Kinematic cues in driver-pedestrian communication to support safe road crossing.

OBJECTIVE: Right-of-way negotiation between drivers and pedestrians often relies on explicit (e.g., waving) and implicit (e.g., kinematic) cues that signal intent. Since effective driver-pedestrian communication is important for reducing safety-relevant conflicts, this study uses information theory to identify vehicle kinematic behaviors that provide the greatest information gain and serve as cues for pedestrians to cross safely. DATA SOURCES: A driver-pedestrian dataset with 348 interactions was extracted from a large naturalistic driving data collection effort. It includes 325 instances of a pedestrian crossing the vehicle's path and 23 instances in which the vehicle did not yield to a pedestrian. Kinematic data were collected from the vehicle's CAN. Pedestrian behaviors, driver cues, and contextual information were manually annotated from a forward-facing video. METHODS: We used kernel density estimation to quantify the probabilities of vehicle acceleration, speed, and standard deviation of speed, for a given vehicle position and pedestrian behavior. Mutual information was then calculated between the estimated distributions given a pedestrian behavior (crossing/not crossing; walking/pausing) across intersection types (protected, e.g., stop signs; designated, e.g., crosswalks; and undesignated, e.g., jaywalking). RESULTS: The patterns mutual information conveyed by vehicle kinematics differed across measures (acceleration, speed, and standard deviation of speed) reaching peak values (in bits of information) at different distances from the pedestrian path. The mutual information conveyed by vehicle acceleration and pedestrian crossing behaviors peaked the farthest from the pedestrian path in the designated crossings, about 18 m away from the pedestrian path, with a difference in median deceleration of 1.01 m/s2 (p < 0.001) between pedestrian pausing and walking epochs. For protected crossings, the peak in mutual information occurred closer (10 m) to the pedestrian path, where median vehicle deceleration was significantly lower (0.55 m/s2; p < 0.05) in pausing epochs compared to walking epochs. For undesignated crossings, the peak in mutual information was the closest to the pedestrian crossing path, around 5 m, and was associated with a stronger deceleration behavior in pedestrian crossing epochs (-0.33 m/s2; p < 0.1). Vehicle speed demonstrated a similar sensitivity to distance from the pedestrian path across intersection types. Lastly, looking at the outcome of pedestrian behavior (i.e., crossing/not crossing), we find that the mutual information conveyed by acceleration, speed, and standard deviation of speed, peaked when the vehicle was at 30 m (stronger braking -0.37 m/s2; p < 0.1) and 10 m away, with greater acceleration (0.81 m/s2; p < 0.001) and faster speeds (2.41 m/s; p < 0.001) in pedestrian crossing epochs. SIGNIFICANCE OF RESULTS: This study examined driver-pedestrian information exchange using vehicle kinematic behavioral cues. We find that the differences in mutual information are shaped by multiple factors including the intersection type. In general, there was less mutual information gain in protected crossings which may be explained by unambiguous right-of-way rules guiding driver and pedestrian behavior, reducing the need for negotiation. Driver-pedestrian interactions in designated crossings seem to take place over a larger distance range compared to undesignated or protected crossings. These findings may support the design of automated driving and pedestrian safety systems that are able to consider the type, strength, and timing of kinematic cues to optimize driver-pedestrian negotiation. Eventually, such systems may enhance safe, efficient, and social interactions with pedestrians.

Driver response and recovery following automation initiated disengagement in real-world hands-free driving.

OBJECTIVE: Advanced driver assistance systems are increasingly available in consumer vehicles, making the study of drivers' behavioral adaptation and the impact of automation beneficial for driving safety. Concerns over driver's being out-of-the-loop, coupled with known limitations of automation, has led research to focus on time-critical, system-initiated disengagements. This study used real-world data to assess drivers' response to, and recovery from, automation-initiated disengagements by quantifying changes in visual attention, vehicle control, and time to steady-state behaviors. METHODS: Fourteen drivers drove for one month each a Cadillac CT6 equipped with Super Cruise (SC), a partial automation system that, when engaged, enables hands-free driving. The vehicles were instrumented with data acquisition systems recording driving kinematics, automation use, GPS, and video. The dataset included 265 SC-initiated disengagements identified across 5,514 miles driven with SC. RESULTS: Linear quantile mixed-effects models of glance behavior indicated that following SC-initiated disengagement, the proportions of glances to the Road decreased (Q50Before=0.91, Q50After=0.69; Q85Before=1.0, Q85After=0.79), the proportions of glances to the Instrument Cluster increased (Q50Before=0.14, Q50After=0.25; Q85Before=0.34, Q85After=0.45), and mean glance duration to the Road decreased by 4.86 sec in Q85. Multinomial logistic regression mixed-models of glance distributions indicated that the number of transitions between glance locations following disengagement increased by 43% and that glances were distributed across fewer locations. When driving hands-free, take over time was significantly longer (2.4 sec) compared to when driving with at least one hand on the steering wheel (1.8 sec). Analysis of moment-to-moment distributional properties of visual attention and steering wheel control following disengagement indicated that on average it took drivers 6.1 sec to start the recovery of glance behavior to the Road and 1.5 sec for trend-stationary proportions of at least one hand on the steering wheel. CONCLUSIONS: Automation-initiated disengagements triggered substantial changes in driver glance behavior including shorter on-road glances and frequent transitions between Road and Instrument Cluster glance locations. This information seeking behavior may capture drivers' search for information related to the disengagement or the automation state and is likely shaped by the automation design. The study findings can inform the design of more effective driver-centric information displays for smoother transitions and faster recovery.

Interdependence of driver and pedestrian behavior in naturalistic roadway negotiations.

OBJECTIVE: This paper characterizes the actions of pedestrian-driver dyads by examining their interdependence across intersection types (e.g., zebra crossings, stop signs). Additionally, the analysis of interdependence captures other external factors, such as other vehicles or pedestrians, that may influence the interaction. METHODS: A 228 epoch vehicle-pedestrian interaction dataset was extracted from a large naturalistic driving data collection effort, which included vehicle, pedestrian, and contextual information (e.g., intersection type, jaywalking, vehicle maneuver, and lead vehicle presence). An expanded Actor-Partner Interdependence Model (APIM) was used to analyze driver-pedestrian dyads using driver and pedestrian standard deviations of velocity as the independent variables and wait times as dependent variables. APIM structural equation models were augmented to include driver effects (i.e., lead vehicle and maneuver type) and pedestrian effects (i.e., lead pedestrian, crossing group size, crossing direction). RESULTS: The level of protection afforded by an intersection had an effect on the extent of driver-pedestrian dyadic behavior. Interactions in undesignated crossings (i.e., jaywalking) were associated with interdependent behavior whereas interactions in designated crossings (i.e., crosswalks and parking lots) showed a partner effect on the driver's wait time but no significant corresponding partner effect on the pedestrian. Finally, protected intersection interactions (i.e., traffic lights and stop signs) demonstrated no significant partner effects. CONCLUSIONS: The difference in behavior patterns associated with the intersection type and level of protection shows that context can mediate the level of negotiation required between drivers and pedestrians. These findings inform how context and driver-pedestrian interactions should be incorporated in future modeling efforts which may, ultimately, support design of automated systems that are able to interact more safely, efficiently, and socially.

Characterizing driver speeding behavior when using partial-automation in real-world driving.

Objective: Speeding is a prevalent and complex risky behavior that can be affected by many factors. Understanding how drivers speed is important for developing countermeasures, especially as new automation features emerge. The current study seeks to identify and describe types of real-world speeding behaviors with and without the use of partial-automation.Methods: This study used a combination of supervised and unsupervised data analysis techniques to assess relevant factors in real-world speeding epochs, extracted from the MIT Advanced Vehicle Technology Naturalistic Driving Study, and classified them into distinct speeding behaviors. Speeding epochs were defined as traveling at least 5 mph over the speed limit for a minimum duration of 3 s. Vehicle speed-exceedance profiles were characterized over time using Dynamic Time Warping and included in multivariate models that evaluated the associations between different features of the speeding epochs, such as speeding duration and magnitude. Finally, the identified features were used to cluster speeding behaviors using the Gower dissimilarity measure.Results: The analysis yielded four types of behaviors in both partially-automated and manual driving: (i) Incidental speeding (low duration, low magnitude), (ii) Moderate speeding (low duration, moderate magnitude), (iii) Elevated speeding (moderate duration, high magnitude), and (iv) Extended speeding (long duration, high magnitude). When comparing the behaviors with and without partial-automation use, both Incidental and Moderate speeding were found to have significantly longer durations with partial-automation than manual driving. Elevated speeding was found to be more prevalent and associated with higher magnitudes during manual than with partially-automated driving. Finally, although Extended speeding was more prevalent during automation use, it was associated with a lower mean and maximum speed magnitude compared to Extended speeding during manual driving.Conclusions: This work highlights the variability in speeding behavior between and within partially-automated and manual driving. The design of systems that mitigate risky speeding behaviors should consider targeting divergent behaviors observed between manual and automated driving as a mechanism to mitigate the prevalence of the different behaviors associated with each state.

Speeding behavior while using adaptive cruise control and lane centering in free flow traffic.

OBJECTIVE: Adaptive cruise control (ACC) and lane centering are usually marketed as convenience features but may also serve a safety purpose. However, given that speeding is associated with increased crash risk and worse crash outcomes, the extent to which driver's speed using ACC may reduce the maximum safety benefit they can obtain from this system. The current study was conducted to characterize speeding behavior among drivers using adaptive cruise control and a similar system with added lane centering. METHODS: We recruited 40 licensed adult drivers from the Boston, Massachusetts, metro area. These drivers were given either a 2017 Volvo S90 or a 2016 Range Rover Evoque to use for about 4 weeks. RESULTS: Drivers were significantly more likely to speed while they used ACC (95%) relative to periods of manual control (77%). A similar pattern arose for drivers using ACC with added lane centering (96% vs. 77%). Drivers who traveled over the posted limit with these systems engaged also sped slightly faster than drivers controlling their vehicle manually. Finally, we found that these differences were the most pronounced on limited-access roads with a lower speed limit (55 mph). CONCLUSIONS: These findings point to a possible obstacle to obtaining the full safety potential from this advanced vehicle technology. Any consideration of the net safety effect of ACC and lane centering should account for the effects of more frequent and elevated speeding.

A model for naturalistic glance behavior around Tesla Autopilot disengagements.

OBJECTIVE: We present a model for visual behavior that can simulate the glance pattern observed around driver-initiated, non-critical disengagements of Tesla's Autopilot (AP) in naturalistic highway driving. BACKGROUND: Drivers may become inattentive when using partially-automated driving systems. The safety effects associated with inattention are unknown until we have a quantitative reference on how visual behavior changes with automation. METHODS: The model is based on glance data from 290 human initiated AP disengagement epochs. Glance duration and transition were modelled with Bayesian Generalized Linear Mixed models. RESULTS: The model replicates the observed glance pattern across drivers. The model's components show that off-road glances were longer with AP active than without and that their frequency characteristics changed. Driving-related off-road glances were less frequent with AP active than in manual driving, while non-driving related glances to the down/center-stack areas were the most frequent and the longest (22% of the glances exceeded 2 s). Little difference was found in on-road glance duration. CONCLUSION: Visual behavior patterns change before and after AP disengagement. Before disengagement, drivers looked less on road and focused more on non-driving related areas compared to after the transition to manual driving. The higher proportion of off-road glances before disengagement to manual driving were not compensated by longer glances ahead. APPLICATION: The model can be used as a reference for safety assessment or to formulate design targets for driver management systems.

Driver behavior and the use of automation in real-world driving.

BACKGROUND: The emergence of partial-automation in consumer vehicles is reshaping the driving task, the driver role, and subsequent driver behavior. When using partial-automation, drivers delegate the operational control of the dynamic driving task to the automation system, while remaining responsible for monitoring, object/event detection, response selection, and execution. Hence, driving has become a collaboration between driver and automation systems that is characterized by dynamic Transfers of Control (TOC). OBJECTIVE: This study aimed to assess how drivers leverage automation in real-world driving, identify driver and system-initiated TOCs, and provide a taxonomy to capture the underlying driver behaviors associated with automation disengagement. METHODS: Fourteen participants drove instrumented Cadillac CT6 vehicles for one-month each, yielding 1690 trips (22,108 miles), with a total of 5343 TOCs between manual driving, SAE Level 1 Adaptive Cruise Control (ACC), and SAE Level 2 Super Cruise (SC). RESULTS: The use of automation on limited access highways was prevalent (40 % of the miles driven were with SC and 10 % with ACC) yet not continuous. Drivers frequently initiated transitions between automation levels (mean = 9.98, SD = 8.32, transitions per trip), temporarily taking over the longitudinal and/or lateral vehicle control. These transitions were not necessarily related to immediate risk mitigation, but rather to the execution of functions beyond the automation system's capabilities or representing preferences in task execution. Driver-initiated TOCs from SC to manual driving followed the structure and temporal aspects of the hierarchical model of driver behavior. Strategic, Maneuver, and Control TOCs were associated with significantly different patterns of vehicle kinematics, automation disengagement modality, and TOC duration. System-initiated automation disengagements from SC to manual driving were rare (1%). CONCLUSIONS: Generalizing from objective, real-world driving data, this study provides an ecologically valid taxonomy for transfer of control building upon the hierarchical model of driver behavior. We show that driver-automation interactions can occur in each level of the hierarchical model and that TOCs are part of the driver's strategic, maneuver, and control levels of decision making. Thus, TOCs are not isolated or rare events, but rather an integral part of an ongoing, continuous and dynamic collaboration. This taxonomy contextualizes TOCs, paving the way for greater understanding of when and why drivers will takeover control, exposes the underlying motivations for TOCs, and characterizes how these are reflected in the driver's actions. The findings can inform the development of driver-centered automation systems as well as policies and guidelines for current and future automation levels.

Crash rates over time among younger and older drivers in the SHRP 2 naturalistic driving study.

OBJECTIVE: To examine crash rates over time among 16-17-year-old drivers compared to older drivers. METHODS: Data were from a random sample of 854 of the 3,500 study participants in SHRP 2, a U.S. national, naturalistic driving (instrumented vehicle) study. Crashes/10,000 miles by driver age group, 3-month period, and sex were examined within generalized linear mixed models. RESULTS: Analyses of individual differences between age cohorts indicated higher incidence rates in the 16-17-year old cohort relative to older age groups each of the first four quarters (except the first quarter compared to 18-20 year old drivers) with incident rate ratios (IRR) ranging from 1.98 to 18.90, and for the full study period compared with drivers 18-20 (IRR = 1.69, CI = 1.00, 2.86), 21 to 25 (IRR = 2.27, CI = 1.31, 3.91), and 35 to 55 (IRR = 4.00, CI = 2.28, 7.03). Within the 16-17-year old cohort no differences were found in rates among males and females and the decline in rates over the 24-month study period was not significant. CONCLUSIONS: The prolonged period of elevated crash rates suggests the need to enhance novice young driver prevention approaches such as Graduated Driver's Licensing limits, parent restrictions, and post-licensure supervision and monitoring. Practical Applications: Increases are needed in Graduated Driver's Licensing limits, parent restrictions, and postlicensure supervision and monitoring.

Learner Driver Experience and Teenagers' Crash Risk During the First Year of Independent Driving.

Importance: One mechanism for teenagers' elevated crash risk during independent driving may be inadequate learner driving experience. Objective: To determine how learner driver experience was associated with crash risk during the first year of independent driving. Design, Setting, and Participants: Youth aged 15.5 to 16.1 years at recruitment were eligible to participate. Participants' vehicles were instrumented with sensors, and driving was recorded during the learner period through 1 year of independent driving. Data were collected from January 2011 through August 2014 in southwestern Virginia. Exposures: The amount, consistency and variety of practice, driving errors, and kinematic risky driving (KRD) rates during the learner period were recorded. Surveys, including one on sensation-seeking personality traits, were assessed at baseline. Main Outcomes and Measures: Cox proportional hazard regressions examined associations between individual characteristics and learner driving experience with driving time to first crash and all crashes in the first year of independent driving. So that hazard ratios (HRs) can be directly comparable, units of measurement were standardized to the interquartile range. Results: Of 298 individuals who responded to recruitment, 90 fulfilled the criteria and 82 completed the study (of whom 75 were white [91%] and 44 were girls [54%]). Teenage participants drove a mean (SD) of 1259.2 (939.7) miles over 89 days during the learner period. There were 49 property-damage crashes and/or police-reportable crashes during independent driving. Factors associated with driving time to first crash included higher sensation-seeking personality scale scores (HR, 1.67 [95% CI, 1.08-2.57] per 0.75-unit increase), learner driving KRD rates (HR, 1.27 [95% CI, 1.12-1.43] per 9.24-unit increase), and learner driving errors (HR, 0.44 [95% CI, 0.22-0.86] per increase of 6.48 errors). Similar results were obtained for all crashes in the first year, with the addition of consistency of learner driving practice (HR, 0.61 [95% CI, 0.38-0.99] per 0.23-unit increase). Conclusions and Relevance: Individual characteristics and learner driving experiences were associated with crash risk during independent driving. As expected, there was an association between sensation seeking and crashes. Elevated KRD rates during the learner period may reflect risky driving behavior among novices or tolerance to abrupt maneuvers by parents who supervise driving. Consistent practice throughout the learner period could reduce teenage crash risk, which is supported by learning theories indicating distributed practice is effective for developing expertise. Errors during practice may constitute learning events that reinforce safer driving. Physicians could encourage parents to provide opportunities for regular practice driving and monitor their teenager's KRD rates during the learner period using in-vehicle or smartphone-based technology.

Kinematic risky driving behavior among younger and older drivers: Differences over time by age group and sex.

Objective: This research examined the incidence rates of elevated gravitational force events (kinematic risky driving, KRD) among 16- to 17-year-old drivers compared to those of 18- to 20-year-old, 21- to 25-year-old, and 35- to 55-year-old drivers over a 12-month period. Methods: Data were sampled from the Strategic Highway Research Program 2 (SHRP2) naturalistic driving study that recruited a U.S. national sample of study participants. General linear mixed models (GLIMMIX) for recurrent events were used to estimate KRD incident rates for age cohorts in 3-month periods. Results: KRD incidence rates for 16- to 17-year-old drivers were higher than the rates for older drivers at each 3-month period. Analyses of individual differences for the 12-month period indicated that incidence rates for the 16- to 17-year-old group were 1.84 times higher than the rates for 18- to 20-year-old drivers, 2.86 higher than those for 21- to 25-year-old drivers, and 4.92 times higher than those for 35- to 55-year-old drivers. The incident rate for 16- to 17-year-old males was 1.9 times higher than that for same-aged females in the first 3 months and 2.3 times higher over 12 months. Over the study period, KRD rates of 16- to 17-year-old participants declined 24.5% among females and 18.0% among males. Conclusions: KRD rates were higher among younger relative to older, more experienced drivers and did not decline over time, consistent with a protracted period of risky driving behavior. The persistently higher KRD rate among young drivers suggests the need to enhance crash prevention approaches, such as feedback about abrupt maneuvering, to young drivers and their parents.

Distracted Driving, Visual Inattention, and Crash Risk Among Teenage Drivers.

INTRODUCTION: Distracted driving resulting from secondary task engagement is a major contributing factor to teenage drivers' crash risk. This study aims to determine the extent to which visual inattention while engaging in distracting secondary tasks contributes to teenage drivers' crash risk. METHODS: Real-world driving data were collected from a cohort of 82 newly licensed teenagers (average age 16.48 years, SD=0.33) recruited in Virginia. Participants' private vehicles were equipped with data acquisition systems that documented driving kinematics and miles driven, and made video recordings of the driver and driving environment. Data were collected from 2010 to 2014 and analyzed in 2017. The analysis of secondary task engagement was based on 6-second video segments from both crash and random samples of normal driving. RESULTS: Of a wide range of secondary tasks, only manual cellphone use (OR=2.7, 95% CI=1.1, 6.8) and reaching/handling objects while driving (OR=6.9, 95% CI=2.6, 18.6) were associated with increased crash risk. Drivers' duration of eyes off the road accounted for 41% of the crash risk associated with manual cellphone use and 10% of the risk associated with reaching/handling objects while driving. CONCLUSIONS: Secondary tasks vary in the risk they introduce to the teenage driver. Manual cellphone use and reaching for objects were found to be associated with increased crash risk. These findings objectively quantify the effect of visual inattention resulting from distracting secondary tasks on teenage drivers' crash risk. Teenage drivers may benefit from technologic and behavioral interventions that will keep their eyes on the road at all times and discourage engagement in distracting secondary tasks.

Crash Risk and Risky Driving Behavior Among Adolescents During Learner and Independent Driving Periods.

PURPOSE: Novice adolescents' crash rates are highly elevated early in licensure, despite substantial practicedriving during the learner period. The objectives of this study were to examine the variability in measures of driving risk among adolescents during the learner and early independent driving periods and evaluate how risk varies by driving experience, gender, time of day, and road surface conditions. METHODS: Objective driving data were collected in a naturalistic cohort study of 90 adolescent drivers with learner driving permit and 131 experienced adult drivers. Participants' private vehicles were equipped with data acquisition system documenting driving kinematics, miles driven, and video recordings of the driver and the driving environment. Crash/near-crash (CNC) and kinematic risky driving (KRD) rates were calculated during the learner and early independent driving periods by gender (female/male), time of day (day/night), and road surface conditions (wet/dry) for adolescents and adults. RESULTS: CNC and KRD rates of adolescents were similar to adult drivers during the learner period (CNC: incident rate ratio [IRR] = 1.67, confidence interval [CI] = .98-2.82 and KRD: IRR = 1.04, CI = .78-1.40, respectively), but dramatically higher in the first year of independent driving (CNC: IRR = 6.51, CI = 4.03-10.51 and KRD: IRR = 3.95, CI = 2.96-5.26, respectively), and particularly elevated the first 3months of licensure. Adolescent KRD rates were higher for males than females and invariably higher than adult rates during day and night, wet and dry conditions. CONCLUSIONS: While the learner driving period was relatively safe for adolescents, the transition to independent driving was typified by a dramatic increase in risk among adolescents that was higher than adult rates overall and under varying driving conditions.

Vehicle ownership and other predictors of teenagers risky driving behavior: Evidence from a naturalistic driving study.

OBJECTIVE: Risky driving behavior may contribute to the high crash risk among teenage drivers. The current naturalistic driving study assessed predictors for teenagers' kinematic risky driving (KRD) behavior and the interdependencies between them. METHOD: The private vehicles of 81 novice teenage drivers were equipped with data acquisition system that recorded driving kinematics, miles driven, and video recordings of the driver, passengers and the driving environment. Psychosocial measures were collected using questionnaires administered at licensure. Poisson regression analyses and model selection were used to assess factors associated with teens' risky driving behavior and the interactions between them. RESULTS: Driving own vs shared vehicle, driving during the day vs at night, and driving alone vs with passengers were significantly associated with higher KRD rates (Incidence rate ratios (IRRs) of 1.60, 1.41, and 1.28, respectively). Teenagers reporting higher vs lower levels of parental trust had significantly lower KRD rates (IRR = 0.58). KRD rates were 88% higher among teenagers driving with a passenger in their own vehicle compared to teenagers driving with a passenger in a shared vehicle. Similarly, KRD rates during the day were 74% higher among teenagers driving their own vehicle compared to those driving a shared vehicle. CONCLUSIONS: Novice teenagers' risky driving behavior varied according to driver attributes and contextual aspects of the driving environment. As such, examining teenagers' risky driving behavior should take into account multiple contributing factors and their interactions. The variability in risky driving according to the driving context can inform the development of targeted interventions to reduce the crash risk of novice teenage drivers.

U.S. Adolescent Street Racing and Other Risky Driving Behaviors.

PURPOSE: We examined demographic characteristics and risky driving behaviors associated with street racing among adolescents in the NEXT Generation Health Study (N = 2,395). METHOD: Binomial logistic regression tested associations between demographics and driving in a street race (DSR) or being a passenger in a street race (PSR). Sequential logistic regression tested the robustness of the association between DSR and crashes. RESULTS: Hispanic/Latino, non-Hispanic Black/African-American, and mixed-race participants were more likely to engage in DSR. Males were more likely and teens with moderate socioeconomic status were less likely to engage in DSR and PSR. DSR was associated with other risky driving behaviors in bivariate models but was not independently associated with crashes after sequential modeling. CONCLUSIONS: Among adolescents, those who are male, racial/ethnic minorities, or low socioeconomic status may be at higher risk of DSR. However, overall driving risk might explain the association between DSR engagement and higher crash risk.

Teens' distracted driving behavior: Prevalence and predictors.

INTRODUCTION: Teen drivers' over-involvement in crashes has been attributed to a variety of factors, including distracted driving. With the rapid development of in-vehicle systems and portable electronic devices, the burden associated with distracted driving is expected to increase. The current study identifies predictors of secondary task engagement among teenage drivers and provides basis for interventions to reduce distracted driving behavior. We described the prevalence of secondary tasks by type and driving conditions and evaluated the associations between the prevalence of secondary task engagement, driving conditions, and selected psychosocial factors. METHODS: The private vehicles of 83 newly-licensed teenage drivers were equipped with Data Acquisition Systems (DAS), which documented driving performance measures, including secondary task engagement and driving environment characteristics. Surveys administered at licensure provided psychosocial measures. RESULTS: Overall, teens engaged in a potentially distracting secondary task in 58% of sampled road clips. The most prevalent types of secondary tasks were interaction with a passenger, talking/singing (no passenger), external distraction, and texting/dialing the cell phone. Secondary task engagement was more prevalent among those with primary vehicle access and when driving alone. Social norms, friends' risky driving behaviors, and parental limitations were significantly associated with secondary task prevalence. In contrast, environmental attributes, including lighting and road surface conditions, were not associated with teens' engagement in secondary tasks. CONCLUSIONS: Our findings indicated that teens engaged in secondary tasks frequently and poorly regulate their driving behavior relative to environmental conditions. Practical applications: Peer and parent influences on secondary task engagement provide valuable objectives for countermeasures to reduce distracted driving among teenage drivers.

Factors Influencing Learner Permit Duration.

An increasing number of countries are requiring an extended learner permit prior to independent driving. The question of when drivers begin the learner permit period, and how long they hold the permit before advancing to independent licensure has received little research attention. Licensure timing is likely to be related to "push" and "pull" factors which may encourage or inhibit the process. To examine this question, we recruited a sample of 90 novice drivers (49 females and 41 males, average age of 15.6 years) soon after they obtained a learner permit and instrumented their vehicles to collect a range of driving data. Participants completed a series of surveys at recruitment related to factors that may influence licensure timing. Two distinct findings emerged from the time-to-event analysis that tested these push and pull factors in relation to licensure timing. The first can be conceptualized as teens' motivation to drive (push), reflected in a younger age when obtaining a learner permit and extensive pre-permit driving experience. The second finding was teens' perceptions of their parents' knowledge of their activities (pull); a proxy for a parents' attentiveness to their teens' lives. Teens who reported higher levels of their parents' knowledge of their activities took longer to advance to independent driving. These findings suggest time-to-licensure may be related to teens' internal motivation to drive, and the ability of parents to facilitate or impede early licensure.

Visual, tangible, and touch-screen: Comparison of platforms for displaying simple graphics.

Four different platforms were compared in a task of exploring an angular stimulus and reporting its value. The angle was explored visually, tangibly as raised fine-grit sandpaper, or on a touch-screen with a frictional or vibratory signal. All platforms produced highly accurate angle judgments. Differences were found, however, in exploration time, with vision fastest as expected, followed by tangible, vibration, and friction. Relative to the tangible display, touch-screens evidenced greater noise in the perceived angular value, with a particular disadvantage for friction. The latter must be interpreted in the context of a first-generation display and a rapidly advancing technology. On the whole, the results point both to promise and barriers in the use of refreshable graphical displays for blind users.