The purpose of this study was to describe the characteristics and progression of practice driving during the learner license period in a sample of teenagers. During the first and last 10 h of practice driving, we examined (1) the amount, variety and complexity of conditions of practice; (2) the nature of parental instruction; and (3) errors that teens made while driving. Data were collected from 90 teens and 131 parents living in Virginia, USA, using in-vehicle cameras, audio recorders, GPS and trip recorders. Based on data collected from the instrumented vehicles, teens practiced for 46.6 h on average, slightly higher than the GDL requirement for their jurisdiction, though half did not complete the required 45 h of practice and only 17% completed the required 15 h of night time driving. Exposure to diverse roadways increased over the practice driving period, which averaged 10.6 months. Most driving instruction occurred in reaction to specific driving situations, such as navigating and identifying hazards, and could be characterized as co-driving. Higher order instruction, which relates to the tactics or strategies for safe driving, was less frequent, but remained stable through the practice driving period. Instruction of all forms was more likely following an elevated gravitational force (g-force) event. Errors decreased over time, suggesting improvements in manual and judgment skills, but engagement in potentially distracting secondary tasks increased (when an adult was in the vehicle). A small percentage of trips occurred with no passenger in the front seat, and the g-force rate during these trips was almost 5 times higher than trips with an adult front-seat passenger. Taken collectively, these findings indicate (1) most teens got at least the required amount of supervised practice, but some did not; (2) instruction was mainly reactive and included some higher order instruction; (3) teens driving skills improved despite increased exposure to complex driving conditions, but secondary tasks also increased. Opportunities remained for improving the quality and variability in supervision and enhancing the development of skills during the lengthy period of practice.

Introduction: Naturalistic 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. Method: Forty-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. Results: Most 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. Conclusion: Awareness 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 applications: Naturalistic driving studies are likely to yield valid measurements of driving behavior. (C) 2017 National Safety Council and Elsevier Ltd. All rights reserved.

In the first few months of 2010 the news media reported numerous instances of “unintended acceleration”. Vehicles were recalled because a floor mat was thought to be snagging the accelerator pedal. However, some vehicles that had gone through the recall still experienced unintended accelerations, indicating that the problem also lay elsewhere. In the 1980s there were recalls of over 100,000 Audi vehicles with automatic transmissions in which drivers had reported unintended accelerations. Earlier cases had also been reported (Mortimer, 2011), so the issue is an old one. As a result of such runaway events, primarily in vehicles with automatic transmissions, automakers equipped them with shift-interlocks, so that the vehicle could not be moved into either a forward or reverse gear after starting the engine unless the driver’s foot was depressing the brake pedal. About 83% of unintended acceleration events were reduced by this device (Schmidt, 1993), confirming the role of driver pedal misapplication when starting out. Other studies found that drivers show pedal confusions also when the vehicle is under way. Those events are not reduced by the shift-interlock. Rogers & Wierwille (1988) found about 0.2% of foot movements resulted in a “serious” pedal misapplication error in a simulator. Tomerlin & Vernoy (1990) found that 1 of 169 of their drivers stepped on the accelerator instead of the brake and continued to do so in a driving test. Such results show that drivers “frequently” make pedal errors, especially when considering that there is about one brake application per mile (Mortimer et al, 1970) in the U.S. The panel members will discuss the human factors issues that cause pedal misapplication in various types of vehicles and their frequency, including crashes that result. The thrust of the discussions will be on how our present knowledge can be used to reduce these hazardous events and to discern what additional information is needed to solve the “unintended acceleration” problem.

In-depth analyses were conducted examining both quantitative and qualitative differences between drivers who were involved in a high number of crashes and near-crashes (mean of 1,438.1 per MVMT) versus drivers who were involved in far fewer crashes and near-crashes (mean of 195.4 per MVMT). These two groups of drivers were labeled as safe and unsafe, respectively. Primary findings indicated that unsafe drivers exhibited more hard deceleration, acceleration, and swerve maneuvers during baseline driving than did the safe drivers. Results also indicated that risky driving behaviors such as traveling at inappropriate speeds and improper braking may increase drivers relative crash risk above that of normal driving. Exploratory analyses were also conducted to assess engagement in risky driving behavior during a variety of environmental and roadway conditions. The results from this analysis indicated that all drivers were willing to engage in risky behaviors during moderately high traffic densities when their speed was impeded than during very low traffic densities when speed selection was not impeded.

United States. National Highway Traffic Safety Administration. Office of Human-Vehicle Performance Research

The Naturalistic Driving is a three-phased effort designed to accomplish three objectives: Phase I, Conduct Test Planning Activities; Phase II, Conduct a Field Test; and Phase III, Prepare for Large-Scale Field Data Collection Effort. This report documents the efforts of Phase II. Project sponsors are the National Highway Traffic Safety Administration and the Virginia Department of Transportation. The 100-Car Naturalistic Driving Study is the first instrumented-vehicle study undertaken with the primary purpose of collecting large-scale, naturalistic driving data. Drivers were given no special instructions, no experimenter was present, and the data collection instrumentation was unobtrusive. In addition, 78 of 100 vehicles were privately owned. The resulting database contains many extreme cases of driving behavior and performance, including severe drowsiness, impairment, judgment error, risk taking, willingness to engage in secondary tasks, aggressive driving, and traffic violations. The data set includes approximately 2,000,000 vehicle miles, almost 43,000 hours of data, 241 primary and secondary drivers, 12 to 14 months of data collection for each vehicle, and data from a highly capable instrumentation system including 5 channels of video and many vehicle state and kinematic sensors. From the data, an database was created, similar in classification structure to an epidemiological crash database, but with video and electronic driver and vehicle performance data. The events are crashes, near-crashes, and other incidents. Data are classified by pre-event maneuver, precipitating factor, event type, contributing factors, associative factors, and the avoidance maneuver. Parameters such as vehicle speed, vehicle headway, time-to-collision, and driver reaction time are also recorded. The current project specified ten objectives or goals that would be addressed through the initial analysis of the event database. This report addresses the first 9 of these goals, which include analyses of rear-end events, lane change events, the role of inattention, and the relationship between levels of severity. Goal 10 is a separate report and addresses the implications for a larger-scale data collection effort.

A naturalistic driving study involving 100 light vehicles equipped with video cameras and other data collection equipment was recently completed. The resulting data set was searched to identify critical incidents involving both light vehicles (LVs) and heavy vehicles (HVs). Each incident was coded on a number of dimensions including the type of incident (what happened) and the Critical Reason for the incident (why it happened). Goals of the analysis included gaining a better understanding of LVHV interactions and providing background information that would serve as a necessary prerequisite to the development of crash countermeasures. For 217 of the 246 LV-HV interaction incidents recorded, the event initiator was attributed to either the LV driver (64%) or the HV driver (36%). The most frequent Incident Type for LV driver initiated incidents was Late Braking for Stopped/Stopping Traffic (41.3%), followed by Lane Change Without Sufficient Gap (21.7%). The most frequently noted Critical Reasons for LV driver initiated incidents were Aggressive Driving Behavior (24.6%), Too Fast for Conditions (15.2%), and Internal Distraction (13.8%). Given that LV drivers were more likely to have initiated an incident, it is believed that efforts at addressing the LV-HV interaction problem should include focusing on the LV driver.

Phase II—Study 6 was part of the Enhanced Night Visibility project, a larger research effort investigating drivers' visual performance during nighttime driving. Study 6 evaluated the possibility of improving the detection distances of pavement markings through the use of fluorescent materials, combined with augmentation of vehicle headlamps with ultraviolet (UV)-A sources. Three different pavement marking materials and 11 headlamp configurations [vision enhancement systems (VESs)] were evaluated. The VESs studied included halogen low beam (HLB), high intensity discharge (HID), halogen high beam (HHB), and high output halogen (HOH) sources. Both the HLB and HID configurations were used in the systems augmented with UV-A sources. The pavement marking materials included fluorescent paint, fluorescent thermoplastic, and a two-component liquid system. Thirty participants from three age groups (young, middle-aged, and older) participated in the study. The results indicated that all of the VESs provided adequate minimal visibility distances for all of the pavement markings at the 40-km/h (25-mi/h) speed driven and that the supplemental UV-A did not improve the detection distances obtained with either the HID or the HLB headlamps. The liquid system and thermoplastic pavement markings outperformed the fluorescent paint. The report discusses the results and implications for both headlamp type and the pavement marking materials.

This volume, an executive summary of the Enhanced Night Visibility project, is the first of 18 volumes that report on the project's evaluation of the merit of implementing supplemental ultraviolet headlamps, supplemental infrared systems, and other vision enhancement systems (VESs) to enhance drivers' nighttime roadway safety. The entire project evaluated 18 VESs in terms of their ability to provide object detection and recognition. Objects included scenarios with pedestrians standing or walking in different locations on the roadway. Pedestrians were dressed in black, white, or blue clothing to produce varying levels of contrast with their surroundings. Detection and recognition testing took place in clear weather, rain, snow, and fog conditions. Project research also evaluated a subset of the VESs for their effect on drivers' disability and discomfort glare. The VESs were also tested for their value in facilitating drivers' detection of pavement markings and other traffic control devices. The results indicated that supplemental ultraviolet headlamps do not provide sufficient benefit to justify further testing; however, supplemental infrared vision enhancement systems do offer an improvement over headlamps alone for detection of pedestrians. Near infrared (NIR) systems have the potential to provide an added benefit in detecting pedestrians in inclement weather, but the implementation of NIR technology is the key to achieving this benefit.

Phase II—Study 5 helped expand the knowledge of how current vision enhancement systems (VESs) affect the discomfort glare experienced by nighttime drivers. The empirical testing for this study was performed on the Smart Road. Sixty participants were involved in the study, which consisted of two data collection efforts. An 11 (VES) by 3 (Age) experimental design was used to investigate the effects of different types of VESs and driver's age on discomfort glare. In addition, an evaluation of the Schmidt-Clausen and Bindels equation was performed to determine its predictive value in driving scenarios with oncoming glare. The results of the empirical testing suggest that halogen headlamps selected for this testing produce more discomfort glare than the high intensity discharge headlamps tested. There was also some indication that ultraviolet (UV)-A may add slightly to discomfort glare. In addition, modifications of the Schmidt-Clausen and Bindels equation may provide headlamp designers with insight into how drivers will rate discomfort glare of proposed headlamps.

Phase II--Study 1 was performed as a stepping stone to expand the knowledge of how different vision enhancement systems can affect detection and recognition of different types of objects. The empirical testing for this study was performed on the Smart Road testing facility during clear weather conditions. A total of 30 participants were involved in the study. A 12 by 9 by 3 mixed-factorial design was used to investigate the effects of different types of vision enhancement systems, types of objects on the roadway, and driver's age on detection and recognition distances; subjective evaluations were obtained for the different systems as well. The results of the empirical testing suggest that no vision enhancement system consistently performs best in clear weather conditions. However, the halogen headlamp tested (low-beam configuration) consistently provided one of the longest detection and recognition distances, and even when other systems provided farther detection distances, these distances were generally not significantly different from halogen low beam. The only exception was the infrared thermal imaging system tested, which resulted in significantly farther detection distances for pedestrians and cyclists wearing dark-colored (low-contrast) clothing.

In situ data collection refers to the gathering of performance and behario data in a naturalistic, real-workd environment. Unlike research efforts that use laboratory, simulator, or other controlled or fabricated environments, in- situ data collection places data gathering equipment inside the vehicle with a driver as that driver operates under real-world conditions. This paper highlights the features of in- situ data collection, and provides an overview of a data collection system with a description of the platforms used for heavy vehicle and light vehicle research. Examples of its use in U.S. DOT-funded research projects are discussed, including recent applications to study local/short haul driver fatigue, long haul driver fatigue, and an ongoing passenger vehicle study. Potential future applications of the in-situ data collection platform are also discussed.