Sensation Seeking and Drivers Glance Behavior while Engaging in a Secondary Task

Transcription

1 Proceedings of the Human Factors and Ergonomics Society 206 Annual Meeting 864 Sensation Seeking and Drivers Glance Behavior while Engaging in a Secondary Task Joonbum Lee, Bruce Mehler, Bryan Reimer, and Joseph F. Coughlin MIT AgeLab and New England University Transportation Center, Cambridge, MA, USA To investigate possible relationships between drivers sensation seeking and glance behavior while interacting with human-machine interfaces, a total of 70 drivers eye-glance data, Sensation Seeking Scale (SSS), and Driver Behavior Questionnaire (DBQ) data were collected and analyzed. Participants conducted radio tuning tasks with two standard production interfaces while driving on a highway, and their glance allocations to defined regions were recorded and manually annotated. Results showed that sensation seeking scores were related with self-reported violation scores, off-road glance patterns, and driving speed: () violation scores of DBQ were positively correlated with sensation seeking, (2) mean and standard deviation of off-road glance duration were positively correlated with sensation seeking for younger drivers (under 40 years), (3) total off-road glance time per minute and number of off-road glances per minute were positively correlated with sensation seeking for older drivers (over 40 years), and (4) percentage of speed change was negatively correlated with sensation seeking for both younger and older drivers. The results indicate that sensation seeking is associated with drivers off-road glance patterns and driving behavior. These observations further highlight the relationship between personal traits and driver behavior. Copyright 206 by Human Factors and Ergonomics Society. DOI 0.77/ INTRODUCTION Relationships between driver characteristics and risky driving behavior have been studied for decades. Both social cognition and personality traits of drivers have been examined to understand risky driving behavior (for instance, Ulleberg & Rundmo, 2003). In particular, significant relationships have been observed between sensation-seeking personality characteristics and self-reported (e.g., DBQ)/observed risky driving behaviors (Bachoo, Bhagwanjee, & Govender, 203; Jonah, 997; Schwebel et al., 2007; Schwebel, Severson, Ball, & Rizzo, 2006), and self-reported engagement in driver distraction (Chen, Donmez, Hoekstra-Atwood, & Marulanda, 206). For example, Jonah (997) reviewed 40 studies related to this topic, and reported that most of the studies (36 out of 40) found a significant positive relationship between sensation seeking and some aspect of risky driving with correlations generally in the range. Evaluating relationships between driver characteristics (e.g., sensation seeking) and their potential interaction with driver distractibility may well become increasingly significant as the number and richness of human-machine interfaces (HMIs) proliferate in modern vehicles. For this reason, the main objective of this manuscript is to examine whether drivers sensation-seeking scores are related to objective measures of glance behavior and driving speed while completing tasks with production-level HMIs. To achieve this goal: () a total of 70 drivers Sensation Seeking Scale (SSS) and Driver Behavior Questionnaire (DBQ) scores were collected to assess their sensation-seeking level and selfreported driving behavior, (2) glance behavior while interacting with HMIs were used to calculate duration and frequency of off-road glances, and (3) vehicle telemetry data (e.g., speed) were measured to assess observed driving behavior. As age differences in glance behavior and a higher variability in glance duration from older drivers have been reported (for instance, Lee et al., 205), four age groups from an existing sample were regrouped into two (younger and older driver group) to decrease the variability. Note, the terms, younger drivers and older drivers, in this manuscript only refer relative age difference between the two groups and do not follow more commonly used cutoffs to define older drivers. METHODS This study is a secondary analysis of data collected from two research projects conducted by the MIT AgeLab. Complete methodological details can be found in technical reports (Mehler, Reimer, Dobres, & Coughlin, 205a; Mehler et al., 205b) and only key details related to this study are presented here. Participants Recruitment drew from the greater Boston area using online and newspaper advertisements. Four age groups (20-24, 25-39, 40-54, and 55 over) were formed to conform to National Highway Traffic Safety Administration (NHTSA) s recommendations (National Highway Traffic Safety Administration, 203). Participants were required to meet seven criteria: () a valid driver s license for more than three years, (2) driving on average three or more times per week, (3) being in self-reported reasonable good health for their age and meeting a set of health exclusion criteria, (4) clearly understanding and speaking English, (5) no police reported accident in the past year, (6) not actively using any medications causing drowsiness, and (7) not having been a participant in an AgeLab on-road driving study in the past six months. Out of a total of 96 participants, only cases that had fully completed the SSS (responded to all forced-choice item pairs) were included in the main analysis, resulting in a total

2 Proceedings of the Human Factors and Ergonomics Society 206 Annual Meeting 865 of 70 cases (mean age = 38.79, SD age = 6.3, n of male participants = 32, n of female participants = 38). Apparatus As noted, data were collected in similar on-road experiments in two different standard production vehicles. Study employed a 204 Mercedes CLA (with the COMMAND infotainment system) and Study 2 was conducted in a 205 Toyota Corolla (with Entune Premium Audio with navigation). Both vehicles were instrumented with custom data acquisition systems for time-synchronized recording of data from multiple sources (e.g., video cameras, CAN bus, and a Garmin 8X GPS). Tasks Drivers were instructed to complete a number of invehicle HMI tasks while driving on a highway. For the purposes of this paper, the analysis considered visual-manual radio tuning, which required multiple manual engagements. The interactions usually required several off-road glances to the center stack area where the radio interface was located. The basic protocol was modeled on the hard tuning task employed in the Driver Workload Metrics project (Angell et al., 2006) and structured to conform to the visual-manual radio reference tuning task as specified in the NHTSA (203) distraction guidelines for in-vehicle electronic devices. In the CLA system, the task required two hard button presses (to select the radio menu and change the frequency band) and knob rotation to obtain a specified frequency. In the Corolla system, the task required two touch screen presses and knob rotation. During the on-road assessment, participants were first presented with two trials of a simpler (one press ) preset station selection task, followed by two trials of the multi-step radio tuning task. Glance metrics presented were averaged across the two trials of the multi-step task. Questionnaire Sensation Seeking Scale. The Sensation Seeking Scale (SSS) was designed to assess personal traits (Zuckerman, Kolin, Price, & Zoob, 964). SSS form V (Zuckerman, Eysenck, & Eysenck, 978) was employed in this study. SSS form V consists of 40 sets of paired statements (in forcedchoice format in which the respondent selects the statement that is most like them). A total score and four sub-scales result. The four sub-scales are: () Thrill and Adventure Seeking (TAS), (2) Disinhibition (Dis), (3) Experience Seeking (ES), and (4) Boredom Susceptibility (BS). Driver Behavior Questionnaire. The Manchester Driver Behavior Questionnaire (DBQ) (Reason, Manstead, Stradling, Baxter, & Campbell, 990) has been widely used as an instrument for obtaining self-report information on aberrant driving behaviors and DBQ scores have been found to correlate with both retrospective and prospective crash involvement. The DBQ consists of 24 questions (in a six-point scale to indicate how often they engaged in each type of behavior while driving) and three sub-scales: () Errors, (2) Violations, and (3) Lapses (Lawton, Parker, Manstead, & Stradling, 997; Reason et al., 990). A U.S. version (see Reimer et al., 2005) was employed here; previous work has shown DBQ scores to be associated with objective measures of actual driving behavior (Zhao et al., 202). Procedure Participants received training on how to complete each of the tasks prior to being asked to consider engaging with them while driving. Training took place in a parking lot at MIT and at a rest stop on I-495. The content of the training in each location depended on a counter-balanced ordering of tasks across the sample. The ordering of the tasks with-in a pairing was randomized across the sample. The manual radio tuning reference tasks were always presented last on the return route on I-93 (see Figure ) (note that this procedure describes the entire experimental process although only the manual radio tuning tasks were analyzed in the present study). Glance Coding Figure : Overview of the experimental protocol. Manual glance coding was conducted by two independent research associates by watching in-vehicle video of the drivers faces. The MIT AgeLab Video Annotator ( was used. A third independent research associate mediated discrepant glances if: () the coders started or ended their coding at different times, (2) a divergent number of glances was coded, (3) the coders identified a divergent target for a glance, or (4) the timing of a coded glance differed by more than 200ms. A total of 2 codes were available ( road, rearview mirror, center stack, instrument cluster, left, right, left blind spot, right blind spot, passenger, other, eyes not visible, and poor video quality ); the latter three were excluded from the analysis. RESULTS Data reduction and analyses were conducted using the R statistical language (R Development Core Team, 2008). As noted, previous studies have reported age differences in glance behavior and a higher variability in glance duration from older drivers (for instance, Lee et al., 205), therefore a set of analyses was separately applied to the entire group (N = 70), a younger driver group which combined the first two age groups (i.e., 20 to 39 years) (n = 36, range 20 to 3 years, mean age = 24.47, and SD = 3.28), and an older driver group which combined the last two age groups (i.e., over 40 years) (n = 34, range 40 to 69 years, mean age = 53.94, and SD = 8.52). The results section consists of three parts. First, a relationship between SSS and DBQ was tested. Second, a relationship between SSS and glance measurements was tested. Lastly,

3 Proceedings of the Human Factors and Ergonomics Society 206 Annual Meeting Mean_duration SD_duration Percentage_long_glance Frequency_glance_pm Total_glance_time_pm Mean_duration SD_duration Percentage_long_glance Frequency_glance_pm Total_glance_time_pm Mean_duration SD_duration Percentage_long_glance Frequency_glance_pm Total_glance_time_pm Mean_duration SD_duration Percentage_long_glance Frequency_glance_pm Total_glance_time_pm SSS and driving speed was tested (supplementary figures can be found at SSS and DBQ Figure 2: Correlations between SSS and glance measures for the younger driver group (note that only statistically significant correlations present coefficient values, colors of values represent direction of correlation, and brightness represents magnitude of correlation). Correlations between sub-scales of SSS and sub-scales of DBQ are considered first. DBQ Violation scores were positively correlated with all of the sub-scale scores (and total scores) of SSS, except BS scores, r (67) =.43, p <.00 with total scores, r (67) =.32, p <.0 with TAS, r (67) =.25, p <.05 with Dis, r (67) =.32, p <.0 with ES. The same analysis was applied to the younger driver group and older driver group respectively. In the younger driver group, Violation scores were positively correlated with SSS total, r (34) =.49, p <.0 and Dis scores, r (34) =.45, p <.0. In the older driver group, Violation scores were positively correlated with SSS total scores, r (3) =.4, p <.05, TAS scores, r (3) =.42, p <.05, and ES scores, r (3) =.39, p <.05. SSS and Off-Road Glance Measurements Similar to the previous section, correlation tests were applied to SSS and off-road glance measurements observed during the radio tuning tasks. Mean off-road glance duration (Mean_duration) and percentage of long duration (i.e., greater than 2 seconds) glances (percentage_long_glance) were measured following NHTSA guidelines (National Highway Traffic Safety Administration, 203). Additionally, standard deviation of off-road glance duration (SD_duration), number of off-road glances per minute (Frequency_glance_pm), and total off-road glance time per minute (Total_glance_time_pm) were considered. None of the glance measurements was significantly correlated with SSS scores for the entire group. However, interestingly, for the younger driver group, TAS scores were positively correlated with mean off-road glance duration, r (34) =.35, p <.05, and standard deviation of off-road glance duration, r (34) =.37, p <.05. This may indicate that younger drivers with higher TAS scores (e.g., seeking more thrill and adventure) tended to have longer mean off-road glance durations and higher variability in mean off-road glance duration (see Figure 2). For the older driver group, TAS scores were positively correlated with total off-road glance time per minute, r (32) =.45, p <.0, and with number of off-road glances per minute, r (32) =.49, p <.0. Thus, older drivers with higher TAS scores tended to have longer total off-road glance times per minute and more off-road glances per minute during radio tuning tasks (see Figure 3). SSS and Driving Speed Figure 3: Correlations between SSS and glance measures for the older driver group. This analysis considered the mean and standard deviation of speed (Mean_speed, SD_speed) during the radio tuning task. In addition, the percentage change in speed (Percentage_speed_change) from a two-minute single-task baseline period (i.e., just driving) before the radio tuning tasks was calculated (see the equations below).!"#$"%&!"#!h!"#$!"!"##$ =!"##$!"#$!"##$!"!"#$%&'$!"#$#%& 00!"##$ =!"#$!"##$!"#$%&!"#$%!"#$#%!"#$#!"#$!"##$!"!"#$%&'$!"#$#%& Table shows mean speed of both baseline and radio tuning task periods for the younger and older driver group. Table : Mean speed (mph) of baseline and radio tuning task periods for the younger and older driver group. Baseline Radio tuning Younger driver 60 (SD = 2.56) (SD =.07) Older driver (SD = 2.39) 57.7 (SD =.25)

4 Proceedings of the Human Factors and Ergonomics Society 206 Annual Meeting Mean_speed SD_speed Mean_speed SD_speed Percentage_speed_change Mean_speed SD_speed Mean_speed SD_speed Percentage_speed_change Percentage_speed_change Percentage_speed_change Figure 4: Correlations between SSS and speed measures for the younger driver group. For the entire group, mean speed was positively correlated with TAS scores, r (68) =.24, p <.05, and percentage change of speed was negatively correlated with SSS total scores, r (68) = -.38, p <.0, TAS, r (68) = -.28, p <.05, Dis, r (68) = -.26, p <.05, and ES scores, r (68) = -.24, p <.05. For the younger driver group, percentage change of speed was negatively correlated with total SSS scores, r (34) = -.33, p <.05, and TAS scores, r (34) = -.35, p <.05 (see Figure 4). Similarly, for the older driver group, percentage change of speed was negatively correlated with total SSS scores, r (32) = -.57, p <.00, TAS, r (32 = -.36, p <.05, and Dis, r (32) = -.46, p <.0 (see Figure 5). This result may indicate that for both younger and older driver group, drivers with higher SSS scores drove slower when completing the radio tuning task compared to when just driving without secondary tasks. Especially, the older driver group showed a stronger relationship between percentage change of speed and SSS scores than the older driver group did. DISCUSSION The present study investigated relationships between drivers sensation seeking as measured by the SSS and () self-reported driving behavior, (2) glance behavior, and (3) observed driving behavior when engaged in a common secondary task (visual-manual radio tuning). In general agreement with previous work (for instance, Jonah, 997), this study found significant relationships between drivers sensation seeking and self-reported risky driving behavior (e.g., Violation scores in DBQ). Further analysis found some variation in the pattern of correlation across age groups of subscales of SSS and the Violation scores in DBQ. In the younger driver group, Disinhibition scores showed positive correlations with Violations, whereas in the older driver group, Thrill and Adventure Seeking and Experience Seeking scores were positively correlated with Violations. Total scores of SSS were positively correlated with Violations for both groups. Interestingly, sensation-seeking scores were also significantly correlated with off-road glance measures. In the younger driver group, mean off-road glance duration and standard deviation of off-road glance duration were positively correlated with Thrill and Adventure Seeking scores. This may Figure 5: Correlations between SSS and speed measures for the older driver group. indicate that personal traits play a role in drivers glance behavior. Specifically, younger drivers with higher sensationseeking scores tended to glance to off-road regions longer (and with higher variability in glance duration). In the older driver group, total off-road glance time per minute and number of off-road glances per minute were positively correlated with Thrill and Adventure Seeking scores. In other words, older drivers with higher sensation-seeking scores tended to spend more time glancing to off-road regions (probably the center stack areas while interacting the HMI) and glanced more often. As a significant correlation was found between both younger and older drivers self-reported violation scores and sensation seeking, significant relationships between speed measures and sensation-seeking scores was observed in both younger and older driver groups. In the younger driver group, the percentage change of speed was negatively correlated with total SSS scores and Thrill and Adventure Seeking scores. The older driver group showed similar patterns with higher correlation coefficients. The percentage change of speed was negatively correlated with total SSS scores, Thrill and Adventure Seeking, and Disinhibition scores. The direction of the correlation was interesting as it indicates that drivers with higher sensation-seeking scores showed a greater reduction in speed when they were engaged in secondary tasks (e.g., radio tuning). However, additional investigation will be required to determine whether this was an intentional speed compensation to mitigate the demand of the task or not. Other findings in this study support more engagement in the secondary task with higher sensation-seeking scores. These findings are also consistent with a previous study that showed a relationship between sensation seeking and self-reported distraction engagement (Chen et al., 206). Therefore, it might be more reasonable to hypothesize that drivers with higher sensationseeking scores were more absorbed in the secondary task, and as a consequence, less engaged in the primary task (e.g., maintaining speed). To examine potential variability associated with age, the four NHTSA age groupings of the sample were re-categorized into two groups (e.g., relatively younger and older groups). The analyses showed various patterns across the age groups. For example, glance measures that correlated with sub-scales

5 Proceedings of the Human Factors and Ergonomics Society 206 Annual Meeting 868 of SSS were not the same for the younger and older driver groups. For example, Thrill and Adventure Seeking was correlated with mean and standard deviation of the off-road glance duration in the younger driver group, whereas the same SSS sub-scale was correlated with total off-road glance time and number of off-road glances in the older driver group. Direction of correlations between sensation seeking and driving speed was consistent across the age groups, but the older driver group showed stronger relationships. These findings suggest that it may be appropriate to further consider the relationship between a drivers personal traits, glance behavior, and driving performance by age. This study considered potential contributors to differences in driver behavior by focusing on a broader set of personality characteristics (e.g., sensation seeking) and its impact on offroad glance behavior and driving behavior. The results showed that sensation seeking may contribute to explaining drivers off-road glance patterns and driving performance. The present study suggests a need for a more in-depth investigation of the relationship between driver characteristics and driver behavior. Deeper insight in this area may help formulate stronger predictions as to how certain individuals will behave on the roadway. Optimally, interventions can be targeted to improve potentially aberrant behaviors before they are observed. ACKNOWLEDGEMENTS Support for this work was provided in part by the US DOT s Region I New England University Transportation Center at MIT and the Toyota Class Action Settlement Safety Research and Education Program. The views and conclusions being expressed are those of the authors, and have not been sponsored, approved, or endorsed by Toyota or plaintiffs class counsel. Acknowledgement is also extended to Toyota s CSRC for providing funding for the studies from which the data for this analysis was drawn. Appreciation is extended to all MIT AgeLab staff, especially Alea Mehler, Anthony Pettinato, Daniel Brown, Hale McAnulty, Hillary Abraham, Jonathan Dobres, and Thomas McWilliams. REFERENCES Angell, L., Auflick, J., Autria, P. A., Kochhar, D., Tijerina, L., Biever, W., et al. (2006). Driver Workload Metrics Project Task 2 Final Report (No. DOT HS ). Washington, D.C.: National Highway Traffic Safety Administration. Bachoo, S., Bhagwanjee, A., & Govender, K. (203). The influence of anger, impulsivity, sensation seeking and driver attitudes on risky driving behaviour among post-graduate university students in Durban, South Africa. Accident Analysis and Prevention, 55, Chen, H.-Y. W., Donmez, B., Hoekstra-Atwood, L., & Marulanda, S. (206). Self-reported engagement in driver distraction: An application of the Theory of Planned Behaviour. 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