National Surface Transportation Safety Center for Excellence Reports (NSTSCE, VTTI)

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https://hdl.handle.net/10919/5529

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Real-time Risk Prediction Using Temporal Gaze Pattern and Evidence Accumulation
Yang, Gary; Kaskar, Omkar; Sarkar, Abhijit (National Surface Transportation Safety Center for Excellence, 2024-06-26)
Driver gaze information provides insight into the driver’s perception and decision-making process in a dynamic driving environment. It is important to learn whether driver gaze information immediately before a safety-critical event (SCE) can be used to assess crash risk, and if so, how early in the crash we can predict the potential crash risk. This requires a thorough understanding of the temporal pattern of the data. In this project, we explored multiple key research questions pertaining to driver gaze and its relation to SCEs. We first looked at how temporal gaze patterns in SCEs like crashes and near-crashes are different from baseline events. Then we extended this analysis to understand if the temporal gaze pattern can indicate the direction of the threat. Finally, we showed how contextual information can aid the understanding of gaze patterns. We used traffic density, positions of traffic actors, and vehicle speed as key factors that can influence the temporal nature of gaze and impact safety. This study introduces a real-time crash risk prediction framework leveraging deep learning models based on driver gaze and contextual data. The analysis utilizes the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study, preprocessing it to extract temporal driver gaze patterns and contextual data for two distinct subsets of data. Dataset 1 comprises one-dimensional temporal driver gaze patterns only (named “SHRP2_Gaze”). Dataset 2 includes additional contextual information such as vehicle speed, distance to lead vehicle, and level of service (named “SHRP2_Gaze_Context).” Dataset 1 was used to explore the feasibility of predicting crash risk solely based on driver gaze data. Dataset 2 was applied to assessing the potential for early crash warnings by analyzing both driver gaze patterns and contextual data. For SHRP2_Gaze, four deep learning models (long short-term memory [LSTM], one-dimensional convolutional neural network [1D CNN], 1D CNN+LSTM, and 1D CNN+LSTM+Attention) were trained and tested on various inputs (Input 1: 20 seconds from five zones; Input 2: different lengths from five zones; and Input 3: 20 seconds from 19 zones), considering different lengths and zones of driver gaze data. The 1D CNN with XGBoost exhibited superior performance in crash risk prediction but demonstrated challenges in distinguishing specific crash and near-crash events. This portion of the study emphasized the significance of the temporal gaze pattern length and zone selection for real-time crash risk prediction using driver gaze data only. With SHRP2_Gaze_Context, the 1D CNN model with XGBoost emerged as the top-performing model when utilizing 11 types of 20-second driver gaze and contextual data to predict crash, near-crash, and baseline events. Notably, vehicle speed proves highly correlated with event categories, showcasing the effectiveness of combining driver gaze and contextual data. The study also highlighted the model’s ability to adapt to early crash warning scenarios, leveraging different 2-second multivariate data without an increase in computing time. The deep learning models employed in this study outperform traditional statistical models in distinguishing features from driver gaze and contextual data. These findings hold significant implications for accurate and efficient real-time crash risk prediction and early crash warning systems. The study’s insights cater to public agencies, insurance companies, and fleets, providing valuable understanding of driver behavior and contextual information leading to diverse safety events.
Human-Machine Interface Review: A Comparison of Legacy and Touch-Based Center Stack Controls
Anderson, Gabrial T.; Antona-Makoshi, Jacobo; Klauer, Charlie (National Surface Transportation Safety Center for Excellence, 2024-04-19)
The current study investigated the effect of center stack design on driver distraction. Replacing physical center stack controls with touchscreens is an emerging trend in automotive design. This design decision requires a driver to take their eyes off the forward roadway to interact with a touchscreen center stack, as there is no tactile feedback like touching physical controls. Multiple resource theory (Wickens, 2004) suggests that performing dual tasks (i.e., driving and touchscreen interaction) that compete for similar resources (i.e., visual attention and manual input) can degrade performance on both tasks. It is important to understand the impact of touchscreen controls on driver distraction to ensure safe human-machine interface design. Data from legacy vehicles with physical center stack controls were extracted from the Second Strategic Highway Research Program, an NDS focusing on driver behavior over time in personal vehicles. Data from modern vehicles with touchscreen designs were extracted from the Virginia Connected Corridor 50 Elite Vehicle NDS and Virginia Tech Transportation Institute Level 2 NDS, both focusing on driver behavior in personal vehicles equipped with SAE Level 2 (L2) driving automation features. Twenty-second events that had a center stack interaction (CSI) and minimum speed of 35-mph or greater were selected from each dataset. For the modern vehicle dataset, L2 system status was coded for each event as L2 active or L2 inactive, and task type was coded as visual or visual-manual. The legacy vehicle dataset only had visual-manual CSIs. Driver distraction was defined as eye glances towards the center stack (eyes on center stack; EOCS) during the 20-second event. EOCS was split into total time, mean time, single longest glance, number of glances, and glances over 2 seconds in duration. Total time on task was recorded for the modern vehicles. Results suggest that CSIs with modern vehicle touchscreens have higher EOCS compared to legacy vehicle physical controls. Notably, these differences are even more pronounced when comparing visual-manual CSIs (e.g., adjusting climate control) across display type. Modern vehicle CSIs were also more likely to include glances over 2 seconds compared to legacy vehicle CSIs. Within the modern vehicle dataset, all EOCS metrics (except number of glances), time on task, and glances over 2 seconds were significantly higher when L2 systems were active versus inactive. Visual-manual CSIs were higher for all variables compared to visual CSIs. Glances over 2 seconds were more likely when L2 systems were active for all visual CSIs, but not for visual-manual CSIs. Touchscreen center stack designs are shown to be more distracting than legacy designs comprised of physical controls. When L2 systems are active, CSIs are more distracting than when L2 systems are inactive. Although display type has been shown to have a distracting effect, comparison of specific tasks (e.g., adjusting climate controls) is needed to represent true differences in driver distraction, as more complex tasks that are possible in modern vehicles versus legacy vehicles could contribute to the results of the current study.
VTTI L2 Naturalistic Driving Study: A Self-funded Effort to Capture L2 Feature Use Landscape
Hankey, Jonathan M.; Bedwell, Kaitlyn; Wiersma, Ethel; Stulce, Kelly E.; Perez, Miguel A. (2024-06-20)
The introduction of advanced driver assistance systems (ADAS) into the vehicle fleet continues to accelerate. In the past few years, that introduction has started to permeate the non-luxury vehicle segment, greatly increasing the availability of these technologies to a wide segment of the driving population. The implementation and capabilities of these systems, however, can vary widely across vehicle makes and models, which makes it imperative to have recent data that supports the study of driver adaptations in response to ADAS. While this data can take several forms, naturalistic driving data has proven to provide a flexible means of assessing real-world driver and system performance across a variety of domains and is well suited to understanding ADAS usage. The main objective of the VTTI L2 NDS data collection effort was to create a robust naturalistic driving dataset containing critical information about vehicles with ADAS. As ADAS continue to rapidly evolve and become more readily available in the vehicle fleet, it is essential to understand how these systems are being used and, in some instances misused, by drivers. This knowledge will facilitate the understanding of the safety, performance, and convenience benefits that these systems may offer drivers, along with unintended consequences from the use of these systems. The VTTI L2 NDS is available to help address a wide array of research questions that pertain to the usage of ADAS, along with traditional queries suited to NDS data, in a relatively modern fleet.
Truck Driver Compensation and Crash Risk
Guo, Feng; Xiang, Lanxin (National Surface Transportation Safety Center for Excellence, 2024-06-17)
Compensation methods might affect commercial drivers’ behavior and subsequently the possibility of being involved in a crash. This study investigated the relationship between various compensation strategies for commercial truck drivers and their associated risk of involvement in crashes. A nested case-control approach was adopted. Compensation data was sourced from the follow-up survey of the Commercial Driver Safety Risk Factors study. Upon a case-crash being identified, up to five consented non-crash drivers were contacted to complete a follow-up survey. The analysis employed standard logistic regression and two conditional logistic regression (CLR) models. The CLR models were stratified based on the date of the crash (reference date) and the years of commercial vehicle driving experience to control for temporal and experience-related factors that could confound the relationship between compensation methods and crash risk. To adjust for multiple comparison issues, Tukey’s tests were employed. The results indicate a significant difference in crash rate among driver compensation types. Drivers in PayPerMile-PayPerLoad are associated with a higher crash risk compared to PayPerHour, with odds ratios (ORs) of 3.512, 2.304, and 2.853, in three models, respectively (p < 0.05). Similarly, PayPerMile-PayPerTrip showed increased risk compared to PayPerHour, with ORs of 3.207, 2.260, and 2.548, respectively (p < 0.05). Tukey’s tests confirm significant differences among compensation methods, particularly between PayPerMile-PayPerLoad and PayPerHour, PayPerMile-PayPerLoad, and PayPerLoad, and within PayPerMile-PayPerLoad and PayPerMile. The study suggests a potential link between compensation methods and crash risk, highlighting the need for further research to pinpoint which compensation strategies significantly affect driving safety.
Rocky Mountain Naturalistic Driving Study
Dunn, Naomi; Viray, Reginald (National Surface Transportation Safety Center for Excellence, 2024-06-17)
The Rocky Mountain Naturalistic Driving Study (RMNDS) was, at the time of data collection, the first attempt to use the NDS methodology to conduct research on the effects of cannabis on driving performance. The resulting dataset comprises over 14,000 trips made by 23 participants who self-reported medium to heavy cannabis use and who also reported they had a history of driving under the influence of cannabis. A unique aspect of the study was the collection of quantitative and qualitative drug use data. Qualitative drug use data was collected via an online journal, while quantitative drug use data was collected using a Quantisal oral fluid collection device prior to one driving trip each week. Samples were sent to a toxicology laboratory for analysis, which produced quantifiable test results for the National Institute on Drug Abuse 5 drug panel, including delta-9-THC. Out of the 14,000+ trips, there were a total of 178 verified drug test results along with 1,549 drug use journal entries. While the study proved successful for collecting naturalistic driving data and both objective and subjective drug use data, the difficulty comes in linking the drug use data to the driving data in order to identify periods of driving that may be impacted by the consumption of cannabis and/or other drugs. Further analysis of the RMNDS data, including identification of trips linked to drug use, would provide invaluable information about the impact of cannabis and/or other drugs on driving performance.
Click: Rideshare Naturalistic Driving Study (NDS): Seat Belt Use and Misuse
Miller, Marty; Neurauter, Luke; Radlbeck, Josh; McLaine, Joe (National Surface Transportation Safety Center for Excellence, 2024-06-14)
This report investigates rear passenger seat belt use and misuse within the rideshare environment. This research employed a naturalistic driving study (NDS) approach by instrumenting rideshare vehicles with video recording equipment, yielding insights into seat belt usage patterns. Findings reveal that more than 64% of rideshare passengers did not attempt to use their seat belt, while only 25% of passengers were observed to consistently use their seat belt correctly. Although men and women exhibited similar seat belt usage rates, women were less likely to wear their seat belt correctly. Age and estimated body mass index (BMI) also appeared to influence observed seat belt usage. Children and adult seniors demonstrated the highest ideal usage rates. Passengers with higher BMI demonstrated lower seat belt usage on average. Seating position appeared to impact seat belt usage as well. Passengers in the third (furthest) row demonstrated lower overall seat belt usage compared to those seated in the second row. Notably, across the trips recorded, children and adolescents made up only a very small percentage of the overall passenger population (< 10%) within this rideshare environment. This study underscores the need for interventions to promote seat belt use in rideshare vehicles, potentially leveraging in-vehicle reminders. Addressing these challenges is crucial for enhancing occupant safety and mitigating injury risks in the rideshare context.
Level 2 Automated Driving Systems: Market Inventory and Development of a Reference Guide
Walters, Jacob (National Surface Transportation Safety Center for Excellence, 2024-06-14)
This study was a comprehensive research initiative focused on original equipment manufacturers (OEMs) with significant market shares of Level 2 (L2) automation features in model year 2022 and beyond vehicles. The primary goal of this research was to analyze and categorize operating constraints and human-machine interface (HMI) implementations associated with L2 advanced driver assistance systems (ADAS), emphasizing complex functions and interactivity. The research also prioritized understanding the nuances in implementation across different OEMs, particularly within features like adaptive cruise control and lane-keeping technologies. This assessment focused on identifying and prioritizing OEMs with significant market shares and on-road presence of L2 automation features, streamlining the scope to vehicles with immediate impact. L2 ADAS features were emphasized, particularly adaptive cruise control and lane-keeping technologies, to understand their operational complexity and nuanced HMI components. HMI interactions were categorized across sensory modalities—visual, auditory, and haptic—encompassing all forms of feedback. Describing L2 ADAS features and their communication through HMIs was a key component, alongside creating an OEM matrix outlining feature implementations and conducting cross-OEM comparisons. The matrix is a living documented resource, with the intention that it will be continuously updated with new information, serving as a comprehensive reference for L2 automation features and HMIs. Lessons learned underscore the need for deeper exploration given the variance in OEM approaches and potential pandemic-related supply chain impacts at the time of the initial data collection phase. This research initiative aims to illuminate the landscape of L2 automation features and their intricate HMI interactions, ultimately contributing to a better understanding of these technologies for both internal and external stakeholders.
Investigating Attributes of Young, Inexperienced Commercial Motor Vehicle Drivers
Soccolich, Susan; Camden, Matthew C.; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2024-04-19)
For years, the trucking industry has been concerned with a potential lack of qualified, safe drivers to meet the future demand of the supply chain. The current minimum age at which a driver with a commercial driver’s license (CDL) can operate interstate is 21 years old (49 CFR 391.11). However, recent developments have expanded driver licensing age requirements through the Federal Motor Carrier Safety Administration’s young driver apprenticeship programs and initiatives for young military veterans. The current study used the Commercial Driver Safety Risk Factors (CDSRF) study data to investigate the attributes of safe and unsafe young, inexperienced drivers (ages 21 to 25). The study compared young commercial drivers with and without carrier-recorded crashes, carrier-recorded preventable crashes, nationally recorded crashes, and moving violations for differences in demographic characteristics, driving-related factors, and health-related variables such as medical conditions and treatment status. Overall, most young drivers in the current study did not have a safety-related event. The proportion of drivers with a safety-related event included 14% with at least one carrier-recorded crash, 8% with at least one carrier-recorded preventable crash, 8% with at least one nationally recorded crash, and 10% with at least one moving violation. The study found young drivers who reported an out-of-service (OOS) placement in the past 3 years were at 3 times increased risk of nationally recorded crash involvement. Young drivers with a double/triple trailer endorsement had higher odds of both carrier-recorded and nationally recorded crash involvement compared to drivers without this endorsement. Approximately 80% of the sampled young drivers in the current study had a high school (HS) diploma or higher degree—a higher proportion than observed in an analysis of drivers of all ages in the CDSRF. Drivers showed lower odds of carrier-recorded crash involvement when their academic degree was another degree not listed compared to drivers with a HS diploma or bachelor’s degree. Finally, drivers with diagnosed and treated allergies showed higher risk of crash involvement compared to drivers without this diagnosis; however, it is important to note that very few drivers in the sample had allergies and were receiving treatment. Although the study found few statistically significant factors associated with increased safety event risk, the study did provide more insight into the typical young driver. As younger drivers have more opportunities to join the career field, it is important to better understand this driver age group, their potential risk factors, what factors need further research, and how this driver age group compares to other driver age groups in their demographics and risk.
Evaluation of Truck Parking Needs in a Changing Regulatory Environment
Bell, Stephen; Alden, Andrew (National Surface Transportation Safety Center for Excellence, 2024-03-15)
Commercial driver hours-of-service rules were created to ensure that operators of heavy vehicles on US roads have opportunities to receive adequate rest during and between trips. The use of electronic logging devices to replace handwritten logs, along with the implementation of automated vehicle tracking systems, has created a potential opportunity to track the location of truck drivers with respect to their hours-of-service status. It is envisioned that this real-world driving data can inform the siting of new facilities to address a critical, national shortage of safe and convenient truck parking. This investigation sought to provide proof-of-concept for the use of electronically logged hours-of-service data to determine where additional truck parking areas are needed. A sample of this data was purchased from a commercial telematics provider, and a trusted partner was contracted to transform the acquired raw data into a format that could be used within geographic database system to identify where drivers were located as they neared the end of their allowed driving time. This database would also include the locations of existing truck parking facilities so that gaps in coverage could be identified. Unfortunately, the native format of the hours-of-service data as collected and provided was not conducive to creating a continuous record of a driver’s trips that could be synchronized in time with location data. Also, the sample set of real driving data that was provided in line with the project budget contained too few records to be of practical use. Therefore, proof-of-concept was not validated with this effort. It is likely, though, that the evolution of telematic and electronic logging systems, and the perceived value of this type of information, will result in data quality improvements that will enable the type of analysis envisioned. Examples of the problems encountered are described, and lessons learned and suggestions for future efforts have been provided.
Temporal Patterns in U.S. Pedestrian Traffic Crashes
Witcher, Christina; Henry, Scott; Sullivan, Kaye; Laituri, Tony (2024-04-18)
The number of pedestrian traffic fatalities in the U.S. has been increasing since 2009, despite a general decline during the preceding decades (Figure ES1). In contrast, changes in the number of non-fatal injured pedestrians in traffic crashes were less pronounced during the same period. Our 2022 pedestrian-centric study, funded by the National Surface Transportation Safety Center for Excellence, focused on the differences between those two populations (i.e., fatal and non-fatal injured pedestrians). This analysis extended that study by exploring fatal and non-fatal injured pedestrians from the perspective of temporal characteristics (e.g., year, month, day of week, hour of day), collected from U.S. national datasets for police-reported traffic crashes maintained by the National Highway Traffic Safety Administration. Fatal pedestrian crash data from the Fatality Analysis Reporting System for the 2010–2019 calendar years were examined. Non-fatal injured pedestrian crash data were weighted estimates from two national data sources: the General Estimates System for calendar years 2010–2015 and the Crash Report Sampling System for calendar years 2016–2019. The findings of this temporal analysis can be used to identify potential factors influencing the continued increase in fatalities and the differences between fatal and non-fatal pedestrian crashes. In addition to providing distributions for each temporal characteristic, the ratio of fatal to non-fatal injured pedestrians in traffic crashes was used to identify “peaks” during which fewer pedestrian-involved crashes occurred and/or the injuries were more severe. This ratio was also used to develop categories that typify weekly driving operations. These measures showed distinct differences for fatal and non-fatal injured pedestrians. Fatal pedestrians occurred more often during early hours, weeknights, and weekend-nights, with peaks at night. Non-fatal injured pedestrians occurred more often during weekdays, evening commutes, and weeknights, with peaks during the day. There were no notable differences observed in the 2020 calendar year temporal patterns for fatal and non-fatal injured pedestrians compared with the period 2017–2019. This information is important for determining areas of further study needed to develop or refine vehicle and infrastructure countermeasures and public campaigns to improve pedestrian traffic safety.
Methods to Encourage Slow-moving Trucks to Travel in Designated Lanes
Manke, Aditi; Ridgeway, Christie; Bell, Stephen "Roe" (National Surface Transportation Safety Center for Excellence, 2024-03-20)
As the volume of traffic on highways increases, particularly heavy truck traffic, states throughout the United States are exploring innovative methods to enhance driver comfort, operational efficiency, and road safety. Instead of expanding roadways physically, more organizations are adopting a managed-lanes strategy. This approach assigns specific lanes with unique operational conditions to boost overall roadway performance in efficiency and safety. One popular application of this concept is lane restrictions for trucks. While drivers of smaller vehicles generally welcome these restrictions, research has shown mixed outcomes regarding safety and efficiency improvements. This project aimed to investigate new methods to improve the lane compliance of heavy vehicles, especially slow-moving trucks, on highways. Additionally, existing strategies for enforcement were explored, and new avenues were discussed for improving current restrictions. Six interviews with state Department of Transportation representatives, academic researchers, law enforcement officers, and truck drivers focused on three key areas: policy and enforcement, technological interventions, and effectiveness of interventions. In addition to the interviews, the Virginia 511 real-time traffic information system camera was observed to explore lane compliance violations in Virginia and the number of vehicles impeded due to the violations. Based on the results, 10 recommendations were identified to improve the operations and safety surrounding trucks, especially slow-moving trucks, on the highways.
Streamlining Drowsiness Assessment: An In-Depth Review of ORD and PERCLOS Methods
Soccolich, Susan; Hammond, Rebecca; Camden, Matt; Walker, Stuart (National Surface Transportation Safety Center for Excellence, 2024-03-15)
Every year, drowsy and fatigued driving contributes to thousands of crashes and their resulting injuries and fatalities. Naturalistic driving data allows researchers an opportunity to better understand drowsy driving through review of driver-facing video capturing the driver’s behavior and eyes. Two drowsiness measures that have been successfully used in naturalistic driving data are Observer Rating of Drowsiness (ORD) (Wiegand, McClafferty, McDonald, & Hanowski, 2009) and manual percentage of eye closure (PERCLOS) (Wierwille & Ellsworth, 1994). The current study explored how different drowsiness measures impact fatigue determination for an event and study estimates of fatigue prevalence, risk, and secondary task association for truck and motorcoach drivers. Analyses investigated PERCLOS scores using 1 minute of data (PERCLOS 1) versus 3 minutes of data (PERCLOS 3). The study found the sample size of events with PERCLOS data increased by 8.94% when PERCLOS 1 criteria were used. Overall, matching fatigue determination (whether fatigue was observed) in PERCLOS 3 and PERCLOS 1 scores was found for between 95.89% and 99.48% of truck and motorcoach baselines (BLs) and safety-critical events (SCEs). The risk of SCE involvement when driving while fatigued was consistent for truck drivers when using PERCLOS 1 or PERCLOS 3 to determine fatigue. However, for motorcoach drivers, the risk of SCE involvement when driving while fatigued depended on the PERCLOS measure used. The study also aimed to determine how to potentially lessen the effort of fatigue data reduction in future studies and obtain the most valuable dataset at the lowest cost to time and budget. The single fatigue reduction approach with the lowest time and cost budget was PERCLOS 1 for all events. However, a targeted fatigue reduction approach that includes ORD for all events and targeted PERCLOS 3 or PERCLOS 1 reduction for events that meet or exceed an ORD threshold can reduce the cost of fatigue reduction while maintaining the advantage of ORD reduction.
Developing a Teen Driving Meta-Database Using Three Naturalistic Teen Driving Studies Plus Driver Coach Study
Klauer, Charlie; Hua, Lesheng; Dingus, Thomas A. (2024-01-25)
Motor vehicle collisions are the leading cause of death for teens aged 16 to 19. The risk of motor vehicle crashes is higher among teens aged 16 to 19 than among any other age group. Despite great interest in teen risky driving, little objective information about its prevalence is available. The Naturalistic Teenage Driving Study (NTDS), conducted at the Virginia Tech Transportation Institute (VTTI), provided a rich and powerful dataset, which permits researchers to evaluate driving performance over long periods and provide objective measures of driving risks and contributing factors. However, the NTDS only had 42 novice drivers from southwest Virginia. With the lack of other naturalistic studies of novice teenage driving for comparison, its findings are tentative and need further exploration and confirmation. More NDSs are needed to obtain additional crash data and determine what factors could lead to teen risky driving. Using the trigger thresholds from the NTDS, event databases were created from the Supervised Practice Driving Study (SPDS), the SPDS Attention Deficit and Hyperactivity Disorder (ADHD) Cohort NDS, the Second Strategic Highway Research Program (SHRP 2) NDS, and the Driver Coach Study. Similarly, a database of baseline epochs, per guidelines from the NTDS, was also developed for each of these studies. All event and baseline databases from all five studies were combined into one database to perform meta-analyses using naturalistic teenage driving data. This database is the most complete naturalistic teenage driving database in the world. Many of the key analyses that were performed on only 42 teenage drivers in the NTDS can now be performed on 489 novice drivers from seven locations around the U.S. In this report, we describe each database briefly, including the ADHD teen study, and provide notations about purpose, methods, measures, and instrumentation. We then review what have learned from each database about young driver crash risk. Studies based on the meta-database mainly focused on the prevalence of teen secondary task engagement, distraction, risky driving behavior, and progression of driving skill, as well as the associated crash risks for these behaviors. New projects and new work that this tool has already yielded are described herein, and additional work that still needs to be done is outlined.
Development of a Nighttime Visual Performance Model by Examining Distributions of Detection Distances
Bhagavathula, Rajaram; Gibbons, Ronald B. (2023-12-22)
Modeling the visual performance of drivers at night is complex. In addition to factors like luminance, contrast, observer age, and object size, research has shown that the motion of the object and the expectancy of the observer play an important role in the observer’s ability to detect an object on the roadway at night. Thus, it is important for a visual performance model to account for these factors. However, accounting for these factors could result in highly complex models, as accurately measuring driver expectancy and attention is difficult. A probabilistic approach to modeling nighttime driver visual performance could offer promise. In a probabilistic modeling approach, the variable of interest is treated as a random variable and the probability distribution of this variable is studied as a response to different conditions. In the case of night driving, we propose to use the detection distance of an object (such as a pedestrian) as the variable of interest. Detection distance is a measure of the reaction time of the driver. By studying the distribution of detection distances of objects under different lighting conditions, we can accurately understand the change in the detection probability of an object as a driver approaches an object. The current report had two goals. The first goal was to test if the detection distance distributions are accurately defined by the Weibull distribution. The second goal was to understand how different light levels affect the detection distance distributions of a child-sized mannequin. This was accomplished by performing a distribution analysis involving fitting a Weibull distribution to the detection distance data. The distribution fit will indicate how parameters like shape and scale vary across different conditions and their practical impacts on driver visual performance. The results of the study showed that the Weibull distribution could be used to fit the detection distance data, and that changing the light level definitely influenced the parameters of the distribution. An increase in light level increased the scale parameter and caused the detection distance distribution to stretch out from the pedestrian’s location. The results of the study also showed that both the scale and shape parameters could be used to compare the effectiveness of different lighting systems or interventions. The survivor functions of the detection distance data from the fitted Weibull distribution could be used to compare the effectiveness of a lighting system or a countermeasure by calculating the percentage of the population that detected the pedestrian from a distance greater than the stopping sight distance.
The Influence of COVID-19 Policies on Driving Patterns
Perez, Miguel A.; Werner, Alice (Alec) (National Surface Transportation Safety Center for Excellence, 2023-11-02)
This study sought to investigate the impact of government-imposed COVID-19 restrictions and mandates on driving behavior and patterns during the height of the COVID-19 pandemic in Virginia by examining the pre-restriction (pre-pandemic), restriction (stay-at-home period), and post-restriction (Summer 2020) time periods. Data from 21 vehicles in the VTTI L2 NDS study shaped the investigation. The data encompassed 11,973 trips over 145,000 km (~90,000 miles) and 3,600 data-hours (~5 data-months) of driving. To facilitate the analysis, the vehicle data were split into three separate periods (i.e., pre-pandemic, stay-at-home, and Summer 2020) anchored by three key times in the pandemic’s progression within Virginia’s COVID-19 timeframe. Results showed fluctuations, primarily in the driving exposure, during the pandemic. Changes were most extreme during the stay-at-home period. Moreover, some altered behaviors, particularly those related to driving exposure and trip intent, did not entirely return to their pre-pandemic levels by COVID-19’s 1-year mark. Driving-exposure-related variables revealed the most striking effects; all driver exposure variables changed between the pre-pandemic and stay-at-home periods. Results suggested that trips taken during the stay-at-home period were shorter and briefer, were proportionately fewer in the morning or on weekends, and more commonly involved residential roads. Driving style variables showed other differences, most notably, an increased percentage of speeding mileage between the pre-pandemic and stay-at-home periods. Destination type distributions also changed significantly across the three time periods. For example, “unknown” destinations, indicative of locations with diverse arrays of business types, were more prevalent in the Summer 2020 period than during the stay-at-home period. This change was also observable across all the control periods. While the results do not identify definitive causal factors behind traffic fatality and fatality rate increases, the results inform their discussion. First, drastic changes in the driving exposure occurred during the different pandemic periods. Some of these changes were also accompanied by changes in the driving style. Second, results do not support speeding as the only risk factor for these observed safety issues. While speeds did change, the pandemic simultaneously shifted driving distributions by roadway type. These different driving environments may have played a role in increased traffic fatalities. Third, these observed changes, particularly salient between the pre-pandemic and stay-at-home periods, were not completely elastic; that is, the changes did not fully revert after restrictions were eased.
Koper Curve Principle for Commercial Motor Vehicle (CMV) Traffic Enforcement
Baker, Stephanie Ann; Trimble, Tammy E. (National Surface Transportation Safety Center for Excellence, 2023-08-15)
The Koper curve principle postulates that crime deterrence can be improved with an optimal dosage of police presence at hot spot locations. With the goal of better understanding how to reduce commercial motor vehicle (CMV) crashes, a literature review was conducted to explore whether the Koper Curve principle has ever been applied to efforts aimed at reducing CMV crashes, and if so, how it was applied. In conducting the literature review, several related domains (deterrence, evidence-based policing, and high-visibility enforcement) were also considered as they apply to the use of the Koper Curve for CMV crash reduction. The literature related to the Koper Curve focused primarily on crime deterrence (e.g., robbery), not crash reduction. The literature review revealed one ongoing study that is using the Koper Curve principle toward the goal of reducing CMV crashes on specific interstate corridors (Kentucky Research Center, 2023). Two examples, from Nashville, Tennessee, and São Paulo, Brazil, showed the Koper Curve being applied to crash reduction more generally (not specific to CMVs), which may inform how the Koper Curve could be used to reduce CMV crashes. The literature provided a few best practices that may be helpful to practitioners seeking to reduce crashes in high-risk corridors: (1) use data to target behaviors leading to crashes; (2) use data to identify hot spots where crashes are occurring; (3) provide instruction to officers on how to conduct high-visibility enforcement; and (4) evaluate the enforcement effort.
Face De-identification of Drivers from NDS Data and Its Effectiveness in Human Factors
Thapa, Surendrabikram; Cook, Julie; Sarkar, Abhijit (National Surface Transportation Safety Center for Excellence, 2023-08-08)
Advancements in artificial intelligence (AI) and the Internet of Things (IoT) have made data the foundation for most technological innovations. As we embark on the era of big data analysis, broader access to quality data is essential for consistent advancement in research. Therefore, data sharing has become increasingly important in all fields, including transportation safety research. Data sharing can accelerate research by providing access to more data and the ability to replicate experiments and validate results. However, data sharing also poses challenges, such as the need to protect the privacy of research participants and address ethical and safety concerns when data contains personally identifiable information (PII). This report mainly focuses on the problem of sharing drivers’ face videos for transportation research. Driver video collected either through naturalistic driving studies (NDS) or simulator-based experiments contains useful information for driver behavior and human factors research. The report first gives an overview of the multitude of problems that are associated with sharing driver videos. Then, it demonstrates possible directions for data sharing by de-identifying drivers’ faces using AI-based techniques. We have demonstrated that recent developments in generative adversarial networks (GANs) can effectively help in de-identifying a person by swapping their face with that of another person. The results achieved through the proposed techniques were then evaluated qualitatively and quantitatively to prove the validity of such a system. Specifically, the report demonstrates how face-swapping algorithms can effectively de-identify faces while still preserving important attributes related to human factors research, including eye movements, head movements, and mouth movements. The experiments were done to assess the validity of GAN-based face de-identification on faces with varied anthropometric measurements. The participants used in the data had varied physical features as well. The dataset used was under lighting conditions that varied from normal to extreme conditions. This helped to check the robustness of the GAN-based techniques. The experiment was carried out for over 115,000 frames to account for most naturalistic driving conditions. Error metrics for head movements like differences in roll angle, pitch angle, and yaw angle were calculated. Similarly, the errors in eye aspect ratio, lip aspect ratio, and pupil circularity were also calculated as they are important in the assessment of various secondary behaviors of drivers while driving. We also calculated errors to assess the de-identified and original pairs more quantitatively. Next, we showed that a face can be swapped with faces that are artificially generated. We used GAN-based techniques to generate faces that were not present in the dataset used for training the model and were not known to exist before the generation process. These faces were then used for swapping with the original faces in our experiments. This gives researchers additional flexibility in choosing the type of face they want to swap. The report concludes by discussing possible measures to share such de-identified videos with the greater research community. Data sharing across disciplines helps to build collaboration and advance research, but it is important to ensure that ethical and safety concerns are addressed when data contains PII. The proposed techniques in this report provide a way to share driver face videos while still protecting the privacy of research participants; however, we recommend that such sharing should still be done under proper guidance from institutional review boards and should have a proper data use license.
Do Real-time and Post Hoc Feedback Reduce Teen Drivers' Engagement in Secondary Tasks?
Hua, Lesheng; Ankem, Gayatri; Noble, Alexandria; Baynes, Peter; Klauer, Charlie; Dingus, Thomas A. (National Surface Transportation Safety Center for Excellence, 2023-08-02)
In 2020, 2,800 teens in the United States between the ages of 13 and 19 were killed in motor vehicle crashes (Centers for Disease Control and Prevention, 2023). The purpose of this study is to assess if there is an additional benefit to the driver feedback system implemented in the Driver Coach Study (Klauer et al., 2017) on secondary task reduction and if the same trends of parental involvement are observed. The data used in this study were drawn from two previously completed naturalistic driving studies involving teenage drivers. The Driver Coach Study recruited 90 teen-parent dyads and presented the teen driver with feedback on their driving performance for the first 6 months (Klauer et al., 2017). Parents were able to review a website that provided information on the feedback that their teen received. The Driver Coach Study data were compared to the Supervised Practice Driving Study, which observed 88 teenage drivers during naturalistic driving in the same geographic location who did not receive feedback. Novice driver secondary task engagement was recorded. Parental involvement was examined by tracking which teen/parent groups checked the website and which did not. Results suggest that teen drivers who received feedback were overall less likely to engage in secondary tasks as well as less likely to multitask than those teen drivers who did not receive feedback. Additionally, females generally engaged in secondary tasks more often than males. Teen drivers whose parents logged in to the feedback website also reduced their engagement in some secondary tasks but not all. Unfortunately, no significant reduction in cell phone use was observed between teen drivers who received feedback and those who did not. Overall, the results suggest that further research should be conducted, as monitoring and feedback for teen drivers does reduce overall secondary task engagement.
A Holistic Approach to Reducing Adolescent Risky Behavior: Combining Driving Performance Measures with Psychological and Neurobiological Measures of Risky Adolescent Behavior
Novotny, Adam; Noble, Alex; Kim-Spoon, Jungmeen; Klauer, Charlie (National Surface Transportation Safety Center for Excellence, 2023-08-02)
Adolescent drivers are one of the age groups with the highest crash risks due to factors such as inexperience and poor judgment, an increased propensity for risk-taking, and a higher likelihood to engage in secondary tasks. Previous research has indicated that there may be correlations between teen risky driving behaviors and health risk behaviors such as substance use. Therefore, it is important to understand if there is a relationship between adolescent risky behaviors and unsafe driving outcomes. To investigate this, the Virginia Tech Transportation Institute (VTTI) partnered with the Virginia Tech JK Lifespan Development Lab to conduct a pilot study. During this study, 17 novice teen drivers within 1 month of obtaining their provisional license who were also participating in the Neurobehavioral Determinants of Health-Related Behaviors (NDHRB) Study were recruited. Participants’ personal vehicles were instrumented with VTTI’s mini-data acquisition system, which collected driving performance and behavior data. Data was collected over a 6-month period and analyzed for kinematic risky driving events, eye-glance behavior, secondary task engagement, and seatbelt use. This data was combined with the psychosocial/neurobiological data collected from the surveys, questionnaires, and tests during the NDHRB study. Correlations were discovered between risky driving behaviors (kinematic risky driving events, eye-glance behaviors, secondary task engagement and cellphone use, and proper seatbelt use), and psychosocial/neurobiological measures (reported substance use, insula activation during a lottery task, general health self-assessment, Domain-Specific Risk-Taking Scale health safety risk, health risk behavior, and self-reported risk). The results from this pilot study were promising and point to the need for future research into teen risky behaviors, either driving or otherwise, to create countermeasures to reduce teen crash rates.
Trucking Along: Safe Drives, Healthy Lives
Meissner, Kary; Sloss, Jolee; Mabry, Erin; Gray, April; Martin, Cindy; Levin, Jacob; Camden, Matthew (National Surface Transportation Safety Center for Excellence, 2023-08-02)
This project was initiated to update, refresh, expand, and rebrand the Driving Healthy website and social media accounts into a comprehensive and inclusive healthy driving community platform: Trucking Along: Safe Drives, Healthy Lives. The research team added information, resources, and tools to support healthy habits, both on and beyond the road. Topics covered now include healthy eating, exercise, sleep, mental health, equity and inclusion, and safety on the road, including bringing awareness to the issue of human trafficking and how CMV drivers can help at-risk individuals. The update also added a focus on content and resources for women CMV drivers, who represent a growing but often overlooked group within the trucking industry. The team added new information, including a section dedicated to bringing awareness to sexual harassment. In addition to targeted content for new, seasoned, and prospective CMV drivers, the Trucking Along community platform added information, resources, and tips for end users, who play a critical role in providing support and encouragement to drivers within their’ social and workplace networks. The overall goal of this project was to create a comprehensive and accessible resource that could be used by drivers, from various backgrounds and walks of life, at all stages in their career, to educate them on being happy, safe, and healthy in their trucking careers. The team strives to continue growing and expanding the Trucking Along community platform to continue providing accurate, free, and relevant health information to CMV drivers from all backgrounds.

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