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

Permanent URI for this collection

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

http://www.vtti.vt.edu/national/nstsce/

Browse

Now showing 1 - 20 of 23

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.
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.
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.
Current Implementations of Over-the-Air Updates
Bowden, Zeb; Chandler, Jacob (National Surface Transportation Safety Center for Excellence, 2024-07-22)
Over-the-air (OTA) updates have become a significant aspect of the future development of connected vehicles. OEMs can address safety and security concerns, as well as provide a better customer experience, by delivering software improvements to vehicles faster and without requiring a visit to a service center. OTA updates will enable OEMs to address security, safety, and performance concerns much faster, at a lower cost, and allow delivery of updates in a manner more convenient to vehicle owners. From a cybersecurity perspective, this is important, as the number of security vulnerabilities in vehicles continues to grow (Sibros, 2022). Additionally, recalls are increasingly being addressed by software updates, potentially resolving recall issues faster. From March 2022–March 2023, 8.5 million vehicle units were recalled due to software-based or electrical components (Sibros, 2022; Vehicle Recalls, 2023). Advanced driver assistance systems and automated driving systems are continually being developed and improved. OTA updates are an enabling technology that allows improvements to these technologies to be realized on vehicles at a much more rapid pace than in the past. The objective of this report is to understand the capabilities, challenges, and characteristics of implementations of OTA updates in vehicles. This report summarizes and details key findings of OTA updates in current vehicles and focuses on the potential challenges in adopting this new advancement in technology.
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.
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.
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.
Evaluation of the Interaction of Driver Behavior Based on SHRP 2 NDS Data
Guo, Feng; Fang, Youjia (National Surface Transportation Safety Center for Excellence, 2024-07-25)
Driver behavior constitutes a significant factor contributing to traffic crashes. This study examines the relationship between three primary categories of driver behavior—driver distraction, driving errors, and driver impairment—utilizing data from the Second Strategic Highway Research Program Naturalistic Driving Study. The findings offer concrete evidence of interactions among these behaviors, indicating that the presence of one behavior significantly influences the likelihood of another and the occurrence of two high-risk behaviors amplifies crash risk. This study underscores the multifaceted nature of driver behavior and its profound impact on road safety. The findings can provide crucial information for driver education programs, safety countermeasure development, and advanced driving assistance systems.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Temporary Disability: Development of an Evaluation Framework
Werner, Alice; Perez, Miguel A. (National Surface Transportation Safety Center for Excellence, 2024-07-17)
The purpose of this work was to assess the impact of temporary disability on the ease and effectiveness of automobile driving tasks. The main objective of this work was to generate a framework to evaluate the extent to which a given temporary disability, caused by a given primary condition and associated with different symptoms, affects individual driving tasks at any given timepoint following the onset of the temporary disability. Injuries and medical conditions can limit driving ability—from chronic conditions to temporary conditions. Ultimately, the disability produced by these conditions may result in driving restrictions, reliance on compensatory driving techniques, or both. This can be particularly onerous for individuals that live in areas not well served by public transportation. In an ideal analysis of temporary disability, translating temporary disability into its disruptive impacts on components of the driving task (i.e., functional driving domains) should identify affected driving tasks, the degree of impact on driving tasks, and necessary/useful compensatory driving techniques and vehicle modifications, as well as inform return-to-drive guidelines that are not overly conservative. In reality, however, connections between temporary disability symptoms and their effects of specific driving tasks are either incomplete or completely nonexistent. Given this background, the current effort aimed to propose a preliminary framework and model for the systematic evaluation of effects of temporary disability on functional driving domains through a semi-hierarchical task analysis and identification of different elements of the temporary disability’s manifestation that should be considered in such a framework. A cascading model was developed that first associates a temporary disability event with specific temporary disabilities and then qualifies those disabilities into a set of affected capabilities. Finally, the affected capabilities are translated into impacts on specific driving tasks. The output from the model allows the user to understand the hindrance that a temporary disability event may create on someone’s driving so that reasonable return-to-drive guidelines may be generated and/or patients can self-regulate their driving to accommodate for temporarily diminished skills.
Traffic Sign Characteristics for Machine Vision Safety Benefits
Kassing, Andrew; Gibbons, Ronald B.; Li, Eric; Palmer, Matthew; Hamen, Johann; Medina, Alejandra (National Surface Transportation Safety Center for Excellence, 2024-07-03)
Machine vision has become a central technology for the development of automated driving systems and advanced driver assistance systems. To support safe navigation, machine vision must be able to read and interpret roadway signs, which provide regulatory, warning, and guidance information for all road users. Complicating this task, transportation agencies use a large variety of signs, which can have significantly different shapes, sizes, contents, installation methods, and retroreflectivity levels. Additionally, many environmental factors, such as precipitation, fog, dew, and lighting, also affect the visibility and legibility of roadway signs. Understanding how environmental factors and sign conditions affect machine vision performance will be important for transportation agencies to maximize the technology’s safety benefits. Research began by conducting a literature review cataloguing current research concerning roadway sign and visual performance, vehicle vision systems, and sign significance for automated driving. Information and insight gained during the literature review process informed the design and system development of data collection systems. Field data collection was then performed over the course of 3 months in late spring to early summer in 2021. Simultaneously, sign data were harvested using Google Street View and mapped using ArcGIS. Data collected during the experimental trips were then reduced and carefully prepared for analysis. Researchers conducted a thorough data analysis, particularly looking at sign location, viewing distance, sign color, font size, sun position, and illumination, to assess the impact of many environmental and infrastructure factors on the legibility of sign characters. Results showed that blue and brown signage with white legend text provided the best chance of sign character legibility during the daytime; sign characters were easy to read during the day at all three experimental distances (200, 400, and 500 ft), with small characters becoming less legible as view distance increased; daytime legibility decreased as light levels decreased; sign images captured at nighttime illumination levels had poor legibility results; sign characters on overhead signage were found to be more legible and are expected to be identified at a higher rate by vehicle vision systems; and vehicle vision systems should use a high-quality camera capable of taking pictures at night without motion blur.

Browse

Browsing National Surface Transportation Safety Center for Excellence Reports (NSTSCE, VTTI) by Content Type "Technical report"

Results Per Page

Sort Options