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

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

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New Tech Educational Outreach
Baker, Stephanie Ann; Levin, Jacob; Trimble, Tammy E.; Giurintano, Amelia; Bell, Stephen (National Surface Transportation Safety Center for Excellence, 2025-07-10)
The impacts of transportation on human health and safety may be addressed at least in part by new vehicle technologies such as advanced driver assistance systems (ADAS) and electric vehicles (EVs). To reap the safety and health benefits of these technologies and overcome barriers to adoption, the driving public needs to understand the benefits of these technologies, their limitations, and how to properly use them. One way to address barriers to proper use and adoption of these new vehicle technologies is through educational outreach. The objectives of this project were to gather information and materials from current EV educational outreach efforts and the National Highway Traffic Safety Administration’s (NHTSA’s) ADAS educational outreach program to inform the development of a New Tech Outreach (NTO) educational outreach program and to identify potential partners for a future effort. The approach to conducting the project involved two steps. As a first step, the project team reviewed materials from NHTSA’s ADAS educational campaign among others and conducted a scan of current literature on EV educational outreach. The second step was reviewing three EV educational outreach programs being conducted by public-facing organizations to include as examples of how to conduct EV educational outreach. As part of the program review, the project team reviewed program websites and key reports, attended events, and interviewed a program lead from each program. Interviews, which were conducted between May 1 and November 14, 2024, covered a range of topics including program goals, target audiences, topics, approaches, funding, partnerships, implementation barriers, and lessons learned. Each interview also explored future opportunities for collaboration and partnership not only with the Virginia Tech Transportation Institute (VTTI) but for the types of organizations represented by the National Surface Transportation Safety Center for Excellence. A set of key takeaways was identified, including potential partnership opportunities. The key takeaways are not an exhaustive list of everything reviewed or generalizable findings on this topic; rather, they are meant to serve as a summary of what the project team considers to be potential inputs to the design of a future NTO program. Throughout the takeaway discussion, resources that the project team may use or reference in a future NTO program are highlighted. In addition, an important aspect of this project is how VTTI can partner with organizations such as those included here in a future NTO program. Working with universities in general and VTTI specifically was discussed during each program review, and numerous suggestions were made for how such collaboration can occur in the future. As a next step, the report concludes with a suggestion and basic outline for an NTO pilot project.
Parents’ Usage of Commercially Available Mobile Phone Applications for Teen Drivers: What Is Working?
Young, Taylor C.; Bedwell, Kaitlyn E.; Anderson, Gabrial T.; Klauer, Sheila G. (National Surface Transportation Safety Center for Excellence, 2025-07-09)
Recent years have shown increasing popularity in a particular type of mobile phone application, or app: parent-teen driver performance monitoring apps. A growing body of research suggests that feedback and post hoc intervention of teen driving can be used as a tool to influence behavioral changes in driving. Commercially available apps for smartphones that incorporate telematics data (i.e., vehicle speed, hard braking, and GPS location) are a trending way for parents to be able to track their teens’ driving. Previous research provides many insights into how teens perceive driver monitoring apps and what role these apps play in improving driving behavior (Gesser-Edelsburg & Guttman, 2013; Peer et al., 2020). Teen driver monitoring apps can provide teens with tangible proof of their driving behavior that either demonstrates positive safe driving behavior or targets areas where constructive criticism on risky behavior is merited. Teens often feel driver monitoring apps are a more objective and unbiased way to monitor their driving with evidence compared to their parents’ perceptions. On the other hand, these apps can also be viewed by teens as an extension of parental supervision, as well as an invasion of privacy and a restriction of their independence. Parents greatly influence their teens’ behavior whether they are behind the wheel or not. Parents have a unique role as the main enforcers for what the states may require for licensing rules. Parents have their own views regarding the driving risks that their teens face and may use these driver monitoring apps in different ways. This project aimed to understand what information parents of teen drivers want to see and use on a driving monitoring app and what they find useful for enabling the most effective feedback relationship with their teen. The research team worked with stakeholders to develop survey tools to help better understand parents’ and teens’ attitudes, preferences, and needs regarding app-based driving feedback. The survey was administered by State Farm using their survey software (Suzy) to collect data nationally, including research participants from U.S. territories. Suzy can filter by gender, age, employment, education, income, and location. The questions took different forms, including multiple choice, Likert scales, open ended, and ranking questions. A total of 649 responses were received. It was found that parents generally check monitoring apps the most during the following conditions: locating their teen, situations at certain times of the day, or when they know their teen is driving through bad weather. Parents are using monitoring apps to know where their teen is located, to see if their teen is speeding while driving, and to see the location where the teen is driving. Parents responded that the alerts they prefer from the monitoring apps include crash alerts, speeding alerts, and safe arrival notifications.
Weather Characterization
Palmer, Matthew; Stowe, Loren (National Surface Transportation Safety Center for Excellence, 2025-07-03)
The Virginia Tech Transportation Institute (VTTI) successfully developed and deployed a mobile weather characterization system aimed at enhancing transportation safety research at the Virginia Smart Roads facility. This sensor was used to characterize a sampling of the water-based, VTTI simulated weather at the Virginia Smart Roads Facility. A Parsivel2 disdrometer was mounted on a vehicle to measure precipitation particle size distribution and falling velocity. The mobile nature of the system enables efficient data collection along the entire roadway section. Using the sensor, a rain characterization study revealed that the rain produced by the facility showed variability in droplet size distribution, with deviations from natural rain patterns. The limited fall height (10 meters) led to lower terminal velocities than naturally occurring rainfall, which usually fits the Gunn-Kinzer relationship. With respect to the Marshall-Palmer relationship, the VTTI rain represents stratiform rain distribution more than convective rain. Wind was found to have a bigger effect on measurement accuracy due to the sensitivity of the sensor. A snow characterization study revealed challenges in correlating liquid water equivalents measured to actual snow depth due to variability in snow density and particle orientation of the VTTI-produced, water-based snow. The disdrometer software assumes the snow density to calculate the liquid water equivalent. The addition of a heated precipitation gauge could enhance accuracy. Operationally, the study found that calibrating weather towers by pressure, rather than visual estimation, improved the consistency of rainfall production. However, issues such as hose kinks impacted flow rates, indicating areas for infrastructure improvements. Recommendations for future work include enhancements such as wind sensors, articulating mounts, and longer duration testing under various wind conditions are recommended to improve weather characterization fidelity.
Naturalistic Driving Study on Cannabis Use in Washington and Virginia
Bedwell, Kaitlyn E.; Jain, Sparsh; Young, Taylor C.; Perez, Miguel A.; Hankey, Jonathan M. (National Surface Transportation Safety Center for Excellence, 2025-07-11)
This study examined the consumption behavior of cannabis users and its influence on driving performance through a comprehensive naturalistic driving study (NDS) conducted in Washington and Virginia. The study aimed to address gaps in research by leveraging real-world data to evaluate how cannabis consumption impacts driver behavior, safety-critical events, and crash risk. The study’s objectives included assessing the prevalence of driving under the influence of cannabis (DUIC), examining variations in impairment across different consumption methods and doses, and exploring the relationships between self-reported intoxication levels and objective performance metrics. Participants were selected based on their regular cannabis use and self-reported DUIC history. Data was collected via in-vehicle instrumentation, a smartphone-based journal app, breathalyzer readings, and oral fluid tests. The study offered key insights into the impact of self-reported cannabis consumption on driving behavior, with cannabis trips occurring alongside sober trips with similar frequency and temporal distributions. Self-reported substance use data revealed that cannabis consumption methods differed between regions, with dabs being the preferred form in Washington and smoking cannabis flower (e.g., joints, pipes, bowls) dominating in Virginia. Polysubstance use, particularly with alcohol, was prevalent, with 13.7% of Washington and 20.1% of Virginia journal entries involving multiple substances. Breathalyzer data showed that 20% of Washington’s and 14.5% of Virginia’s alcohol-positive trips exceeded the 0.08% blood alcohol concentration (BAC) limit. Quantisal oral fluid tests highlighted variations in tetrahydrocannabinol (THC) levels, with mean delta-9 THC levels significantly higher in Washington (1,662 ng/ml) compared to Virginia (260.9 ng/ml). While 85% of Quantisal tests were successfully submitted, challenges such as outlier THC readings due to participant noncompliance with testing protocols were noted, indicating the complexity of linking subjective impairment levels to objective performance metrics. The findings highlight the complexity of DUIC and the need for further research to inform public policy, law enforcement practices, and safety guidelines. The dataset provides a valuable resource for understanding cannabis-related driving risks and developing targeted interventions. Further analyses should explore the nuanced effects of cannabis potency, user tolerance, and polysubstance interactions on driving performance. Enhanced data collection techniques could improve the reliability of future studies.
Pilot In-Vehicle Carbon Monoxide Detector Study
Manke, Aditi; Hicks, Pat; Hankey, Jonathan M. (National Surface Transportation Safety Center for Excellence, 2025-03-28)
This study addresses the critical issue of carbon monoxide (CO) exposure in truck cabins, particularly in vehicles used for work zones. The research explores the levels of CO within these confined environments, with the objective of identifying factors that could contribute to increased CO levels. Two Truck Mounted Attenuators equipped with CO sensors and data acquisition systems were monitored under real-world operational conditions from July to December 2023. The study shows that average in-cabin CO levels across the two vehicles were generally low, 1.22 ppm in Truck 1 and 1.61 ppm in Truck 2. There were occasional spikes, with levels reaching 10.05 ppm in Truck 1 and 8.59 ppm in Truck 2. These peaks occur during specific operational scenarios, such as prolonged idling, open windows, and acceleration near traffic congestion. The findings highlight the significance of both environmental factors (e.g., proximity to exhaust sources, ventilation efficiency) and operational behaviors in influencing CO exposure. The analysis showed some patterns: CO levels were lowest during motion (1.14 ppm in Truck 1, 1.43 ppm in Truck 2), attributed to improved air circulation. But when parked on the road, levels rose to 1.63 ppm and 1.98 ppm, likely from idling and nearby traffic emissions. In controlled environments, such as parking facilities, CO levels stayed consistently low. These findings support prior studies that emphasize the impact of ventilation settings and driver practices on air quality (Dirks et al., 2018; Marinello et al., 2023). The study highlights the role of vehicle maintenance and design in mitigating CO exposure. Older vehicles with compromised exhaust systems and poor ventilation settings worsen the in-cabin pollution levels. To minimize risks, real-time CO monitoring and regular maintenance are essential. Additionally, educating drivers on best practices, such as limiting idling and optimizing ventilation modes, can significantly reduce exposure. While the study provides valuable insights, it is limited by its sample size (two vehicles) and duration (39 operational days per truck), which may not capture seasonal variations or represent broader fleet conditions. Future research should include more vehicle types, longer study periods, and additional factors like weather and window positioning to provide a more complete picture. Overall, the research highlights the need for targeted interventions in truck cabin air quality management. Practical steps include upgrading ventilation systems, integrating CO detection technology, and implementing urban planning measures to cut down on traffic-related exposure. By focusing on these strategies, industry leaders can enhance driver safety and well-being while also contributing to broader public health improvements.
Effectiveness of Wearable Devices to Study Driving Stress of Long-haul Truck Drivers in Naturalistic Driving Systems
Thapa, Surendra Bikram; Sarkar, Abhijit (National Surface Transportation Safety Center for Excellence, 2025-03-21)
Advancements in wearable technology, driven by innovations in artificial intelligence and the Internet of Things, have significantly expanded our ability to monitor health and safety in various domains, including transportation. In this age of big data, the continuous collection and analysis of physiological data from wearable devices has opened new avenues for enhancing road safety and driver well-being. This report investigates the feasibility and effectiveness of using wearable technology to monitor fatigue and stress levels among long-haul commercial motor vehicle drivers. The goal of this research is to reduce risks associated with drowsy driving, which is a significant contributor to road accidents worldwide (M. Islam & Mannering, 2023). Wearable technologies, such as the Empatica EmbracePlus smartwatch, offer a promising approach to real-time health monitoring by providing continuous insights into drivers’ physiological states. This study was designed to evaluate the capability of these devices to detect early signs of fatigue and stress, understand the various factors affecting a driver’s well-being, and identify strategies to manage these issues effectively. A repeated measures study design was implemented, collecting comprehensive health data from a sample of 10 long-haul drivers over a 5-day period (i.e., 1 work week), with one driver providing additional data over an extended 4-week period. Data collection involved continuous monitoring of physiological signals, such as heart rate variability and electrodermal activity, supplemented by self-reported information on stress levels, traffic conditions, diet, and other relevant variables through daily questionnaires. The findings from this study highlight the potential of wearable technology to transform driver safety and health management practices. The data collected provided valuable insights into the drivers’ daily experiences and behaviors, revealing patterns related to stress levels, dietary habits, hydration practices, and coping mechanisms. Most participants experienced mild to moderate stress, influenced significantly by traffic conditions and driving durations. The report indicates that wearable technology can provide key insights by enabling continuous monitoring of fatigue and stress levels; this then suggests a potential for early alerts for necessary breaks and prevention of accidents due to drowsy driving. Furthermore, the data generated by these devices can be used to develop personalized interventions that can improve drivers’ health and work conditions. For successful implementation, it is important to address concerns regarding data privacy and usability while creating an environment that encourages the adoption of such technology. Encouraging awareness about the applications of wearable devices and their capabilities in monitoring health information could create such an environment. Future research should focus on refining wearable technologies to enhance user comfort, maintain data security, and explore broader applications within transportation safety related to long-haul drivers.
Pediatric Vehicular Hyperthermia Injury: Feasibility of Data Collection
Glenn, Laurel; Glenn, Eric; Perez, Miguel A. (National Surface Transportation Safety Center for Excellence, 2025-02-07)
Pediatric vehicular hyperthermia (PVH) remains a critical public health issue, characterized by the rapid and dangerous increase in a child’s body temperature when left in a hot vehicle. Despite public awareness campaigns and legislative efforts, PVH continues to account for an average of 37 fatalities annually in the United States. PVH cases are a combination of complex situations involving the unique vulnerability of children to hyperthermia and caregiver memory lapses, intentionally leaving a child unattended, and children gaining access to vehicles. The research conducted aimed to assess the feasibility of collecting detailed data on non-fatal PVH cases, which are currently underreported and poorly understood. This investigation utilized interviews with personnel from a variety of organizations likely to be involved in PVH incidents, such as police departments, fire departments, emergency medical services (EMS), and hospitals. The findings revealed critical gaps in the existing data collection systems that impede accurate tracking and reporting of PVH events. None of the interviewed organizations had specific data fields to capture PVH cases, leading to the reliance on narrative fields, which are inconsistent and subjective. This research hence highlights the need for the implementation of required, standardized data fields across national databases, such as the National EMS Information Systems (NEMSIS) and the National Fire Incident Reporting System (NFIRS), as well as within hospital coding systems. Furthermore, the addition of a specific International Classification of Diseases (ICD) code for PVH is recommended to facilitate more accurate case tracking once medical organizations are involved. Improved data collection and reporting would provide a clearer understanding of the prevalence of PVH and guide more effective public health interventions and legislative actions.
Preparing First Responder Stakeholders for ADAS and ADS Deployments
Trimble, Tammy E.; Faulkner, Daniel (National Surface Transportation Safety Center for Excellence, 2024-12-16)
Previous research has found that public safety providers are unclear about the capabilities associated with advanced driver assistance systems (ADAS)- and Automated Driving System (ADS)-related technologies. Providing outreach to this population will reduce uncertainty regarding these technologies, which in turn will lead to improved safety and interactions, including crash documentation, while in the field. A training curriculum was developed that consisted of two parts: (1) a classroom portion which can be delivered in-person or online and (2) a hands-on experiential portion. Two training options were presented to local agencies: (1) an approximately 1-hour online session, to be held at the agency’s convenience, which covers the prepared training materials; and (2) an in-person, half-day session which covers the prepared training materials and provides exposure to ADAS- and ADS-equipped vehicles. Recruitment efforts resulted in five in-person and six online attendees. In-person attendees represented three separate organizations, with one organization being represented by officers from three locations. The online attendees represented six separate organizations. Only one organization had an attendee in both the in-person and online options. To better understand the time to be allotted for the online training, the in-person training was held first. As a result, the online training was ultimately extended to 1.5 to 2 hours, which allowed time for discussion throughout the training. Feedback received directly from the participants at the conclusion of the training and via the online questionnaires was overwhelmingly positive. Moving forward, the training materials will need to be updated on a continual basis to ensure the ongoing timeliness of information shared. To share the materials with a wider range of individuals, the training could be developed and shared in a manner like the Virginia Tech Transportation Institute’s (VTTI’s) Sharing the Road program, where VTTI representatives visit schools to provide information and hands-on encounters to promote safely sharing the road with large trucks. A key to success will be employing individuals with first responder experience to provide the training. Feedback suggested that those with hands-on experience combined with their ties to VTTI resulted in perceived credibility. Also, providing hands-on opportunities to see variations in technologies across vehicle models and applications was considered beneficial. Working with VTTI partners, it may be possible to obtain demonstration vehicles for this purpose. Through this development process, the team can work towards accreditation and providing the training as part of academy, in-service, or regional training days.
Using Artificial Intelligence/Machine Learning Tools to Analyze Safety, Road Scene, Near-Misses and Crashes
Yang, Gary; Sarkar, Abhijit; Ridgeway, Christie; Thapa, Surendrabikram; Jain, Sandesh; Miller, Andrew M. (National Surface Transportation Safety Center for Excellence, 2024-11-18)
Artificial intelligence (AI) and machine learning technologies have the potential to enhance road safety by monitoring driver behavior and analyzing road scene and safety-critical events (SCEs). This study combined a detailed literature review on the application of AI to driver monitoring systems (DMS) and road scene perception, a market scan of commercially available AI tools for transportation safety, and an experiment to study the capability of large vision language models (LVLMs) to describe road scenes. Finally, the report provides recommendations, focusing on integrating advanced AI methods, data sharing, and collaboration between industry and academia. The report emphasizes the importance of ethical considerations and the potential of AI to significantly enhance road safety through innovative applications and continuous advancements. Future research directions include improving the robustness of AI models, addressing ethical and privacy concerns, and fostering industry-academic collaborations to advance AI applications in road safety.
Assessing Factors Leading to Commercial Driver Seat Belt Non-Compliance
Camden, Matthew C.; Soccolich, Susan A.; McSherry, Thomas; Ridgeway, Christie; Stapleton, Steven (National Surface Transportation Safety Center for Excellence, 2024-10-24)
The current research study utilized a literature review and analysis of two data sources to determine situational factors associated with reduced seat belt usage among CMV drivers. The literature review identified characteristics of seat belt use, reasons drivers may or may not use seat belts, methods to improve seat belt use rates, and important gaps in the literature. The data analysis used data collected in two separate studies to assess seat belt use rates and explore the relationship between seat belt use and environmental, roadway, vehicle, and driver factors. The first study collected observational data in 2015 from multiple sites in Michigan with high rates of truck/bus-involved crashes. The second study collected naturalistic driving data during the Federal Motor Carrier Safety Administration’s Advanced System Testing Utilizing a Data Acquisition System on Highways (FAST DASH) second Safety Technology Evaluation Project (commonly referred to as FAST DASH 2). The naturalistic driving data set included safety-critical events (SCEs), which were reduced for driver behaviors and environmental and roadway information. In the current study, driver seat belt use was observed in 93% of the FAST DASH 2 naturalistic driving SCEs and in 81% of SCEs in the observational data set. The analysis of observational and FAST DASH 2 naturalistic driving study data identified several factors where seat belt use patterns changed significantly across the factor levels; however, the analyses for each data set did not show consistency in statistical significance. The observational data showed seat belt use to be associated with day of week, time of day, road type, truck type, and fleet type. Little correlation was found between seat belt use and other driver behaviors. The analysis of observational study data did find seat belt use to be significantly higher in observations where drivers were using a hands-free cell phone with earpiece compared to drivers not using a cell phone or talking on a handheld cell phone. The naturalistic driving data showed that drivers operating on divided highways had higher seat belt use compared to those driving on non-physically divided roadways.
Equity in Transportation Safety
Robinson, Sarah; Medina, Alejandra; Gibbons, Ron; Kassing, Andrew; Myers, Bradley (National Surface Transportation Safety Center for Excellence, 2024-09-24)
Equity in transportation is a key issue for the Federal Highway Administration (FHWA), as well as state departments of transportation. Equitable transportation ensures safety for all road users across all modes of transportation for all communities. FHWA recommends the adoption and equitable application of a safe system approach to achieve Vision Zero objectives to eliminate traffic fatalities and severe injuries. A safe system fundamentally recognizes human error and accounts for it when designing systems and operations. Incorporating equity into roadway safety data is critical for conducting data-driven safety analysis. FHWA recommends collaboration with underserved communities through a process of collecting and analyzing data, engaging community representatives, implementing improvements, and evaluating impacts. Ensuring robust and accurate data is critical. State programs have worked to incorporate a wide variety of data into their crash models. Social and demographic data such as race, ethnicity, gender, age, education, employment status, income level, disability status, among many other variables, have been evaluated and demonstrated to be factors in the frequency of crashes. States have published mapping tools to visualize data trends and identify locations for targeted implementation efforts in conjunction with scoring metrics for evaluating proposed solutions.
Roadway Departure Events Using SHRP 2 NDS Data
Kassing, Andrew; Gibbons, Ronald B. (National Surface Transportation Safety Center for Excellence, 2024-09-16)
Roadway departures encompass a particularly dangerous subset of driving events during which a vehicle either crosses the centerline or edge line or otherwise leaves the lane of travel. Each year, roadway departure crashes account for roughly 50% of all fatal crashes reported to the National Highway Traffic Safety Administration. This study’s goal was to evaluate the factors contributing to roadway departure events. The data set used was naturalistic driving data collected during the Second Strategic Highway Research Program (SHRP 2). The full data set consisted of 28,937 driving events with information spanning 70 variables that characterized each event. For all events provided to the research team, reductionists categorized each as either a safety-critical or baseline event and reviewed their variable levels. Analyses determined that numerous driver behaviors and roadway environment elements influenced the odds and severity of roadway departure events. Overall, 80% of the adverse driver behavior categories were found to significantly increase the odds of roadway departure crashes at intersections. Drivers who were intoxicated, cut turns, took turns too widely, or were speeding were significantly overrepresented in roadway departure events. The prevalence data indicated that distracted driving was a very common behavior regardless of segment type or event outcome. More specifically, among the baseline events, drivers performing secondary tasks associated with distracted driving were very common. Throughout the baseline sample (i.e., no incidents), drivers were more likely to be distracted by tasks such as device usage, passenger interaction, personal hygiene, eating, smoking, etc., than performing no secondary tasks at all. Across all roadway departure events, adverse driver behavior was observed in 82% of incidents on tangent segments and 93% of incidents at intersections. Roadway environment changes, such as in pavement surface condition, were found to influence roadway departure frequency and severity. Analyses suggested that maintaining the skid resistance of roadway surfaces, even during inclement weather, may be essential to reducing the occurrence and severity of roadway departure events. Furthermore, roadway departures were overrepresented in incidents where sunlight, glare, headlamps, precipitation, vehicles, or infrastructure were obstructing the driver’s view. Researchers noted that visibility obstructions were significantly more common at intersections than tangent segments.
Risk Factors Re-evaluation with Bayesian Network Using SHRP 2 Data
Han, Shu; Guo, Feng (National Surface Transportation Safety Center for Excellence, 2024-09-11)
Traffic safety is a complex system influenced by numerous factors, including human behavior, road design, vehicle technology, and environmental conditions. Each of these factors can impact the safety of the transportation system in unique ways, and all factors could interact with each other in complex ways. The goal of this study was to evaluate the joint contribution of multiple risk factors to traffic safety by examining the interactions among different factors. This study considered 24 potential risk factors that reflect different perspectives in the analysis, including driver demographics, driving behavior, environmental conditions, road characteristics, traffic context, vehicle kinematics within a 5-second window of each event, and cell phone ban policies. There were two aspects to this study: first, it explored the relationships between traffic safety risk factors using unsupervised learning models with data from the Second Strategic Highway Research Program Naturalistic Driving Study. Second, with supervised learning models, the study developed a robust data-driven Bayesian network model, evaluated impacting risk factors, quantified their corresponding importance on driving risk, and consequently identified high-risk scenarios.
Development of a Naturalistic Observer-Based Rating of Near-Crash Severity in Naturalistic Driving Data
McClafferty, Julie A.; Walker, Stuart (National Surface Transportation Safety Center for Excellence, 2024-09-04)
The analysis of safety-critical events, including crashes and near-crashes, from naturalistic driving studies has proven extraordinarily useful in guiding transportation safety policies, transportation technology, and transportation infrastructure. Near-crashes, which are much more common than crashes, have the potential to answer many research questions. However, they are difficult to define, and their severity is difficult to rate. By definition, there is no impact to measure in a near-crash and therefore no injury or property damage to assess. Near-crashes cover a range of scenarios, and perceptions of severity can vary greatly depending on the person experiencing or perceiving them. From a research perspective, this variability makes near-crashes challenging. Severe near-crashes may be considered most similar to crashes and serve as better surrogates than less severe near-crashes, but less severe near-crashes are still very different from “normal” driving and are still relevant to policy, technology, and infrastructure development research. In this effort, an observer-based, naturalistic near-crash severity rating protocol was developed and tested to help researchers produce near-crash event data effectively and reduce associated variability. Goals included producing a protocol that can (1) produce consistent and meaningful ratings, (2) be incorporated effectively and efficiently into the standard primary event assessment, (3) be implemented by trained data reductionists with access to video and basic kinematic data charts, (4) be applied without complex models, calculations, or statistical modeling, and (5) mirror the existing crash severity scale in implementation and conceptualization. A key concept in this work was that of conflict urgency. There is no clear answer about how urgency can or should be observed or measured in naturalistic data, especially in non-crash scenarios. It is clear, however, that the concepts of collision imminence (a sense of conflict timing) and potential crash severity (related to possible damage and injury) are important factors. Thus, an additional goal was to achieve a balance between actual kinematics, predictive outcomes, and perceived subjective risks. Operational definitions, associated research protocols, and reference guides were developed for four levels of near-crash severity ranging from Critical Severity to Lower Severity. These are documented in the appendices. At their core, the definitions are based on objective metrics such as relative speed, time-to-collision, and type of conflict, but with room for subjective assessments. An iterative approach was used in the development of these definitions, and this included assessments to evaluate interrater reliability. Results indicated that reference materials and training improve interrater reliability.
Aerial Traffic
Viray, Reginald; Saffy, Joshua; Mollenhauer, Michael A. (National Surface Transportation Safety Center for Excellence, 2024-08-30)
This report documents a significant advancement in work-zone safety through the strategic integration of aerial drone technology and machine vision software. It summarizes the project’s phases: Technical Assessment and Procurement, System Integration and Validation, and Deployment Assessment. The Technical Assessment and Procurement phase led to the selection of Smartek ITS’s DataFromSky product for its unique real-time processing capabilities of aerial drone video, making it superior to other commercial offerings. The System Integration and Validation phase ensured that the video streams, whether real-time or recorded, were processed effectively for varying roadway scenarios, including work zones and intersection monitoring. Accompanying development work included a user-defined data interface with the capability to trigger intruding vehicle alarms. The Deployment Assessment phase confirmed the system’s precision, with object detection up to 150 meters and sub-500 millisecond latency in relaying data for real-time alerts. Despite some GPS data discrepancies due to wind-induced drone movements, the system showed promise in controlled and real-world environments. Overall, the project acquired and validated the system’s functionality, with successful tests on live and recorded video feeds, software video processing, and real-time data transmission, culminating in the development of a robust intruding vehicle alarm mechanism. The system demonstrated great potential for deployment across various Virginia Tech Transportation Institute research initiatives, setting a precedent for future work in enhancing work zone safety.
Highly Automated Ridesharing: Implications of Novel Seating Configurations and Seatbelt Use
Anderson, Gabrial T.; Radlbeck, Joshua (National Surface Transportation Safety Center for Excellence, 2024-08-29)
Upcoming novel vehicle designs, such as vehicles equipped with Level 4 (L4) driving automation features, are intended to be used as rideshare automated vehicles (RAVs). In vehicles without L4 automation features, drivers typically receive all regulated telltales, indicators, and alerts intended to encourage seatbelt use. In a vehicle with L4 features, a driver is not present, meaning these alerts need to be presented directly to the occupants. Understanding occupant behavior in a novel vehicle design can inform effective methods for encouraging seatbelt use. Thirty participants rode in an RAV on a closed test track. Participants rode in groups of three on a 5-minute route at speeds up to 15 mph. There were two benches with three seats per bench: a forward-facing row and rear-facing row. Human-machine interfaces were placed overhead for each seating position; these included a novel seatbelt reminder alert (SBR) that would chime if the passenger was unbuckled when the vehicle started to move. If participants remained unbuckled, the SBR would last 10 seconds before doubling in tempo until the participant in that seating position was buckled. A post ride survey was administered to capture participant opinions of their experience. Where appropriate, results from a previous proprietary study of 60 single riders were compared to the current study. Group riders were significantly more likely to buckle before vehicle movement than single riders. Group riders were more likely to sit in the rear-facing row of seats than the single riders. Across all participants, females were more likely to buckle before vehicle movement than males. For those participants who received the SBR alert (i.e., those who were not buckled before vehicle movement), the majority began buckling within the first ~7-seconds of SBR presentation. Results suggest riding context can impact seatbelt use. Although a majority of participants preferred to face forward, group dynamics forced some participants to sit backwards in all rides. Further research is needed to understand the impact of riding environment, other rider populations, and SBRs on buckling behavior in RAVs.
Investigation of ADAS/ADS Sensor and System Response to Rainfall Rate
Cowan, Jonathan B.; Stowe, Loren (National Surface Transportation Safety Center for Excellence, 2024-08-23)
Advanced driver assistance systems (ADAS) and automated driving systems (ADS) rely on a variety of sensors to detect objects in the driving environment. It is well known that rain has a negative effect on sensors, whether by distorting the inputs via water film on the sensor or attenuating the signals during transmission. However, there is little research under controlled and dynamic test conditions exploring how rainfall rate affects sensor performance. Understanding how precipitation may affect the sensor’s performance, in particular the detection and state estimation performance, is necessary for safe operation of the ADAS/ADS. This research strove to characterize how rainfall rate affects sensor performance and to provide insight into the effect it may have on overall system performance. The team selected a forward collision warning/automatic emergency braking scenario with a vehicle and surrogate vulnerable road user (VRU) targets. The research was conducted on the Virginia Smart Roads’s weather simulation test area, which can generate various simulated weather conditions, including rain, across a test range of 200 m. The selected sensors included camera, lidar, and radar, which are the primary sensing modalities used in ADAS and ADS. The rain rates during testing averaged 21 mm/h and 40 mm/h. Overall, the data backed up the expected trend that increasing rainfall rate worsens detection performance. The reduced detection probability was most prominent at longer ranges, thus reducing the effective range of the sensor. The lidars showed a general linear trend of 1% reduction in range per 1 mm/h of rainfall with some target type dependence. The long-range and short-range cameras show at least a 60% reduction in detection range at 40 mm/h. The object camera, which only detected the vehicle target, showed better performance with only a 20% reduction in range at 40 mm/h, which may be due to the underlying ADAS specific detection model. For vehicles, the radars typically showed a linear drop in detection range performance with an approximately 20% reduction in range at 40 mm/h rainfall rate. The VRU target showed a larger decrease in detection range compared to the vehicle target due likely to the smaller overall signal the VRU target returns.
Assessing the Impact of Disability on Drivers’ Equitable Use of Advanced Driver Assistance Systems (ADAS): A Literature Review
Stulce, Kelly E.; Antin, Jonathan F. (NSTSCE, 2024-08-22)
The growing prevalence of advanced driver assistance systems (ADAS) in the U.S. passenger fleet promises increased mobility and enhanced safety outcomes for all drivers, but particularly for disabled drivers, a group that comprises 11.9% of the driving population (U.S. Bureau of Labor Statistics, 2021). For ADAS to realize their full potential, stakeholders need to consider the difficulties associated with ADAS use by disabled drivers as well as the potential benefits. To support this reckoning, the authors reviewed the extant literature to discover emerging themes and to identify gaps in the literature. We then synthesized these results into a proposed road map for future work that addresses the challenges of using ADAS to enhance mobility and improve safety for all drivers, including those who are disabled. Our review of the literature reveals gaps that point the way forward for further work that will support the optimal implementation of ADAS to compensate for disability-induced driving performance deficits. Specifically, our gap analysis and research road map suggest that this work should begin with using subjective methodologies (e.g., focus groups, interviews, and surveys) to learn from the disabled driver community in a manner that centers these individuals. Such research should yield results that more authentically capture the experience (or lack thereof) of disabled individuals driving with and making use of ADAS. Additionally, longitudinal research is necessary to support extended observation of real-world ADAS use by disabled drivers across driving environments and their disability-related functional states, which are often transient
Pedestrian-Vehicle Interaction Data Dictionary and Analysis Protocol: Design, Development, and Pilot Application on Two Naturalistic Driving Databases
Mabry, J. Erin; Wotring, Brian; McClafferty, Julie A.; Soccolich, Susan A.; Boucher, Ben (2024-08-09)
Pedestrian conflicts with vehicles continue to be a serious problem in the United States. Unlike vehicle occupants, pedestrians do not have the protection of airbags, a steel structure surrounding them, or other vehicle safety technologies; their resultant vulnerability places them at higher risk of injury when involved in a traffic crash or conflict. Examining pedestrian behavior in a variety of settings and interaction severity levels supports research goals to improve pedestrian safety. The goals of this study were to: 1. Create an inclusive dictionary of video data analysis variables that details and describes interactions between pedestrians and motorized vehicles; and 2. Develop and pilot test a Pedestrian-Vehicle Interaction Data Reduction Protocol (PVIP) using existing naturalistic driving datasets. Implementing the PVIP confirmed that coding elements related to the interaction response between the pedestrian and vehicle from each perspective, and according to the three epoch stages (i.e., leading up to, during, and following the interaction), was critical for characterizing the entire interaction with consideration of all viewpoints. Pedestrian behaviors, locations, communication strategies, distractions, impairments, and glance behaviors were observed and coded at each stage of the epoch to account for behavioral, sensory-related, and positional changes of the pedestrian occurring over the course of the interaction that could impact the outcome. Similarly, coding the vehicle maneuver, driver behaviors, driving-related tasks, and glance behavior across the interaction epoch may be important elements to consider for pedestrian safety. Pedestrian location across the epoch was also an important variable in the pilot analyses. This study is the first of its kind to design, develop, and systematically apply a comprehensive, video-based, and pedestrian-centric data reduction protocol to NDS data to explore and describe interactions between pedestrians and vehicles for better understanding of pedestrian safety. The output of this project is a comprehensive and systematic PVIP that can be used to characterize pedestrian-vehicle interactions and behaviors. The protocol is organized so that researchers may select questions or groups of questions that are applicable for their specific research objectives in an à la carte fashion to create a focused protocol that fully explores a pedestrian-vehicle scenario using available data.
Creating a Dataset of Naturalistic Ambulance Driving: A Pilot Study of Two Ambulances
Valente, Jacob T.; Terranova, Paolo; Perez, Miguel A. (National Surface Transportation Safety Center for Excellence, 2024-08-02)
Motor vehicle collisions (MVCs) are an everyday occurrence in the United States. This pressing transportation and health care topic affects millions of citizens each year, and in many cases may result in fatality or lifelong injury complications. Despite best efforts, and notable success, to improve the frequency and severity of MVCs, these events are still a prevalent issue. In the wake of an MVC, crash occupants rely on emergency responders to quickly respond to the scene, control hazards, and administer necessary medical care. Efficiency within the emergency response event, to an MVC or some other medical care need, is contingent on a properly working transportation system, allowing emergency medical services (EMS) to travel to and from scenes both quickly and safely. Previous research has revealed that complex interactions with other road users not only hinders emergency response efficiency, but often results in hazardous and dangerous interactions on roadways. To capture these complex interactions from a firsthand perspective, this report details a naturalistic driving study that involved two ambulances and the subsequent dataset that was generated, which is the first of its kind. A custom data acquisition system was used to collect four external and three internal video perspectives on each vehicle, with continuous vehicle data that included vehicle speed, GPS location, and emergency system activation (i.e., emergency light or siren use). Following data collection, the dataset was summarized in the context of each participating agency, the consented drivers, trip type (emergent vs. non-emergent), trip duration, trip distance, and the time of day that the trip took place. The dataset was also processed through a map-matching algorithm that utilized the collected GPS data to provide additional context, including posted speed limit road classification. Finally, the dataset was subsampled to assess and interpret other road user behavior during emergent trips. The work outlined in this report serves as the foundation for additional research that could be leveraged from this dataset, as this dataset is intended to support the inquiry of future research questions within the scope of emergency vehicle operation and transportation. Additionally, some findings of this study and their implications apply beyond the scope of emergency MVC response and may be related more broadly to emergency response for all first responders and emergency events.

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