Driver crash risk factors and prevalence evaluation using naturalistic driving data.

The accurate evaluation of crash causal factors can provide fundamental information for effective transportation policy, vehicle design, and driver education. Naturalistic driving (ND) data collected with multiple onboard video cameras and sensors provide a unique opportunity to evaluate risk factors during the seconds leading up to a crash. This paper uses a National Academy of Sciences-sponsored ND dataset comprising 905 injurious and property damage crash events, the magnitude of which allows the first direct analysis (to our knowledge) of causal factors using crashes only. The results show that crash causation has shifted dramatically in recent years, with driver-related factors (i.e., error, impairment, fatigue, and distraction) present in almost 90% of crashes. The results also definitively show that distraction is detrimental to driver safety, with handheld electronic devices having high use rates and risk.

Distracted driving and risk of road crashes among novice and experienced drivers.

BACKGROUND: Distracted driving attributable to the performance of secondary tasks is a major cause of motor vehicle crashes both among teenagers who are novice drivers and among adults who are experienced drivers. METHODS: We conducted two studies on the relationship between the performance of secondary tasks, including cell-phone use, and the risk of crashes and near-crashes. To facilitate objective assessment, accelerometers, cameras, global positioning systems, and other sensors were installed in the vehicles of 42 newly licensed drivers (16.3 to 17.0 years of age) and 109 adults with more driving experience. RESULTS: During the study periods, 167 crashes and near-crashes among novice drivers and 518 crashes and near-crashes among experienced drivers were identified. The risk of a crash or near-crash among novice drivers increased significantly if they were dialing a cell phone (odds ratio, 8.32; 95% confidence interval [CI], 2.83 to 24.42), reaching for a cell phone (odds ratio, 7.05; 95% CI, 2.64 to 18.83), sending or receiving text messages (odds ratio, 3.87; 95% CI, 1.62 to 9.25), reaching for an object other than a cell phone (odds ratio, 8.00; 95% CI, 3.67 to 17.50), looking at a roadside object (odds ratio, 3.90; 95% CI, 1.72 to 8.81), or eating (odds ratio, 2.99; 95% CI, 1.30 to 6.91). Among experienced drivers, dialing a cell phone was associated with a significantly increased risk of a crash or near-crash (odds ratio, 2.49; 95% CI, 1.38 to 4.54); the risk associated with texting or accessing the Internet was not assessed in this population. The prevalence of high-risk attention to secondary tasks increased over time among novice drivers but not among experienced drivers. CONCLUSIONS: The risk of a crash or near-crash among novice drivers increased with the performance of many secondary tasks, including texting and dialing cell phones. (Funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Highway Traffic Safety Administration.).

Variability in crash and near-crash risk among novice teenage drivers: a naturalistic study.

OBJECTIVE: Using video monitoring technologies, we investigated teenage driving risk variation during the first 18 months of independent driving. STUDY DESIGN: Driving data were collected on 42 teenagers whose vehicles were instrumented with sophisticated video and data recording devices. Surveys on demographic and personality characteristics were administered at baseline. Drivers were classified into 3 risk groups using a K-mean clustering method based on crash and near-crash (CNC) rate. The change in CNC rates over time was evaluated by mixed-effect Poisson models. RESULTS: Compared with the first 3 months after licensure (first quarter), the CNC rate for participants during the third, fourth, and fifth quarters decreased significantly to 59%, 62%, and 48%, respectively. Three distinct risk groups were identified with CNC rates of 21.8 (high-risk), 8.3 (moderate-risk), and 2.1 (low-risk) per 10 000 km traveled. High- and low-risk drivers showed no significant change in CNC rates throughout the 18-month study period. CNC rates for moderate-risk drivers decreased substantially from 8.8 per 10 000 km in the first quarter to 0.8 and 3.2 in the fourth and fifth quarters, respectively. The 3 groups were not distinguishable with respect to personality characteristics. CONCLUSION: Teenage CNC rates varied substantially, with distinct high-, moderate-, and low-risk groups. Risk declined over time only in the moderate-risk group. The high-risk drivers appeared to be insensitive to experience, with CNC rates consistently high throughout the 18-month study period, and the moderate-risk group appeared to learn from experience.

Crash and risky driving involvement among novice adolescent drivers and their parents.

OBJECTIVES: We compared rates of risky driving among novice adolescent and adult drivers over the first 18 months of adolescents' licensure. METHODS: Data-recording systems installed in participants' vehicles provided information on driving performance of 42 newly licensed adolescent drivers and their parents. We analyzed crashes and near crashes and elevated g-force event rates by Poisson regression with random effects. RESULTS: During the study period, adolescents were involved in 279 crashes or near crashes (1 involving injury); parents had 34 such accidents. The incidence rate ratio (IRR) comparing adolescent and parent crash and near-crash rates was 3.91. Among adolescent drivers, elevated rates of g-force events correlated with crashes and near crashes (r = 0.60; P < .001). The IRR comparing incident rates of risky driving among adolescents and parents was 5.08. Adolescents' rates of crashes and near crashes declined with time (with a significant uptick in the last quarter), but elevated g-force event rates did not decline. CONCLUSIONS: Elevated g-force events among adolescents may have contributed to crash and near-crash rates that remained much higher than adult levels after 18 months of driving.

Investigating the impact of driving automation systems on distracted driving behaviors.

Driving automation systems (e.g., SAE Level 2) ultimately aim to enhance the comfort and safety of drivers. At present, these systems are able to control some portions of the driving task (e.g., braking, steering) for extended time periods, giving drivers the opportunity to disengage from the responsibilities associated with driving. In this study, data derived from two naturalistic driving studies involving automation-equipped vehicles were analyzed to evaluate driver behaviors with respect to driving automation system use, specifically distraction-related factors (i.e., secondary task engagement, eye-glance behavior, and drowsiness). The results indicate that when drivers had prior experience using driving automation systems, they were almost two times as likely to participate in distracted driving behaviors when the systems were active than during manual driving. Drivers with less experience and familiarity with driving automation systems were less likely to drive distracted when the systems were active; however, these drivers tended to be somewhat drowsy when driving with systems activated. The results provide important insights into different operational phases of driving automation system use (i.e., learning/unfamiliar vs experienced users), whereby experience results in overtrust and overreliance on the advanced technologies, which subsequently may negate some of the safety benefits of these systems. Thus, while the safety benefits of driving automation systems are evident, it is imperative to better understand the impact these advanced technologies may have on driver behavior and performance in order to evaluate and address any unintended consequences associated with system use.

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.

Parent and Teen Factors Associated with the Amount and Variety of Supervised Practice Driving.

Despite the fact that a minimum number of practice hours for novice drivers has been widely adopted in the U.S., Australia, NZ and in some European countries, surprisingly little is known about the amount or variety of driving during the learner stage. One factor may be due to the difficulty in obtaining objective measures of the amount and conditions during which practice driving takes place. The purpose of this study was to use objective measures of the amount and variety of practice driving occurring during the learner permit stage, and examine how these differ according to individual and household level characteristics, which were measured at baseline using parent and teen surveys. We found that increased practice was associated with parenting practices, such as parent trust, as well as household income and pre-permit driving experience. Taken collectively, the findings suggest the amount of practice driving may be a function of the motivation or interest of the teen to drive, combined with the quality of the relationship between parents and teens and the household environment within which these characteristics are occurring. Surprisingly, in this study, teens from households with lower incomes gained more practice hours and days. Population level studies examining the association between the family context and how teenagers learn to drive are needed to confirm the findings from this study in a representative sample. The use of both naturalistic and survey methods used in this study demonstrates how they can build on one another.

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.

A method for evaluating collision avoidance systems using naturalistic driving data.

This paper describes a method for use in evaluating the performance of collision avoidance systems (CASs) using naturalistic driving data collected during real crashes and near-crashes. The method avoids evaluation of algorithms against specific assumptions of reaction times or response inputs. It minimizes interpretation of the involved driver's perception and response levels which permits generalizing findings beyond the performance of the involved driver. The method involves four parts: input of naturalistic crash data into alert models to determine when alerts would occur, kinematic analysis to determine when different responses would be required to avoid collision, translation of the time available into an estimate of the percentage of the population able to avoid the specific event, and an evaluation of the frequency of alerts that would be generated by the CASs. The method permits comparison of CAS performance and provides guidance for CAS development. The method is described primarily in the context of Forward Collision Warning CASs, but is applicable to other CAS types.

DETECTION OF ROAD HAZARDS BY NOVICE TEEN AND EXPERIENCED ADULT DRIVERS.

Previous laboratory and simulator research has indicated that hazard detection skills and abilities are less developed among novice drivers compared with experienced adult drivers. Novices tend to miss some relevant cues and may be less able to process important elements in the environment while driving. As was found with other research methods, it was hypothesized that novices would have lower hazard detection skills and will react less appropriately to hazards than older and more experienced drivers.Three hazard perception scenarios were simulated on a test track and data were collected on newly licensed teen drivers (within 2 weeks of licensure) and a comparison group of adults. The scenarios included a hidden stop sign, hidden pedestrian, and hidden pedestrian with lane closure (this last included a text-messaging task). Discrete quantitative performance metrics were evaluated for this analysis, including: 1) Did the participant glance at the potential hazard (e.g., stop sign, pedestrian)? 2) Did the participant stop (for the stop sign scenario)? 3) Did the participant show signs of indecision, caution, or awareness (for all hazards)?Significant differences between teen drivers and more experienced adult drivers were found in a combined hazard detection analysis. Results indicate that the adult drivers observed hazards and demonstrated overt recognition of hazards more frequently than the teen drivers. Results indicated that a large portion of teen drivers failed to disengage from peripheral task engagement in the presence of hazards. The results will later be compared to naturalistic data for the same set of drivers to see whether these test track results are predictive of real-world behavior.

The driver-level crash risk associated with daily cellphone use and cellphone use while driving.

This study examined the overall prevalence of cellphone use, including the rates of calls and texts both per day and hourly while driving, and assessed whether or not individual crash risk was correlated with cellphone use. The study used data from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS), which had more than 3500 participants who provided up to three years of driving data. Of these participants, 620 provided cellphone records, 564 of which included both call and text records. The prevalence of cellphone calls and texts per day was calculated. By overlaying the cellphone records with the SHRP 2 NDS data, we also evaluated the rates of calls and texts while driving by driver demographics. Crashes for these cellphone-using participants were also identified from the SHRP 2 NDS data. Negative binomial regression models were used to determine whether the crash rate was associated with cellphone use. Participants made an average of 27.1 texts and 7.3 calls per day. They averaged 1.6 texts and 1.2 calls per hour of driving. Cellphone use varied significantly by age, especially for texting. The texting rate for drivers aged 16-19 was 59.4 per day and 2.9 per hour of driving, four times higher than the 14.3 per day and 1.0 per hour for drivers 30-64 years old. The texting rate for drivers 20-29 years old was also high at 42.4 per day and 2.6 per hour of driving. Participants experienced 243 crashes in 216,231 h of driving. It was found that those who texted more often per day or per hour of driving had higher crash rates after adjusting for age and gender effects. The severe crash rate increases 0.58% for every additional text per day and all 8.3% for every text per hour of driving; overall crash rate increases 0.41% for every additional text per day and 6.46% for every text per hour of driving. The results show that cellphone texting and calling are quite common while driving. The texting rate for young drivers is substantially higher than for middle-aged and senior drivers. This study confirmed that those who text at a higher rate are associated with a higher crash risk.

The sleep of commercial vehicle drivers under the 2003 hours-of-service regulations.

Previous research has found that commercial drivers get an average of 5.18 h of sleep per night. The revised hours-of-service (HOS) regulations (in the United States) are in place to provide drivers with more opportunities to get sleep. However, are drivers really getting more sleep under these new regulations? Also, is there a relationship between sleep quantity and involvement in critical incident (crashes, near-crashes, or crash-relevant conflicts)? Data from 73 truck drivers, collected during a naturalistic driving study after the implementation of the 2003 HOS regulations, were analyzed to determine overall sleep quantity (using actigraphy), along with sleep quantity prior to being involved in a critical incident. Sixty-two drivers had at least seven consecutive days (Monday through Sunday) of reliable actigraphy data; mean sleep quantity per 24-h period (midnight centered using the Cole-Kripke algorithm) for these drivers was 6.28 h (S.D.=1.42 h). Fifty-eight critical incidents were recorded in the 10th and 11th driving hours. Analysis results indicated that drivers received significantly less sleep in the period prior to a critical incident as compared to their mean overall sleep quantity. The results of this study indicate drivers may be getting more sleep under the revised 2003 HOS regulations as compared to the old regulations. In addition, significantly less sleep in the 24-h period prior to involvement in a critical incident suggests driver fatigue may have been a potential contributing factor in these critical incidents.

Performance of basic kinematic thresholds in the identification of crash and near-crash events within naturalistic driving data.

Understanding causal factors for traffic safety-critical events (e.g., crashes and near-crashes) is an important step in reducing their frequency and severity. Naturalistic driving data offers unparalleled insight into these factors, but requires identification of situations where crashes are present within large volumes of data. Sensitivity and specificity of these identification approaches are key to minimizing the resources required to validate candidate crash events. This investigation used data from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) and the Canada Naturalistic Driving Study (CNDS) to develop and validate different kinematic thresholds that can be used to detect crash events. Results indicate that the sensitivity of many of these approaches can be quite low, but can be improved by selecting particular threshold levels based on detection performance. Additional improvements in these approaches are possible, and may involve leveraging combinations of different detection approaches, including advanced statistical techniques and artificial intelligence approaches, additional parameter modifications, and automation of validation processes.

A validation of the low mileage bias using naturalistic driving study data.

INTRODUCTION: This paper evaluated the low mileage bias (LMB) phenomenon for senior drivers using data mined from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study. Supporters of the LMB construct postulate that it is only those seniors who drive the lowest annual mileage who are primarily responsible for the increased crash rates traditionally attributed to this population in general. METHOD: The current analysis included 802 participants, all aged 65 or older who were involved in 163 property damage and injury crashes, and deemed to be at-fault in 123 (75%) of those instances. Poisson regression models were used to evaluate the association between annualized mileage driven and crash risk. RESULTS: Results show that the crash rate for drivers with lower annualized mileage (i.e., especially for those driving fewer than approximately 3000miles per year) was significantly higher than that of drivers with higher annualized mileage, and that 25% of the overall sample were low- mileage drivers according to this criterion. Data were also evaluated by gender and meta-age group (i.e., younger-old: 65-74 and older-old: 75-99), and the results were consistent across these sub-groups. CONCLUSIONS: This study provides strong support for the existence of the LMB. PRACTICAL APPLICATIONS: These results can help to reshape how transportation safety stakeholders view senior drivers in general and help them to focus their efforts on those seniors most in need of either risk-reducing countermeasures or alternative means of transportation.

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.

The effects of age on crash risk associated with driver distraction.

Background: Driver distraction is a major contributing factor to crashes, which are the leading cause of death for the US population under 35 years of age. The prevalence of secondary-task engagement and its impacts on distraction and crashes may vary substantially by driver age. Methods: Driving performance and behaviour data were collected continuously using multiple cameras and sensors in situ for 3542 participant drivers recruited for up to 3 years for the Second Strategic Highway Research Program Naturalistic Driving Study. Secondary-task engagement at the onset of crashes and during normal driving segments was identified from videos. A case-cohort approach was used to estimate the crash odds ratios associated with, and the prevalence of, secondary tasks for four age groups: 16-20, 21-29, 30-64 and 65-98 years of age. Only severe crashes (property damage and higher severity) were included in the analysis. Results: Secondary-task-induced distraction posed a consistently higher threat for drivers younger than 30 and above 65 when compared with middle-aged drivers, although senior drivers engaged in secondary tasks much less frequently than their younger counterparts. Secondary tasks with high visual-manual demand (e.g. visual-manual tasks performed on cell phones) affected drivers of all ages. Certain secondary tasks, such as operation of in-vehicle devices and talking/singing, increased the risk for only certain age groups. Conclusions: Teenaged, young adult drivers and senior drivers are more adversely impacted by secondary-task engagement than middle-aged drivers. Visual-manual distractions impact drivers of all ages, whereas cognitive distraction may have a larger impact on young drivers.

The impact of secondary task cognitive processing demand on driving performance.

Crash causation research has identified inattention as a major source of driver error leading to crashes. The series of experiments presented herein investigate the characteristics of an in-vehicle information system (IVIS) task that could hinder driving performance due to uncertainty buildup and cognitive capture. Three on-road studies were performed that used instrumented passenger and tractor-trailer vehicles to obtain real-world driving performance data. Participants included young, middle-aged, and older passenger vehicle drivers and middle-aged and older commercial vehicle operators. While driving, they were presented with IVIS tasks with various information densities, decision-making elements, presentation formats, and presentation modalities (visual or auditory). The experiments showed that, for both presentation modalities, the presence of multiple decision-making elements in a task had a substantial negative impact on driving performance of both automobile drivers and truck drivers when compared to conventional tasks or tasks with only one decision-making element. The results from these experiments can be used to improve IVIS designs, allowing for potential IVIS task phenomena such as uncertainty buildup and cognitive capture to be avoided.

The development of a naturalistic data collection system to perform critical incident analysis: an investigation of safety and fatigue issues in long-haul trucking.

Traditionally, both epidemiological and empirical methods have been used to assess driving safety. This paper describes an alternative, hybrid, naturalistic approach to data collection that shares advantages with each traditional approach. Though this naturalistic approach draws on elements of several safety techniques that have been developed in the past, including the Hazard Analysis Technique, instrumented vehicle studies, and fleet studies of driving safety interventions, it has a number of unique elements. Sophisticated instrumented vehicles collected over 400,000 km of commercial vehicle data to address the long-haul trucking application described in this paper. The development of this data collection and analysis method and data collection instrumentation has resulted in a set of valuable tools to advance the current state-of-the-practice in driving safety assessment. An application of this unique approach to a study of long-haul truck driver performance, behavior, and fatigue is described herein.

Red-light running violation prediction using observational and simulator data.

In the United States, 683 people were killed and an estimated 133,000 were injured in crashes due to running red lights in 2012. To help prevent/mitigate crashes caused by running red lights, these violations need to be identified before they occur, so both the road users (i.e., drivers, pedestrians, etc.) in potential danger and the infrastructure can be notified and actions can be taken accordingly. Two different data sets were used to assess the feasibility of developing red-light running (RLR) violation prediction models: (1) observational data and (2) driver simulator data. Both data sets included common factors, such as time to intersection (TTI), distance to intersection (DTI), and velocity at the onset of the yellow indication. However, the observational data set provided additional factors that the simulator data set did not, and vice versa. The observational data included vehicle information (e.g., speed, acceleration, etc.) for several different time frames. For each vehicle approaching an intersection in the observational data set, required data were extracted from several time frames as the vehicle drew closer to the intersection. However, since the observational data were inherently anonymous, driver factors such as age and gender were unavailable in the observational data set. Conversely, the simulator data set contained age and gender. In addition, the simulator data included a secondary (non-driving) task factor and a treatment factor (i.e., incoming/outgoing calls while driving). The simulator data only included vehicle information for certain time frames (e.g., yellow onset); the data did not provide vehicle information for several different time frames while vehicles were approaching an intersection. In this study, the random forest (RF) machine-learning technique was adopted to develop RLR violation prediction models. Factor importance was obtained for different models and different data sets to show how differently the factors influence the performance of each model. A sensitivity analysis showed that the factor importance to identify RLR violations changed when data from different time frames were used to develop the prediction models. TTI, DTI, the required deceleration parameter (RDP), and velocity at the onset of a yellow indication were among the most important factors identified by both models constructed using observational data and simulator data. Furthermore, in addition to the factors obtained from a point in time (i.e., yellow onset), valuable information suitable for RLR violation prediction was obtained from defined monitoring periods. It was found that period lengths of 2-6m contributed to the best model performance.

Naturalistic teenage driving study: Findings and lessons learned.

INTRODUCTION: This paper summarizes the findings on novice teenage driving outcomes (e.g., crashes and risky driving behaviors) from the Naturalistic Teenage Driving Study. METHOD: Survey and driving data from a data acquisition system (global positioning system, accelerometers, cameras) were collected from 42 newly licensed teenage drivers and their parents during the first 18 months of teenage licensure; stress responsivity was also measured in teenagers. RESULT: Overall teenage crash and near-crash (CNC) rates declined over time, but were >4 times higher among teenagers than adults. Contributing factors to teenage CNC rates included secondary task engagement (e.g., distraction), kinematic risky driving, low stress responsivity, and risky social norms. CONCLUSIONS: The data support the contention that the high novice teenage CNC risk is due both to inexperience and risky driving behavior, particularly kinematic risky driving and secondary task engagement. PRACTICAL APPLICATIONS: Graduated driver licensing policy and other prevention efforts should focus on kinematic risky driving, secondary task engagement, and risky social norms.