The Long and Winding Road: 25 Years of the National Advanced Driving Simulator.

The National Advanced Driving Simulator is a high-fidelity motion-base simulator owned by the National Highway Transportation Safety Administration and managed and operated by the University of Iowa. Its 25-year history has intersected with some of the most significant developments in automotive history, such as advanced driver assistance systems like stability control and collision warning systems, and highly automated vehicles. The simulator is an application of immersive virtual reality that uses multiprojection instead of head-mounted displays. A large-excursion motion system provides realistic acceleration and rotation cues to the driver. Due to its level of immersion and realism, drivers respond to events in the simulator the same way they would in their own vehicle. We document the history and technology behind this national facility.

Quantifying and recommending seat belt reminder timing using naturalistic driving video data.

INTRODUCTION: To better understand the timing of when people buckle their seat belt, an analysis of a naturalistic driving study was used. The study provided a unique perspective inside of the vehicle where the entire seat belt was visible from the time the driver entered the vehicle to one minute of driving forward or 32 kph. METHOD: Seat belt buckling behavior was identified for 30 drivers. An additional 10 drives for 13 of these drivers were identified for a seat belt sequencing, which identified the points when the vehicle was put into ignition, shifted, when vehicle movement began, and when the seat belt was buckled. The speed at belt closure was also identified. The timing from ignition to buckle and to shifting into forward gear were examined to identify the speed and appropriate timing for seat belt reminders. RESULTS: The data show that drivers were buckled in over 92% of the 3,102 drives. In addition, in 70% of those total drives, the drivers were buckled before the vehicle began movement. Of greater interest for seat belt reminders/interlocks are those drives when drivers buckle after movement. When considering time from ignition to seat belt closure, the mean was 27.5 s. Because higher speeds are typically reached when traveling forward rather than reverse, it was important to know the time duration from shifting into drive to buckling. With this consideration, the mean to buckle dropped to 16.2 s. The mean speed at buckling when traveling forward was 15.3 kph. From the regression analysis, the input variables 'Age,' 'Sex,' 'Weight,' 'Environment,' and 'Weather' are significant contributors in predicting the log odds of a driver putting on seatbelt. CONCLUSIONS: With the understanding that higher speeds lead to an increased risk of injury and/or death and with the results of the analysis, a recommendation of a 30 s time from forward shift and a 25 kph (6.9 m/s) threshold for reminder systems should be implemented. The regression analysis also validates that most of the predicted seat belt buckling times are within 30 s. Practical Applications: This would reduce perception of nuisance alerts and protect the driver from higher speed unbuckled crashes. The seat belt buckling time prediction model also demonstrates good potential for developing tailored buckling warning system for different drivers.

Mechanisms of cannabis impairment: Implications for modeling driving performance.

Past research on cannabis has been limited in scope to THC potencies lower than legally available and efforts to integrate the effects into models of driving performance have not been attempted to date. The purpose of this systematic review is to understand the implications for modeling driving performance and describe future research needs. The risk of motor vehicle crashes increases 2-fold after smoking marijuana. Driving during acute cannabis intoxication impairs concentration, reaction time, along with a variety of other necessary driving-related skills. Changes to legislation in North America and abroad have led to an increase in cannabis' popularity. This has given rise to more potent strains, with higher THC concentrations than ever before. There is also rising usage of novel ingestion methods other than smoking, such as oral cannabis products (e.g., brownies, infused drinks, candies), vaping, and topicals. The PRISMA guidelines were followed to perform a systematic search of the PubMed database for peer-reviewed literature. Search terms were combined with keywords for driving performance: driving, performance, impairment. Grey literature was also reviewed, including congressional reports, committee reports, and roadside surveys. There is a large discrepancy between the types of cannabis products sold and what is researched. Almost all studies that used inhalation as the mode of ingestion with cannabis that is around 6% THC. This pales in comparison to the more potent strains being sold today which can exceed 20%. Which is to say nothing of extracts, which can contain 60% or more THC. Experimental protocol is another gap in research that needs to be filled. Methodologies that involve naturalistic (real world) driving environments, smoked rather than vaporized cannabis, and non-lab certified products introduce uncontrollable variables. When considering the available literature and the implications of modeling the impacts of cannabis on driving performance, two critical areas emerge that require additional research: The first is the role of cannabis potency. Second is the route of administration. Does the lower peak THC level result in smaller impacts on performance? How long does potential impairment last along the longer time-course associated with different pharmacokinetic profiles. It is critical for modeling efforts to understand the answers to these questions, accurately model the effects on driver performance, and by extension understand the risk to the public.

Examining the Association Between Age and Tractor Driving Performance Measures Using a High-Fidelity Tractor Driving Simulator.

HIGHLIGHTS This study uses a new tractor driving simulator to examine the impact of age on perception response time in an emergency braking situation. The results demonstrate increased risk for crash among older farm equipment operators. ABSTRACT. Transportation-related incidents are the leading cause of occupational fatalities for all industries in the U.S. In the agriculture industry, where tractor-related incidents are the leading cause of occupational fatality, fatal crashes occur more frequently among senior farm equipment operators (FEOs) than younger FEOs. This study examined the association between age and driving performance among FEOs using a simulated driving environment. We demonstrated that older FEOs have longer perception response times when encountering an incurring semi-truck during a simulated drive than younger FEOs. These results persisted when adjusted for selected medical diagnoses and medications, tractor generation, and tractor horsepower. However, due to the small sample size and limitations of the tractor driving simulator, its use for event perception response time research is questionable. The tractor driving simulator used in this study may be better suited for distracted driving studies and studies comparing the ways in which FEOs drive passenger vehicles compared to tractors.

Examination of Realism in a High-fidelity Tractor Driving Simulator.

Transportation-related incidents are the leading cause of occupational fatalities for all industries in the U.S., including the agricultural industry, which suffers thou- sands of crashes involving farm equipment each year. Simulated driving studies offer a safe and cost-effective way to conduct driving research that would not be feasible in the real world. A tractor driving miniSim was developed and then evaluated for realism at the University of Iowa among 99 Midwestern farm equipment operators. It is important for driving simulators to have a high degree of realism for their results to be applicable to non-simulated driving operations. High-fidelity driving simulators facilitate extrapolations made by driving research but should be re-tested for realism when changes are made to the design of the simulator. The simulator used in this study emulated a tractor cab with realistic controls, three high-resolution screens, and high-fidelity sound. After completing a 10-minute drive, farm equipment operators completed a survey and scored four specific domains assessing specific characteristics (i.e., appearance, user interface, control, and sound) of the tractor simulator's realism using a seven-point Likert scale (from 0 = not at all realistic to 6 = completely realistic). An overall realism score and domain scores were calculated. Farm equipment operators were also asked to provide recommendations for improving the tractor miniSim. Overall, farm equipment operators rated the simulator's realism favorably (i.e., >3 on a scale from 0 to 6) for all individual items and domains. The appearance domain received the highest average realism score (mean = 4.58, SD = 1.03), and the sound domain received the lowest average realism score (mean = 3.86, SD = 1.57). We found no significant differences in realism scores across farm equipment operator characteristics. The most frequently suggested improvements were to tighten the steering wheel (27%), make the front tires visible (19%), and that no improvements were needed to improve the simulator realism (18%). This study demonstrates that the new trac- tor miniSim is a viable approach to studying farm equipment operations and events that can lead to tractor-related crashes. Future studies should incorporate the suggested improvements and seek to validate the simulator as a research and outreach instrument.

Classifying and predicting risky driving among novice drivers: A group-based trajectory approach.

INTRODUCTION: Classifying risky driving among new teenage drivers is important for efficiently targeting driving interventions. We thoroughly investigated whether novice drivers can be clustered by their driving outcome profiles over time. METHODS: A sample of 51 newly licensed teen drivers was recruited and followed over a period of 20 weeks. An in-vehicle video recording system was used to gather data on dangerous driving events referred to as DDEs (elevated g-force, near-crash, and crash events), risky driving behaviors referred to as RDBs (e.g., running stop signs, cell phone use while driving), and miles traveled. The DDE and RDB weekly rates rate were determined by dividing the number of DDEs and RDBs in a week by the number of miles traveled in that week, respectively. Group-based trajectory modeling was used to map the clustering of DDE rate and RDB rate patterns over time and their associated covariates. RESULTS: Two distinct DDE rate patterns were found. The first group (69.1% of the study population) had a lower DDE rate which was consistent over time. The second had a higher DDE rate pattern (30.9%) and characterized by a rising trend in DDE rate followed by a steady decrease (inverted U-shaped pattern). Two RDB rate patterns were also identified: a lower RDB rate pattern (83.4% of the study population) and a higher RDB rate pattern (16.6%). RDB and DDE rate patterns were positively related, and therefore, co-occurred. The results also showed that males were more likely than females to be in the higher DDE and RDB rate patterns. CONCLUSION: The groups identified by trajectory models may be useful for targeting driving interventions to teens that would mostly benefit as the different trajectories may represent different crash risk levels. Practical applications: Parents using feedback devices to monitor the driving performance of their teens can use the initial weeks of independent driving to classify their teens as low or high-risk drivers. Teens making a very few DDEs during their early weeks of independent driving are likely to remain in the lower risk group over time and can be spared from monitoring and interventions. However, teens making many DDEs during their initial weeks of unsupervised driving are likely to continue to make even more DDEs and would require careful monitoring and targeted interventions.

Auditory Global Positioning System and Advanced Driver Assistance Systems: A Safer Alternative to Bioptic Telescopes for Drivers Who Are Visually Impaired?

This work challenges the standard of the past 40 years, which required the use of a bioptic telescope by individuals with vision loss wanting to be licensed to drive in most states in the United States.Driving continues to be the key to independence for many individuals, particularly older drivers who live in an area where public transportation is limited or nonexistent. For the past 40 years, the most frequently option to allow drivers who are visually impaired to maintain driving privileges was to require them to use a bioptic telescope. Bioptic telescopes were felt to be necessary for wayfinding when driving. In addition, it was thought that a person could look through a bioptic telescope and still be aware of the driving environment around him/her. Human factor research has shown that the assertion that an individual can attend to two tasks simultaneously is not possible. Taking one's eyes off the road for as little as 2 seconds can lead to lane position breakdown. In 2018, wayfinding can now be more easily accomplished with the use of ubiquitous technologies like Global Positioning System systems on our telephones and in our cars. Driver distraction principles support safer alternatives to bioptic telescopes because these audio options allow the drivers to maintain their eyes and their attention on the road and the traffic around them. The switching of view within the bioptic spectacles is attentionally demanding, and the visual field restriction of such devices reduces overall situation awareness by narrowing the driver's attention.

Teen driver system modeling: a tool for policy analysis.

BACKGROUND: Motor vehicle crashes remain the leading cause of teen deaths in spite of preventive efforts. Prevention strategies could be advanced through new analytic approaches that allow us to better conceptualize the complex processes underlying teen crash risk. This may help policymakers design appropriate interventions and evaluate their impacts. METHODS: System Dynamics methodology was used as a new way of representing factors involved in the underlying process of teen crash risk. Systems dynamics modeling is relatively new to public health analytics and is a promising tool to examine relative influence of multiple interacting factors in predicting a health outcome. Dynamics models use explicit statements about the process being studied and depict how the elements within the system interact; this usually leads to discussion and improved insight. A Teen Driver System Model was developed by following an iterative process where causal hypotheses were translated into systems of differential equations. These equations were then simulated to test whether they can reproduce historical teen driving data. The Teen Driver System Model that we developed was calibrated on 47 newly-licensed teen drivers. These teens were recruited and followed over a period of 5-months. A video recording system was used to gather data on their driving events (elevated g-force, near-crash, and crash events) and miles traveled. RESULTS: The analysis suggests that natural risky driving improvement curve follows a course of a slow improvement, then a faster improvement, and finally a plateau: that is, an S-shaped decline in driving events. Individual risky driving behavior depends on initial risk and driving exposure. Our analysis also suggests that teen risky driving improvement curve is created endogenously by several feedback mechanisms. A feedback mechanism is a chain of variables interacting with each other in such a way they form a closed path of cause and effect relationships. CONCLUSIONS: Teen risky driving improvement process is created endogenously by several feedback mechanisms. The model proposed in the present article to reflect this improvement process can spark discussion, which may pinpoint to additional processes that can benefit from further empirical research and result in improved insight.

Examining teen driver crashes and the prevalence of distraction: Recent trends, 2007-2015.

INTRODUCTION: Teen drivers crash at a much higher rate than adult drivers, with distractions found as a factor in nearly 6 out of 10 moderate-to-severe teen crashes. As the driving environment continues to rapidly evolve, it is important to examine the effect these changes may be having on our youngest and most vulnerable drivers. METHOD: The purpose of this study was to identify types of vehicle crashes teens are most frequently involved in, as well as the distracting activities being engaged in leading up to these crashes, with a focus on identifying changes or trends over time. We examined 2,229 naturalistic driving videos involving drivers ages 16-19. These videos captured crashes occurring between 2007 and 2015. The data of interest for this study included crash type, behaviors drivers engaged in leading up to the collision, total duration of time the driver's eyes were off the forward roadway, and duration of the longest glance away from forward. RESULTS: Rear-end crashes increased significantly (annual % change=3.23 [2.40-4.05]), corresponding with national data trends. Among cell phone related crashes, a significant shift occurred, from talking/listening to operating/looking (annual % change=4.22 [1.15-7.29]). Among rear-end crashes, there was an increase in the time drivers' eyes were off the road (ß=0.1527, P=0.0004) and durations of longest glances away (ß=0.1020, P=0.0014). CONCLUSIONS: Findings suggest that shifts in the way cell phones are being used, from talking/listening to operating/looking, may be a cause of the increasing number of rear-end crashes for teen drivers. PRACTICAL APPLICATIONS: Understanding the role that cell phone use plays in teen driver crashes is extremely important. Knowing how and when teens are engaging in this behavior is the only way effective technologies can be developed for mitigating these crashes.

Evaluating variability in foot to pedal movements using functional principal components analysis.

There are reasons why the driver's foot may not be applied to the correct pedal while driving and this can lead to unintended consequences. In this study, we seek to capture common and unique patterns of variations in drivers' foot movements using functional principal components analysis (FPCA). This analysis technique was used to analyze three categories of pedal response types (direct hits, corrected trajectories, and pedal errors) based on the various foot to pedal trajectories. Data from a driving simulator study with video data of foot movements for 45 drivers was used for analyses. Most foot movements show common patterns associated with direct hits and corrected trajectories with some level of variation. However, those foot movements associated with unique patterns might be early indicators of pedal errors. The findings of this study can be used with collision mitigation systems to provide early detection of foot trajectories that are more likely to result in a pedal error.

Using event-triggered naturalistic data to examine the prevalence of teen driver distractions in rear-end crashes.

INTRODUCTION: While teen driver distraction is cited as a leading cause of crashes, especially rear-end crashes, little information is available regarding its true prevalence. The majority of distraction studies rely on data derived from police reports, which provide limited information regarding driver distraction. METHOD: This study examined over 400 teen driver rear-end crashes captured by in-vehicle event recorders. A secondary data analysis was conducted, paying specific attention to driver behaviors, eyes-off-road time, and response times to lead-vehicle braking. RESULTS: Among teens in moderate to severe rear-end crashes, over 75% of drivers were observed engaging in a potentially distracting behavior. The most frequently seen driver behaviors were cell phone use, attending to a location outside the vehicle, and attending to passengers. Drivers using a cell phone had a significantly longer response time than drivers not engaged in any behaviors, while those attending to passengers did not. Additionally, in about 50% of the rear-end crashes where the driver was operating/looking at a phone (e.g., texting), the driver showed no driver response (i.e., braking or steering input) before impact, compared to 10% of crashes where the driver was attending to a passenger. CONCLUSIONS: The high frequency of attending to passengers and use of a cell phone leading up to a crash, compounded with the associated risks, underlines the importance of continued investigation in these areas. PRACTICAL APPLICATIONS: Parents and teens must be educated regarding the frequency of and the potential effects of distractions. Additional enforcement may be necessary if Graduated Driver Licensing (GDL) programs are to be effective. Systems that alert distracted teens could also be especially helpful in reducing rear-end collisions.

The Effect of Secondary Task Engagement on Adolescents' Driving Performance and Crash Risk.

PURPOSE: The purpose of this review was to synthesize the evidence of the effects of secondary task engagement on novice adolescent's driving performance and crash risk. METHODS: Searches of multiple databases were conducted using search terms related to secondary task engagement and teenage drivers. Articles were selected for inclusion if they were: written in English, an empirical study assessing the impact of secondary task engagement on driving, and included study participants who were licensed drivers between the ages of 14 and 17 years (if research was conducted in the United States) or within 18 months licensure in other countries. Thirty-eight abstracts were reviewed. RESULTS: Fifteen studies met the inclusion criteria. Most studies examined the effects of electronic device use as the secondary task. Effects were assessed using crash databases, simulator, instrumented vehicle, and naturalistic driving studies. Texting resulted in increased lane deviations and eyes off road time in simulated driving, whereas talking on a cell phone had little effect. Naturalistic studies, which use vehicle instrumentation to measure actual driving, found secondary tasks that required drivers to look away from the forward roadway also increased the risk of crashes and near-crashes for young novice drivers, whereas tasks that did not require eyes to be off the forward roadway (e.g., talking on cell phone) had no effect on crash risk. CONCLUSIONS: Methodological differences in the definition and measurement of driving performance make it difficult to directly compare findings, even among the limited number of studies conducted. Despite this, results suggest that secondary tasks degrade driving performance and increase risk only when they require drivers to look away from the forward roadway. Future research needs to focus more explicitly on the ways in which secondary task engagement influences drivers' behavior (e.g., interfering with information acquisition or manual control of the vehicle). This, along with the use of standard measures across studies, would build a more useful body of literature on this topic.

Visual and cognitive distraction metrics in the age of the smart phone: A basic review.

Sources of distraction are numerous and varied, and defining and measuring distraction and attention is complicated. The driving task requires constant adjustments and reallocation of attention to cognitive, motor, and visual processes. While it is fairly straightforward to measure distraction in an experimental situation (e.g., simulator, closed course), driver distraction in the real world is highly contextual. While no single metric is capable of capturing the complexities of distraction, several have proved useful in helping researchers gain fuller understanding of it. Few have reached a level of consensus among researchers and user interface designers. ISO and SAE may be considered the 'gold standard' for providing mechanisms through which open scientific consensus-based standards can be achieved.While there are a number of metrics used in predicting distraction, three have been studied closely and are going through the SAE and ISO standards process. They are (1) 'the occlusion method'; (2) the Lane Change Test (LCT); and (3) the Detection Response Task (DRT). The metrics described here apply generally to the experimental context where driving is tightly controlled. Like any method, there are limitations with each-and they don't necessarily agree with one another.Experimental methods and analyses are different than those in naturalistic driving (ND). ND relies more on data mining versus traditional experimental manipulation. ND data are a challenge precisely in that they lack experimental control.In future, driver metrics will go beyond specific measurement of task load, and will include how drivers self regulate when they choose to be distracted.

Special considerations in distracted driving with teens.

Novice teen drivers have long been known to have an increased risk of crashing, as well as increased tendencies toward unsafe and risky driving behaviors. Teens are unique as drivers for several reasons, many of which have implications specifically in the area of distracted driving. This paper reviews several of these features, including the widespread prevalence of mobile device use by teens, their lack of driving experience, the influence of peer passengers as a source of distraction, the role of parents in influencing teens' attitudes and behaviors relevant to distracted driving and the impact of laws designed to prevent mobile device use by teen drivers. Recommendations for future research include understanding how engagement in a variety of secondary tasks by teen drivers affects their driving performance or crash risk; understanding the respective roles of parents, peers and technology in influencing teen driver behavior; and evaluating the impact of public policy on mitigating teen crash risk related to driver distraction.

Parents' knowledge, attitude, and use of child restraints, Shantou, China.

BACKGROUND: China has not adopted national policies for child safety restraints in cars, although children are increasingly traveling in cars. OBJECTIVE: To describe child restraint use, and parents' knowledge of and attitude toward child restraint in Shantou, China. METHODS: An observational study and driver survey on child restraint use was conducted in the Southeast China city of Shantou in 2012. Observational sites included 22 middle schools, 31 primary schools, 24 kindergartens, and 4 hospitals. Drivers were asked about their knowledge of and attitude toward the use of child restraints. In September 2012, multivariate regression was used to evaluate the factors associated with increased child restraint use. RESULTS: Of 3333 children observed in vehicles, only 22 (0.6%) children were secured in child safety seats or booster seats and 292 (8.7%) children were wearing seatbelts. More than half (n=508, 56.1%) of the infants or toddlers were riding on the laps of adults. Of 1069 drivers who responded to the survey, more than 62% thought it was necessary to use child restraint while traveling in a car. The drivers' higher education status (OR=1.56, 95% CI=1.07, 2.27) and seatbelt use (OR=4.00, 95% CI=2.56, 6.25) were associated with increased child restraint use. Parents (OR=0.55, 95% CI=0.34, 0.88) and male drivers (OR=0.61, 95% CI=0.46, 0.81) had reduced odds of children properly rear-seated. CONCLUSIONS: Child restraint use is very low in China, although the majority of drivers had positive attitudes about child restraint. These findings indicate that child restraint policies and educational approaches are urgently needed in China.

Perception and biodynamics in unalerted precrash response.

This research seeks to better understand unalerted driver response just prior to a serious vehicle crash. Few studies have been able to view a crash from the inside-with a camera focused on the driver and occupants. Four studies are examined: 1) a high-fidelity simulator study with an unalerted intersection incursion crash among 107 drivers; 2) four crashes from the Virginia Tech Transportation Institute (VTTI) 100 car study; 3) 58 crashes from vehicles equipped with an event triggered video recorder; and 4) a custom-designed high-fidelity simulator experiment that examined unalerted driver response to a head-on crash with a heavy truck. Analyses concentrate on decomposing driver perception, action, facial and postural changes with a focus on describing the neurophysiologic mechanisms designed to respond to danger. Results indicate that drivers involved in severe crashes generally have preview that an impact is about to occur. They respond first with vehicle control inputs (accelerator pedal release) along with facial state changes and withdrawal of the head back towards the head restraint. These responses frequently occur almost simultaneously, providing safety system designers with a number of reliable driver performance measures to monitor. Understanding such mechanisms may assist future advanced driver assistance systems (ADAS), advanced restraints, model development of advanced anthropomorphic test dummies (ATDs), injury prediction and the integration of active and passive safety systems.