and Behaviour 6, 23e36.ISO, 2007. Road vehicles e ergonomic aspects of transport information and controlsystems - Simulated lane change test to assess driver distraction. In: ISO/DIS26022.DraftInternationalStandard.InternationalOrganizationforStandardization.ISO, 2009. Road vehicles e ergonomic aspects of transport information and controlsystems e Simulated lane change test to assess in-vehicle secondary taskdemand. In: ISO/DIS 2
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