Naturalistic drive cycle synthesis for pickup trucks

ProblemFuture pick-up trucks are meeting much stricter fuel economy and exhaust emission standards. Design tradeoffs will have to be carefully evaluated to satisfy consumer expectations within the regulatory and cost constraints. Boundary conditions will obviously be critical for decision making: thus, the understanding of how customers are driving in naturalistic settings is indispensable. Federal driving schedules, while critical for certification, do not capture the richness of naturalistic cycles, particularly the aggressive maneuvers that often shape consumer perception of performance. While there are databases with large number of drive cycles, applying all of them directly in the design process is impractical. Therefore, representative drive cycles that capture the essence of the naturalistic driving should be synthesized from naturalistic driving data.MethodNaturalistic drive cycles are firstly categorized by investigating their micro-trip components, defined as driving activities between successive stops. Micro-trips are expected to characterize underlying local traffic conditions, and separate different driving patterns. Next, the transitions from one vehicle state to another vehicle state in each cycle category are captured with Transition Probability Matrix (TPM). Candidate drive cycles can subsequently be synthesized using Markov Chain based on TPMs for each category. Finally, representative synthetic drive cycles are selected through assessment of significant cycle metrics to identify the ones with smallest errors.SummaryThis paper provides a framework for synthesis of representative drive cycles from naturalistic driving data, which can subsequently be used for efficient optimization of design or control of pick-up truck powertrains.Impact on industryManufacturers will benefit from representative drive cycles in several aspects, including quick assessments of vehicle performance and energy consumption in simulations, component sizing and design, optimization of control strategies, and vehicle testing under real-world conditions. This is in contrast to using federal certification test cycles, which were never intended to capture pickup truck segment.     

Trends in teen driver licensure,driving patterns and crash involvement in the United States, 2006–2015

IntroductionThe Monitoring the Future (MTF) survey provides nationally-representative annual estimates of licensure and driving patterns among U.S. teens. A previous study using MTF data reported substantial declines in the proportion of high school seniors that were licensed to drive and increases in the proportion of nondrivers following the recent U.S. economic recession.MethodTo explore whether licensure and driving patterns among U.S. high school seniors have rebounded in the post-recession years, we analyzed MTF licensure and driving data for the decade of 2006–2015. We also examined trends in teen driver involvement in fatal and nonfatal injury crashes for that decade using data from the Fatality Analysis Reporting System and National Automotive Sampling System General Estimates System, respectively.ResultsDuring 2006–2015, the proportion of high school seniors that reported having a driver's license declined by 9 percentage points (11%) from 81% to 72% and the proportion that did not drive during an average week increased by 8 percentage points (44%) from 18% to 26%. The annual proportion of black seniors that did not drive was consistently greater than twice the proportion of nondriving white seniors. Overall during the decade, 17- and 18-year-old drivers experienced large declines in fatal and nonfatal injury crashes, although crashes increased in both 2014 and 2015.ConclusionsThe MTF data indicate that licensure and driving patterns among U.S. high school seniors have not rebounded since the economic recession. The recession had marked negative effects on teen employment opportunities, which likely influenced teen driving patterns. Possible explanations for the apparent discrepancies between the MTF data and the 2014 and 2015 increases in crashes are explored.Practical applicationsMTF will continue to be an important resource for clarifying teen driving trends in relation to crash trends and informing strategies to improve teen driver safety.     

Crashes and near-crashes on horizontal curves along rural two-lane highways: Analysis of naturalistic driving data

IntroductionPrior research has shown the probability of a crash occurring on horizontal curves to be significantly higher than on similar tangent segments, and a disproportionally higher number of curve-related crashes occurred in rural areas. Challenges arise when analyzing the safety of horizontal curves due to imprecision in integrating information as to the temporal and spatial characteristics of each crash with specific curves.MethodsThe second Strategic Highway Research Program(SHRP 2) conducted a large-scale naturalistic driving study (NDS),which provides a unique opportunity to better understand the contributing factors leading to crash or near-crash events. This study utilizes high-resolution behavioral data from the NDS to identify factors associated with 108 safety critical events (i.e., crashes or near-crashes) on rural two-lane curves. A case-control approach is utilized wherein these events are compared to 216 normal, baseline-driving events. The variables examined in this study include driver demographic characteristics, details of the traffic environment and roadway geometry, as well as driver behaviors such as in-vehicle distractions.ResultsLogistic regression models are estimated to discern those factors affecting the likelihood of a driver being crash-involved. These factors include high-risk behaviors, such as speeding and visual distractions, as well as curve design elements and other roadway characteristics such as pavement surface conditions.ConclusionsThis paper successfully integrated driver behavior, vehicle characteristics, and roadway environments into the same model. Logistic regression model was found to be an effective way to investigate crash risks using naturalistic driving data.Practical ApplicationsThis paper revealed a number of contributing factors to crashes on rural two-lane curves, which has important implications in traffic safety policy and curve geometry design. This paper also discussed limitations and lessons learned from working with the SHRP 2 NDS data. It will benefit future researchers who work with similar type of data.     

Identification of aggressive driving from naturalistic data in car-following situations

Introduction: Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. The objective of this study is to investigate the possibility of identifying aggressive driving in car-following situations on motorways by simple jerk metrics derived from naturalistic data. Method: We investigate two jerk metrics, one for large positive jerk and the other for large negative jerk, when drivers are operating the gas and brake pedal, respectively. Results: The results obtained from naturalistic data from five countries in Europe show that the drivers from different countries have a significantly different number of large positive and large negative jerks. Male drivers operate the vehicle with significantly larger number of negative jerks compared to female drivers. The validation of the jerk metrics in identifying aggressive driving is performed by tailgating (following a leading vehicle in a close proximity) and by a violator/non-violator categorization derived from self-reported questionnaires. Our study shows that the identification of aggressive driving could be reinforced by the number of large negative jerks, given that the drivers are tailgating, or by the number of large positive jerks, given that the drivers are categorized as violators. Practical applications: The possibility of understanding, classifying, and quantifying aggressive driving behavior and driving styles with higher risk for accidents can be used for the development of driver support and coaching programs that promote driver safety and are enabled by the vast collection of driving data from modern in-vehicle monitoring and smartphone technology.     

Assessing drivers' response during automated driver support system failures with non-driving tasks

IntroductionWith the increase in automated driver support systems, drivers are shifting from operating their vehicles to supervising their automation. As a result, it is important to understand how drivers interact with these automated systems and evaluate their effect on driver responses to safety critical events. This study aimed to identify how drivers responded when experiencing a safety critical event in automated vehicles while also engaged in non-driving tasks.MethodIn total 48 participants were included in this driving simulator study with two levels of automated driving: (a) driving with no automation and (b) driving with adaptive cruise control (ACC) and lane keeping (LK) systems engaged; and also two levels of a non-driving task (a) watching a movie or (b) no non-driving task. In addition to driving performance measures, non-driving task performance and the mean glance duration for the non-driving task were compared between the two levels of automated driving.ResultsDrivers using the automated systems responded worse than those manually driving in terms of reaction time, lane departure duration, and maximum steering wheel angle to an induced lane departure event. These results also found that non-driving tasks further impaired driver responses to a safety critical event in the automated system condition.ConclusionIn the automated driving condition, driver responses to the safety critical events were slower, especially when engaged in a non-driving task.Practical applicationTraditional driver performance variables may not necessarily effectively and accurately evaluate driver responses to events when supervising autonomous vehicle systems. Thus, it is important to develop and use appropriate variables to quantify drivers' performance under these conditions.     

Creation of the Naturalistic Engagement in Secondary Tasks (NEST) distracted driving dataset

ProblemDistracted driving has become a topic of critical importance to driving safety research over the past several decades. Naturalistic driving data offer a unique opportunity to study how drivers engage with secondary tasks in real-world driving; however, the complexities involved with identifying and coding relevant epochs of naturalistic data have limited its accessibility to the general research community.MethodThis project was developed to help address this problem by creating an accessible dataset of driver behavior and situational factors observed during distraction-related safety-critical events and baseline driving epochs, using the Strategic Highway Research Program 2 (SHRP2) naturalistic dataset. The new NEST (Naturalistic Engagement in Secondary Tasks) dataset was created using crashes and near-crashes from the SHRP2 dataset that were identified as including secondary task engagement as a potential contributing factor. Data coding included frame-by-frame video analysis of secondary task and hands-on-wheel activity, as well as summary event information. In addition, information about each secondary task engagement within the trip prior to the crash/near-crash was coded at a higher level. Data were also coded for four baseline epochs and trips per safety-critical event.Results1,180 events and baseline epochs were coded, and a dataset was constructed. The project team is currently working to determine the most useful way to allow broad public access to the dataset.DiscussionWe anticipate that the NEST dataset will be extraordinarily useful in allowing qualified researchers access to timely, real-world data concerning how drivers interact with secondary tasks during safety-critical events and baseline driving.Practical applicationsThe coded dataset developed for this project will allow future researchers to have access to detailed data on driver secondary task engagement in the real world. It will be useful for standalone research, as well as for integration with additional SHRP2 data to enable the conduct of more complex research.     

Driver Behavior During Overtaking Maneuvers from the 100-Car Naturalistic Driving Study

Objective: Lane changes with the intention to overtake the vehicle in front are especially challenging scenarios for forward collision warning (FCW) designs. These overtaking maneuvers can occur at high relative vehicle speeds and often involve no brake and/or turn signal application. Therefore, overtaking presents the potential of erroneously triggering the FCW. A better understanding of driver behavior during lane change events can improve designs of this human–machine interface and increase driver acceptance of FCW. The objective of this study was to aid FCW design by characterizing driver behavior during lane change events using naturalistic driving study data.

Methods: The analysis was based on data from the 100-Car Naturalistic Driving Study, collected by the Virginia Tech Transportation Institute. The 100-Car study contains approximately 1.2 million vehicle miles of driving and 43,000 h of data collected from 108 primary drivers. In order to identify overtaking maneuvers from a large sample of driving data, an algorithm to automatically identify overtaking events was developed. The lead vehicle and minimum time to collision (TTC) at the start of lane change events was identified using radar processing techniques developed in a previous study. The lane change identification algorithm was validated against video analysis, which manually identified 1,425 lane change events from approximately 126 full trips.

Results: Forty-five drivers with valid time series data were selected from the 100-Car study. From the sample of drivers, our algorithm identified 326,238 lane change events. A total of 90,639 lane change events were found to involve a closing lead vehicle. Lane change events were evenly distributed between left side and right side lane changes. The characterization of lane change frequency and minimum TTC was divided into 10 mph speed bins for vehicle travel speeds between 10 and 90 mph. For all lane change events with a closing lead vehicle, the results showed that drivers change lanes most frequently in the 40–50 mph speed range. Minimum TTC was found to increase with travel speed. The variability in minimum TTC between drivers also increased with travel speed.

Conclusions: This study developed and validated an algorithm to detect lane change events in the 100-Car Naturalistic Driving Study and characterized lane change events in the database. The characterization of driver behavior in lane change events showed that driver lane change frequency and minimum TTC vary with travel speed. The characterization of overtaking maneuvers from this study will aid in improving the overall effectiveness of FCW systems by providing active safety system designers with further understanding of driver action in overtaking maneuvers, thereby increasing system warning accuracy, reducing erroneous warnings, and improving driver acceptance.     

Fleet analysis of headway distance for autonomous driving

IntroductionModern automobiles are going through a paradigm shift, where the driver may no longer be needed to drive the vehicle. As the self-driving vehicles are making their way to public roads the automakers have to ensure the naturalistic driving feel to gain drivers’ confidence and accelerate adoption rates.MethodThis paper filters and analyzes a subset of radar data collected from SHRP2 with focus on characterizing the naturalistic headway distance with respect to the vehicle speed.ResultsThe paper identifies naturalistic headway distance and compares it with the previous findings from the literature.ConclusionA clear relation between time headway and speed was confirmed and quantified. A significant difference exists among individual drivers which supports a need to further refine the analysis.Practical applicationsBy understanding the relationship between human driving and their surroundings, the naturalistic driving behavior can be quantified and used to increase the adoption rates of autonomous driving. Dangerous and safety-compromising driving can be identified as well in order to avoid its replication in the control algorithms.     

Using naturalistic driving data to examine drivers' seatbelt use behavior: Comparison between teens and adults

ProblemTeens and young drivers are often reported as one driver group that has significantly lower seatbelt use rates than other age groups.ObjectiveThis study was designed to address the questions of whether and how seatbelt-use behavior of novice teen drivers is different from young adult drivers and other adult drivers when driving on real roads.MethodDriving data from 148 drivers who participated in two previous naturalistic driving studies were further analyzed. The combined dataset represents 313,500 miles, 37,695 valid trips, and about 9500 h of driving. Drivers did not wear their seatbelts at all during 1284 trips. Two dependent variables were calculated, whether and when drivers used seatbelts during a trip, and analyzed using logistic regression models.ResultsResults of this study found significant differences in the likelihood of seatbelt use between novice teen drivers and each of the three adult groups. Novice teen drivers who recently received their driver's licenses were the most likely to use a seatbelt, followed by older drivers, middle-aged drivers, and young drivers. Young drivers were the least likely to use a seatbelt. Older drivers were also more likely to use seatbelts than the other two adult groups. The results also showed that novice teen drivers were more likely to fasten their seatbelts at the beginning of a trip when compared to the other three adult groups.SummaryNovice teen drivers who were still in the first year after obtaining their driver's license were the most conservative seatbelt users, when compared to adult drivers.Practical applicationFindings from this study have practical application insights in both developing training programs for novice teen drivers and designing seatbelt reminder and interlock systems to promote seatbelt use in certain driver groups.     

Drivers' visual behavior when using handheld and hands-free cell phones

ObjectiveThis study investigated driver distraction and how the use of handheld (HH), portable hands-free (PHF), and integrated hands-free (IHF) cell phones affected the visual behavior of motor vehicle drivers.MethodA naturalistic driving study recorded 204 participating drivers using video cameras and vehicle sensors for an average of 31 days. A total of 1564 cell phone calls made and 844 text messages sent while driving were sampled and underwent a video review. Baselines were established by recording epochs prior to the cell phone interactions. Total eyes-off-road time (TEORT) was examined to assess the visual demands of cell phone subtasks while driving. Percent TEORT was reported and compared against the baseline.ResultsVisual-manual subtasks performed on HH, PHF, and IHF cell phones were found to significantly increase drivers' mean percent TEORT. In contrast, conversing on an HH cell phone was found to significantly decrease drivers' mean percent TEORT, indicating that drivers looked at the forward roadway more often. No significant differences in percent TEORT were found for drivers conversing using PHF or IHF cell phones. The mean TEORT durations for visual-manual subtasks performed on an HH cell phone were significantly longer than the mean TEORT durations on either IHF or PHF cell phones.Practical applicationsThis research helps to further reinforce the distinction made between handheld and hands-free cell phone use in transportation distraction policy.     

Gender differences of young drivers on injury severity outcome of highway crashes

ProblemGender differences of young drivers involved in crashes and the associated differences in risk factors have not been fully explored in the United States (U.S.). Accordingly, this study investigated the topic, where the odds ratios (ORs) were used to identify differences in crash involvements between male and female young drivers.MethodLogistic regression models for injury severity of young male drivers and young female drivers were developed. Different driver, environmental, vehicle, and road related factors that have affected young female drivers' and young male drivers' crash involvements were identified using the models.ResultsResults indicated that some variables are significantly related to female drivers' injury risk but not male drivers' injury risk and vice versa. Variables such as driving with valid licenses, driving on weekends, avoidance or slow maneuvers at time of crash, non-collision and overturn crashes, and collision with a pedestrian were significant variables in female driver injury severity model but not in young male driver severity model. Travel on graded roadways, concrete surfaces, and wet road surfaces, collision with another vehicle, and rear-end collisions were variables that were significant in male-driver severity model but not in female-driver severity model.SummaryFactors which increase young female drivers' injury severity and young male drivers' injury severity were identified. This study adds detailed information about gender differences and similarities in injury severity risk of young drivers.Practical applicationsIt is important to note that the findings of this study show that gender differences do exists among young drivers. This sends a message to the industry that the transportation professionals and researchers, who are developing countermeasures to increase the traffic safety, may need to pay attention to the differences. This might be particularly true when developing education materials for driver training for young/inexperienced drivers.     

Teen drivers' awareness of vehicle instrumentation in naturalistic research

IntroductionNaturalistic driving methods require the installation of instruments and cameras in vehicles to record driving behavior. A critical, yet unexamined issue in naturalistic driving research is the extent to which the vehicle instruments and cameras used for naturalistic methods change human behavior. We sought to describe the degree to which teenage participants' self-reported awareness of vehicle instrumentation changes over time, and whether that awareness was associated with driving behaviors.MethodForty-two newly-licensed teenage drivers participated in an 18-month naturalistic driving study. Data on driving behaviors including crash/near-crashes and elevated gravitational force (g-force) events rates were collected over the study period. At the end of the study, participants were asked to rate the extent to which they were aware of instruments in the vehicle at four time points. They were also asked to describe their own and their passengers' perceptions of the instrumentation in the vehicle during an in-depth interview. The number of critical event button presses was used as a secondary measure of camera awareness. The association between self-reported awareness of the instrumentation and objectively measured driving behaviors was tested using correlations and linear mixed models.ResultsMost participants' reported that their awareness of vehicle instrumentation declined across the duration of the 18-month study. Their awareness increased in response to their passengers' concerns about the cameras or if they were involved in a crash. The number of the critical event button presses was initially high and declined rapidly. There was no correlation between driver's awareness of instrumentation and their crash and near-crash rate or elevated g-force events rate.ConclusionAwareness was not associated with crash and near-crash rates or elevated g-force event rates, consistent with having no effect on this measure of driving performance.Practical applicationsNaturalistic driving studies are likely to yield valid measurements of driving behavior.     

Assessing fitness to drive—A validation study on patients with mild cognitive impairment

Objectives: There is no consensus yet on how to determine which patients with cognitive impairment are able to drive a car safely and which are not. Recently, a strategy was composed for the assessment of fitness to drive, consisting of clinical interviews, a neuropsychological assessment, and driving simulator rides, which was compared with the outcome of an expert evaluation of an on-road driving assessment. A selection of tests and parameters of the new approach revealed a predictive accuracy of 97.4% for the prediction of practical fitness to drive on an initial sample of patients with Alzheimer's dementia. The aim of the present study was to explore whether the selected variables would be equally predictive (i.e., valid) for a closely related group of patients; that is, patients with mild cognitive impairment (MCI).

Methods: Eighteen patients with mild cognitive impairment completed the proposed approach to the measurement of fitness to drive, including clinical interviews, a neuropsychological assessment, and driving simulator rides. The criterion fitness to drive was again assessed by means of an on-road driving evaluation. The predictive validity of the fitness to drive assessment strategy was evaluated by receiver operating characteristic (ROC) analyses.

Results: Twelve patients with MCI (66.7%) passed and 6 patients (33.3%) failed the on-road driving assessment. The previously proposed approach to the measurement of fitness to drive achieved an overall predictive accuracy of 94.4% in these patients. The application of an optimal cutoff resulted in a diagnostic accuracy of 100% sensitivity toward unfit to drive and 83.3% specificity toward fit to drive. Further analyses revealed that the neuropsychological assessment and the driving simulator rides produced rather stable prediction rates, whereas clinical interviews were not significantly predictive for practical fitness to drive in the MCI patient sample.

Conclusions: The selected measures of the previously proposed approach revealed adequate accuracy in identifying fitness to drive in patients with MCI. Furthermore, a combination of neuropsychological test performance and simulated driving behavior proved to be the most valid predictor of practical fitness to drive.     

Driving context influences drivers' decision to engage in visual–manual phone tasks: Evidence from a naturalistic driving study

IntroductionVisual–manual (VM) phone tasks (i.e., texting, dialing, reading) are associated with an increased crash/near-crash risk. This study investigated how the driving context influences drivers' decisions to engage in VM phone tasks in naturalistic driving.MethodVideo-recordings of 1,432 car trips were viewed to identify VM phone tasks and passenger presence. Video, vehicle signals, and map data were used to classify driving context (i.e., curvature, other vehicles) before and during the VM phone tasks (N = 374). Vehicle signals (i.e., speed, yaw rate, forward radar) were available for all driving.ResultsVM phone tasks were more likely to be initiated while standing still, and less likely while driving at high speeds, or when a passenger was present. Lead vehicle presence did not influence how likely it was that a VM phone task was initiated, but the drivers adjusted their task timing to situations when the lead vehicle was increasing speed, resulting in increasing time headway. The drivers adjusted task timing until after making sharp turns and lane change maneuvers. In contrast to previous driving simulator studies, there was no evidence of drivers reducing speed as a consequence of VM phone task engagement.ConclusionsThe results show that experienced drivers use information about current and upcoming driving context to decide when to engage in VM phone tasks. However, drivers may fail to sufficiently increase safety margins to allow time to respond to possible unpredictable events (e.g., lead vehicle braking).Practical applicationsAdvanced driver assistance systems should facilitate and possibly boost drivers' self-regulating behavior. For instance, they might recognize when appropriate adaptive behavior is missing and advise or alert accordingly. The results from this study could also inspire training programs for novice drivers, or locally classify roads in terms of the risk associated with secondary task engagement while driving.     

Examining drivers' eye glance patterns during distracted driving: Insights from scanning randomness and glance transition matrix

IntroductionVisual attention to the driving environment is of great importance for road safety. Eye glance behavior has been used as an indicator of distracted driving. This study examined and quantified drivers' glance patterns and features during distracted driving.MethodData from an existing naturalistic driving study were used. Entropy rate was calculated and used to assess the randomness associated with drivers' scanning patterns. A glance-transition proportion matrix was defined to quantity visual search patterns transitioning among four main eye glance locations while driving (i.e., forward on-road, phone, mirrors and others). All measurements were calculated within a 5 s time window under both cell phone and non-cell phone use conditions.ResultsResults of the glance data analyses showed different patterns between distracted and non-distracted driving, featured by a higher entropy rate value and highly biased attention transferring between forward and phone locations during distracted driving. Drivers in general had higher number of glance transitions, and their on-road glance duration was significantly shorter during distracted driving when compared to non-distracted driving.ConclusionsResults suggest that drivers have a higher scanning randomness/disorder level and shift their main attention from surrounding areas towards phone area when engaging in visual-manual tasks.Practical applicationsDrivers' visual search patterns during visual-manual distraction with a high scanning randomness and a high proportion of eye glance transitions towards the location of the phone provide insight into driver distraction detection. This will help to inform the design of in-vehicle human-machine interface/systems.     

Higher cue utilization in driving supports improved driving performance and more effective visual search behaviors

IntroductionOnce qualified, drivers rarely receive objective feedback concerning their performance. This is especially the case in the context of cognitive skills such as situation assessment. The aim of this study was to test the construct validity of an online assessment of motor-vehicle driver cue utilization that forms the foundation for situation assessment. Method: Seventy-one undergraduate Psychology students with broadly comparable driving experience completed a motor-vehicle driving version of EXPERTise 2.0, an online tool that enables inferences concerning the utilization of cues based on responses to task-related stimuli. They also completed a simulated driving task while fitted with an eye tracking device, during which a range of hazards were presented with participants’ responses recorded. Results: The results indicated that higher cue utilization was associated with fewer driving errors and collisions, fewer visual fixations, and fewer saccades in comparison to participants with lower cue utilization. Conclusion: The results provide support for the construct validity of EXPERTise 2.0 as an effective measure of cue utilization in the context of driving.Practical applicationsProviding comparative feedback to drivers concerning their development of situation assessment skills may provide opportunities for further training and development, thereby reducing the likelihood of motor-vehicle accidents.     

Using SHRP 2 naturalistic driving data to assess drivers' speed choice while being engaged in different secondary tasks

IntroductionThe engagement in secondary tasks while driving has been found to result in considerable impairments of driving performance. Texting has especially been suspected to be associated with an increased crash risk. At the same time, there is evidence that drivers use various self-regulating strategies to compensate for the increased demands caused by secondary task engagement. One of the findings reported from multiple studies is a reduction in driving speed. However, most of these studies are of experimental nature and do not let the drivers decide for themselves to (not) engage in the secondary task, and therefore, eliminate other strategies of self-regulation (e.g., postponing the task). The goal of the present analysis was to investigate if secondary task engagement results in speed adjustment also under naturalistic conditions.MethodOur analysis relied on data of the SHRP 2 naturalistic driving study. To minimize the influence of potentially confounding factors on drivers' speed choice, we focused on episodes of free flow driving on interstates/highways. Driving speed was analyzed before, during, and after texting, smoking, eating, and adjusting/monitoring radio or climate control; in a total of 403 episodes.ResultsData show some indication for speed adjustment for texting, especially when driving with high speed. However, the effect sizes were small and behavioral patterns varied considerably between drivers. The engagement in the other tasks did not influence drivers' speed behavior significantly.Conclusions and practical applicationsWhile drivers might indeed reduce speed slightly to accommodate for secondary task engagement, other forms of adaptation (e.g., strategic decisions) might play a more important role in a natural driving environment. The use of naturalistic driving data to study drivers' self-regulatory behavior at an operational level has proven to be promising. Still, in order to obtain a comprehensive understanding about drivers' self-regulatory behavior, a mixed-method approach is required.     

Assessment of patients' medical fitness to drive by primary care physicians: A cross-sectional study

Objectives: Motor vehicle collisions (MVCs) are a significant health burden in Saudi Arabia. The literature has consistently indicated that chronic medical conditions, such as diabetes, heart disease, stroke, obstructive sleep apnea, and neurodevelopmental disorders, increase the risk of MVCs. Therefore, assessment of driver fitness by primary care physicians (PCPs) remains a major health intervention that might reduce MVCs. We studied the practices of PCPs in assessing medical fitness to drive in at-risk patients.

Methods: We conducted a cross-sectional study of all 88 government-funded primary care centers in the city of Riyadh, Saudi Arabia. We administered a self-reported questionnaire to PCPs that inquired about their driving risk assessment for specific medical conditions.

Results: Among all PCPs and centers, 189 PCPs (63%) from 74 centers (84%) participated in our survey. The mean age of the PCPs was 40 ± 10 years, and 108 (57%) were men. The average clinical experience of the group was 13 ± 9 years. Fewer than half of PCPs considered diabetes mellitus (45%) and obstructive sleep apnea (46%) as potential risks for MVCs. Approximately 45% of PCPs did not notify any authority or relatives of potential driving issues that they noticed in their patients. Only 15% of the participants believed that PCPs were responsible for alerting authorities about their fitness to drive.

Conclusions: PCPs did not adequately assess their patients' driving history and eligibility. Efforts are needed to improve awareness among PCPs regarding the effects of chronic medical conditions on driving.     

Role of motorcycle type in fatal motorcycle crashes

IntroductionMotorcycles vary in design and performance capability, and motorcyclists may select certain motorcycle types based on driving preferences. Conversely, motorcycle performance capability may influence the likelihood of risky driving behaviors such as speeding. Both mechanisms may affect fatal crash risk when examined by motorcycle type. Although it was not possible to estimate the effect of each mechanism, the current study analyzed fatal crash data for evidence of motorcycle type differences in risky driving behaviors and risk of driver death.MethodsStreet legal motorcycles were classified into 10 types based on design characteristics and then further grouped as cruiser/standard, touring, sport touring, sport/unclad sport, supersport, and all others. For each motorcycle type, driver death rates per 10,000 registered vehicle years and the prevalence of fatal crash characteristics such as speeding were analyzed. Differences among motorcycle types concerning the effect of engine displacement were examined using Poisson regression.ResultsOverall, driver death rates for supersport motorcycles were four times as high as those for cruiser/standard motorcycles. Fatally injured supersport drivers were most likely to have been speeding and most likely to have worn helmets, but least likely to have been impaired by alcohol compared with drivers of other motorcycle types. The patterns in driver factors held after accounting for the effects of age and gender. Increased engine displacement was associated with higher driver death rates for each motorcycle type.ConclusionStrong effects of motorcycle type were observed on driver death rates and on the likelihood of risky driving behaviors such as speeding and alcohol impairment. Although the current study could not completely disentangle the effects of motorcycle type and rider characteristics such as age on driver death rates, the effects of both motorcycle type and rider age on the likelihood of risky driving behaviors were observed among fatally injured motorcycle drivers.Impact on IndustryCertain motorcycle designs, particularly supersport motorcycles, are associated with increases in risky driving behaviors and higher driver death rates. At present, there are no proven countermeasures for this situation. However, existing countermeasures such as helmet laws and automated speed enforcement could have a substantial benefit.