Browsing by Author "Perez, Miguel A."
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- Assessing Alternate Approaches for Conveying Automated Vehicle IntentionsBasantis, Alexis Rae (Virginia Tech, 2019-10-30)Objectives: Research suggests the general public has a lack of faith in highly automated vehicles (HAV) stems from a lack of system transparency while in motion (e.g., the user not being informed on roadway perception or anticipated responses of the car in certain situations). This problem is particularly prevalent in public transit or ridesharing applications, where HAVs are expected to debut, and when the user has minimal training on, and control over, the vehicle. To improve user trust and their perception of comfort and safety, this study aimed to develop more detailed and tailored human-machine interfaces (HMI) aimed at relying automated vehicle intended actions (i.e., "intentions") and perceptions of the driving environment to the user. Methods: This project developed HMI systems, with a focus on visual and auditory displays, and implemented them into a HAV developed at the Virginia Tech Transportation Institute (VTTI). Volunteer participants were invited to the Smart Roads at VTTI to experience these systems in real-world driving scenarios, especially ones typically found in rideshare or public transit operations. Participant responses and opinions about the HMIs and their perceived levels of comfort, safety, trust, and situational awareness were captured via paper-based surveys administered during experimentation. Results: There was a considerable link found between HMI modality and users' reported levels of comfort, safety, trust, and situational awareness during experimentation. In addition, there were several key behavioral factors that made users more or less likely to feel comfortable in the HAV. Conclusions: Moving forward, it will be necessary for HAVs to provide ample feedback to users in an effort to increase system transparency and understanding. Feedback should consistently and accurately represent the driving landscape and clearly communicate vehicle states to users.
- Assessing Effects of Object Detection Performance on Simulated Crash Outcomes for an Automated Driving SystemGalloway, Andrew Joseph (Virginia Tech, 2023-07-11)Highly Automated Vehicles (AVs) have the capability to revolutionize the transportation system. These systems have the possibility to make roads safer as AVs do not have limitations that human drivers do, many of which are common causes of vehicle crashes (e.g., distraction or fatigue) often defined generically as human error. The deployment of AVs is likely to be very gradual however, and there will exist situations in which the AV will be driving in close proximity with human drivers across the foreseeable future. Given the persistent crash problem in which the makority of crashes are attributed to driver error, humans will continue to create potential collision scenarios that an AV will be expected to try and avoid or mitigate if developed appropriately. The absence of unreasonable risk in an AVs ability to comprehend and react in these situations is referred to as operational safety. Unlike advanced driver assistance systems (ADAS), highly automated vehicles are required to perform the entirety of the dynamic driving task (DDT) and have a greater responsibility to achieve a high level of operational safety. To address this concern, scenario-based testing has increasingly become a popular option for evaluating AV performance. On a functional level, an AV typically consists of three basic systems: the perception system, the decision and path planning system, and vehicle motion control system. A minimum level of performance is needed in each of these functional blocks to achieve an adequate level of operational safety. The goal of this study was to investigate the effects that perception system performance (i.e., target object state errors) has on vehicle operational safety in collision scenarios similar to that created by human drivers. In the first part of this study, recent annual crash data was used to define a relevant crash population of possible scenarios involving intersections that an AV operating as an urban taxi may encounter. Common crash maneuvers and characteristics were combined to create a set of testing scenarios that represent a high iii percentage of the overall crash population. In the second part of this study, each test scenario was executed using an AV test platform during closed road testing to determine possible real-world perception system performance. This provided a measure of the error in object detection measurements compared to the ideal (i.e., where a vehicle was detected to be compared to where it actually was). In the third part of this study, a set of vehicle simulations were performed to assess the effect of perception system performance on crash outcomes. This analysis simulated hypothetical crashes between an AV and one other collision partner. First an initial worst-case impact configuration was defined and was based on injury outcomes seen in crash data. The AV was then simulated to perform a variety of evasive maneuvers based on an adaptation of a non-impaired driver model. The impact location and orientation of the collision partner was simulated as two states: one based on the object detection of an ideal perception system and the other based on the object detection of the perception system from the AV platform used during the road testing. For simulations in which the two vehicles contacted each other, a planar momentum-impulse model was used for impact modeling and injury outcomes were predicted using an omni-directional injury model taken from recent literature. Results from this study indicate that errors in perception system measurements can change the perceived occupant injury risk within a crash. Sensitivity was found to be dependent on the specific crash type as well as what evasive maneuver is taken. Sensitivities occurred mainly due to changes in the principal direction of force for the crash and the interaction within the injury risk prediction curves. In order to achieve full operational safety, it will likely be important to understand the influence that each functional system (perception, decision, and control) may have on AV performance in these crash scenarios.
- Augmented Reality Pedestrian Collision Warning: An Ecological Approach to Driver Interface Design and EvaluationKim, Hyungil (Virginia Tech, 2017-10-17)Augmented reality (AR) has the potential to fundamentally change the way we interact with information. Direct perception of computer generated graphics atop physical reality can afford hands-free access to contextual information on the fly. However, as users must interact with both digital and physical information simultaneously, yesterday's approaches to interface design may not be sufficient to support the new way of interaction. Furthermore, the impacts of this novel technology on user experience and performance are not yet fully understood. Driving is one of many promising tasks that can benefit from AR, where conformal graphics strategically placed in the real-world can accurately guide drivers' attention to critical environmental elements. The ultimate purpose of this study is to reduce pedestrian accidents through design of driver interfaces that take advantage of AR head-up displays (HUD). For this purpose, this work aimed to (1) identify information requirements for pedestrian collision warning, (2) design AR driver interfaces, and (3) quantify effects of AR interfaces on driver performance and experience. Considering the dynamic nature of human-environment interaction in AR-supported driving, we took an ecological approach for interface design and evaluation, appreciating not only the user but also the environment. The requirement analysis examined environmental constraints imposed on the drivers' behavior, interface design translated those behavior-shaping constraints into perceptual forms of interface elements, and usability evaluations utilized naturalistic driving scenarios and tasks for better ecological validity. A novel AR driver interface for pedestrian collision warning, the virtual shadow, was proposed taking advantage of optical see-through HUDs. A series of usability evaluations in both a driving simulator and on an actual roadway showed that virtual shadow interface outperformed current pedestrian collision warning interfaces in guiding driver attention, increasing situation awareness, and improving task performance. Thus, this work has demonstrated the opportunity of incorporating an ecological approach into user interface design and evaluation for AR driving applications. This research provides both basic and practical contributions in human factors and AR by (1) providing empirical evidence furthering knowledge about driver experience and performance in AR, and, (2) extending traditional usability engineering methods for automotive AR interface design and evaluation.
- Automated Vehicle Crash Rate Comparison Using Naturalistic DataBlanco, Myra; Atwood, Jon; Russell, Sheldon M.; Trimble, Tammy E.; McClafferty, Julie A.; Perez, Miguel A. (Virginia Tech Transportation Institute, 2016-01-08)This study assessed driving risk for the United States nationally and for the Google Self-Driving Car project. Driving safety on public roads was examined in three ways. The total crash rates for the Self-Driving Car and the national population were compared to (1) rates reported to the police, (2) crash rates for different types of roadways, and (3) scenarios that give rise to unreported crashes. First, crash rates from the Google Self-Driving Car project per million miles driven, broken down by severity level were calculated. The Self-Driving Car rates were compared to rates developed using national databases which draw upon police-reported crashes and rates estimated from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS). Second, SHRP 2 NDS data were used to calculate crash rates for three levels of crash severity on different types of roads, broken down by the speed limit and geographic classification (termed “locality” in the study; e.g., urban road, interstate). Third, SHRP 2 NDS data were again used to describe various scenarios related to crashes with no known police report. This analysis considered whether such factors as driver distraction or impairment were involved, or whether these crashes involved rear-end collisions or road departures. Crashes within the SHRP 2 NDS dataset were ranked according to severity for the referenced event/incident type(s) based on the magnitude of vehicle dynamics (e.g., high Delta-V or acceleration), the presumed amount of property damage (less than or greater than $1,500, airbag deployment), knowledge of human injuries (often unknown in this dataset), and the level of risk posed to the drivers and other road users (Antin, et al., 2015; Table 1). Google Self-Driving Car crashes were also analyzed using the methods developed for the SHRP 2 NDS in order to determine crash severity levels and fault (using these methods, none of the vehicles operating in autonomous mode were deemed at fault in crashes).
- Behavioral Adaptation to Driving Automation Systems: Guidance for Consumer EducationNoble, Alexandria Marie (Virginia Tech, 2020-04-15)Researchers have postulated that the implementation of driving automation systems could reduce the prevalence of driver errors, or at least mitigate the severity of their consequences. While driving automation systems are becoming increasingly common on new vehicles, drivers seem to know very little about them. The following dissertation describes an investigation of driver behavior and behavioral adaptation while using driving automation systems in order to improve consumer education and training. This dissertation uses data collected from test track environments and two naturalistic driving studies, the Virginia Connected Corridor 50 (VCC50) Vehicle Naturalistic Driving Study and the NHTSA Level 2 Naturalistic Driving Study (L2 NDS), to investigate driver behavior with driving automation systems and make suggestions for modifications to current consumer education practices. Results from the test track study indicated that while training strategy elicited limited differences in knowledge and no difference in driver behaviors or attitudes, operator behaviors and attitudes were heavily influenced by time and experience with the driving automation. The naturalistic assessment of VCC50 data showed that drivers tended to activate systems more frequently in appropriate roadway environments. However, drivers spent more time looking away from the road while driving automation systems were active and drivers were more likely be observed browsing on their cell phones while using driving automation systems. The analysis of L2 NDS showed that drivers' time gap preferences changes as drivers gain experience using the driving automation systems. Additionally, driver eye glance behavior was significantly different with automation use and indicated the potential for an adaptive trend with increased exposure to the system for both glances away from the roadway and glances to the instrument panel. The penultimate chapter of this work presents training guidelines and recommendations for consumer education with driving automation systems based on this and other research that has been conducted on driver interaction with driving automation systems. The results of this research indicate that driver training should be a key focus in future efforts to ensure the continued safe use of driving automation systems as they continue to emerge in the vehicle fleet.
- Characterizing Bicyclists Behavior in Overtaking Scenarios Over Different Road InfrastructuresCrump IV, Eugene Raymond (Virginia Tech, 2024-06-07)Fatal vehicle-bicycle crashes have increased in the United States while cyclist crashes often go unreported. The underreporting of all cyclist crashes results in the overall pre-crash behavior of the cyclists being unknown. What is known is that the most fatal bicycle crash scenario occurs when a vehicle performs an overtaking maneuver. It is crucial to find effective strategies to mitigate these crashes. Vision Zero aims to eliminate all traffic fatalities and disabling injuries by the year 2050 through the implementation of the safe system approach. One of their approaches is using active safety systems like bicycle detecting automatic emergency braking. The purpose of this study was to characterize bicyclist behavior to enhance the crash avoidance potential of advanced driver assistance systems and improve safety for cyclists. An analysis on fatal crashes involving bicyclists was conducted to determine scenarios for testing bicyclist-vehicle interactions on roadways using virtual reality (VR). VR testing was conducted to capture and analyze bicyclist dynamics. Most fatal bicycle crashes occurred when motorists overtook cyclists, especially when cyclists are travelling in a travel lane in the same direction as traffic. These crashes often happen in densely populated areas with favorable weather conditions. This information was used to construct scenarios representing common fatal bicycle crash scenarios. From the analysis, four scenarios were developed. The first scenario was an overtaking scenario with the cyclist traveling in the same direction as traffic, in a travel lane without a bicycle lane or shoulder. The second, third and fourth scenarios were variations of the first to include a bike lane, shoulder, and both a bike lane and a shoulder to analyze the behavior difference due to the inclusion of each. Participants were immersed in a VR simulator that used the combination of a VR headset and a custom-built stationary bicycle. Eighteen individuals were recruited with an average age of 22.7 years. Participants experienced all four scenarios, and their speed, glance, lane position, and standard deviation of lane position were collected and analyzed. The speed for each road type and overtaking phase did not vary significantly, with an average of 4.9 m/s. In the case where there was neither a bike lane or a shoulder, the cyclists looked towards the vehicle more than the other scenarios. As for the lane position, the scenario where the cyclist had neither a shoulder or a bike lane, led to a closer vehicle-bicycle relative position than the other three scenarios. As for standard deviation of lane position, the road with neither a shoulder or bike lane had the largest interquartile range (IQR) and average and the road with both a shoulder and bike lane had the smallest IQR. This implies a lower predictability of the cyclist's movements when they are riding on a roadway with no support lane. Following the testing, participants rated the perceived realism and interactiveness of the VR world and their comfort in each road design. Most of the participants mentioned that having some allocated space felt more comfortable and lowered their sense of danger. To enhance cyclist safety, adopting Euro NCAP testing for AEB systems in the US is recommended. This form of testing could lead to improvements in AEB systems, reducing crashes with cyclists and injury severity. In terms of road infrastructure improvements increasing the number of bike lanes, adding wider shoulders, or widening lanes could also enhance cyclist safety on roadways.
- Characterizing Level 2 Automation in a Naturalistic Driving FleetPerez, Miguel A.; Terranova, Paolo; Metrey, Mariette; Bragg, Haden; Britten, Nicholas (Safe-D University Transportation Center, 2024-01)Introducing automation into the vehicle fleet disrupts how vehicles operate and potentially affects what drivers do with these features and expect from vehicle performance. Therefore, it is imperative to study driver adaptations in response to these innovations. This investigation leveraged 47 vehicles from the Virginia Tech Transportation Institute Level 2 (L2) Naturalistic Driving Study to analyze driver behavior with L2 automation features. Results showed no sizeable differences between periods of L2 feature usage and general driving periods with respect to time-of-day and calendar-related metrics. Most L2 feature usage occurred on motorways, following design expectations. L2 features were activated for 7.2 minutes in trips lasting an average of 22.8 minutes, or about 32% of the L2 trip duration. Driver-initiated overrides were predominantly done by braking or accelerating the vehicle, with steering-based overrides being minimal and likely involving lane changes without using a turn signal. Intervention requests were the most common takeover request, followed by requests due to insufficient driver hand contact with the steering wheel. Findings suggest that as L2 features penetrate the U.S. fleet in non-luxury consumer vehicles, system usage will be common and comparable with previous findings for luxury offerings. While evidence of potential system misuse was observed, future work may further operationalize system misuse and assess the prevalence of such behaviors.
- The Crash Injury Risk to Rear Seated Passenger Vehicle OccupantsTatem, Whitney M. (Virginia Tech, 2020-01-22)Historically, rear seat occupants have been at a lower risk of serious injury and fatality in motor vehicle crashes than their front seat counterparts. However, many passive safety advancements that have occurred over the past few decades such as advanced airbag and seatbelt technology primarily benefit occupants of the front seat. Indeed, safety for front seat occupants has improved drastically in the 21st century, but has it improved so much that the front seat is now safer than the rear? Today, rear-seated occupants account for 10% of all passenger vehicle fatalities. In this era focused on achieving zero traffic deaths, the safety of rear-seated occupants must be further addressed. This dissertation analyzed U.S. national crash data to quantify the risk of injury and fatality to rear-seated passenger vehicle occupants while accounting for the influence of associated crash, vehicle, and occupant characteristics such as crash severity, vehicle model year, and occupant age/sex. In rear impacts, the risk of moderate-to-fatal injury was greater for rear-seated occupants than their front-seated counterparts. In high-severity rear impact crashes, catastrophic occupant compartment collapse can occur and carries with it a great fatality risk. In frontal impacts, there is evidence that the rear versus front seat relative risk of fatality has been increasing in vehicle model years 2007 and newer. Rear-seated occupants often sustained serious thoracic, abdomen, and/or head injuries that are generally related to seatbelt use. Seatbelt pretensioners and load limiters – commonplace technology in the front seating positions – aim to mitigate these types of injuries but are rarely provided as standard safety equipment in the rear seats of vehicles today. Finally, in side impacts, injury and fatality risks to rear- and front-seated occupants are more similar than in the other crash modes studied, though disparities in protection remain, especially in near-side vehicle-to-vehicle crashes. Finally, this work projects great injury reduction benefits if a rear seat belt reminder system were to be widely implemented in the U.S. vehicle fleet. This dissertation presents a comprehensive investigation of the factors that contribute to rear-seated occupant injury and/or fatality through retrospective studies on rear, front, and side impacts. The overall goal of this dissertation is to better quantify the current risk of injury to rear-seated occupants under a variety of crash conditions, compare this to the current risk to front-seated occupants, and, when possible, identify how exactly injuries are occurring and ways in which they may be prevented in the future. The findings can benefit automakers who seek to improve the effectiveness of rear seat safety systems as well as regulatory agencies seeking to improve was vehicle tests targeting rear seat passenger vehicle safety.
- Creating a Dataset of Naturalistic Ambulance Driving: A Pilot Study of Two AmbulancesValente, 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.
- A Dataset of Vehicle and Pedestrian Trajectories from Normal Driving and Crash Events in One Year of Virginia Traffic Camera DataBareiss, Max G. (Virginia Tech, 2023-06-07)Traffic cameras are those cameras operated with the purpose of observing traffic, often streaming video in real-time to traffic management centers. These camera video streams allow transportation authorities to respond to traffic events and maintain situational awareness. However, traffic cameras also have the potential to directly capture crashes and conflicts, providing enough information to perform reconstruction and gain insights regarding causation and remediation. Beyond crash events, traffic camera video also offers an opportunity to study normal driving. Normal driver behavior is important for traffic planners, vehicle designers, and in the form of numerical driver models is vital information for the development of automated vehicles. Traffic cameras installed by state departments of transportation have already been placed in locations relevant to their interests. A wide range of driver behavior can be studied from these locations by observing vehicles at all times and under all weather conditions. Current systems to analyze traffic camera video focus on detecting when traffic events occur, with very little information about the specifics of those events. Prior studies into traffic event detection or reconstruction used 1-7 cameras placed by the researchers and collected dozens of hours of video. Crashes and other interesting events are rare and cannot be sufficiently characterized by camera installations of that size. The objective of this dissertation was to explore the utility of traffic camera data for transportation research by modeling and characterizing crash and non-crash behavior in pedestrians and drivers using a captured dataset of traffic camera video from the Commonwealth of Virginia, named the VT-CAST (Virginia Traffic Cameras for Advanced Safety Technologies) 2020 dataset. A total of 6,779,726 hours of traffic camera video was captured from live internet streams from December 17, 2019 at 4:00PM to 11:59PM on December 31, 2020. Video was analyzed by a custom R-CNN convolutional neural network keypoint detector to identify the locations of vehicles on the ground. The OpenPifPaf model was used to identify the locations of pedestrians on the ground. The location, pan, tilt, zoom, and altitude of each traffic camera was reconstructed to develop a mapping between the locations of vehicles and pedestrians on-screen and their physical location on the surface of the Earth. These physical detections were tracked across time to determine the trajectories on the surface of the Earth for each visible vehicle and pedestrian in a random sample of the captured video. Traffic camera video offers a unique opportunity to study crashes in-depth which are not police reported. Crashes in the traffic camera video were identified, analyzed, and compared to nationally representative datasets. Potential crashes were identified during the study interval by inspecting Virginia 511 traffic alerts for events which occurred near traffic cameras and impacted the flow of traffic. The video from these cameras was manually reviewed to determine whether a crash was visible. Pedestrian crashes, which did not significantly impact traffic, were identified from police accident reports (PARs) as a separate analysis. A total of 292 crashes were identified from traffic alerts, and six pedestrian crashes were identified from PARs. Road departure and rear-end crashes occurred in similar proportions to national databases, but intersection crashes were underrepresented and severe and rollover cases were overrepresented. Among these crashes, 32% of single-vehicle crashes and 50% of multi-vehicle crashes did not appear in the Virginia crash database. This finding shows promise for traffic cameras as a future data source for crash reconstruction, indicating traffic cameras are a capable tool to study unreported crashes. The safe operation of autonomous vehicles requires perception systems which make accurate short-term predictions of driver and pedestrian behavior. While road user behavior can be observed by the autonomous vehicles themselves, traffic camera video offers another potential information source for algorithm development. As a fixed roadside data source, these cameras capture a very large number of traffic interactions at a single location. This allows for detailed analyses of important roadway configurations across a wide range of drivers. To evaluate the efficacy of this approach, a total of 58 intersections in the VT-CAST 2020 dataset were sampled for driver trajectories at intersection entry, yielding 58,180 intersection entry trajectories. K-means clustering was used to group these trajectories into a family of 45 trajectory clusters. Likely as a function of signal phase, distinct groups of accelerating, constant speed, and decelerating trajectories were present. Accelerating and decelerating trajectories each occurred more frequently than constant speed trajectories. The results indicate that roadside data may be useful for understanding broad trends in typical intersection approaches for application to automated vehicle systems or other investigations; however, data utility would be enhanced with detailed signal phase information. A similar analysis was conducted of the interactions between drivers and pedestrians. A total of 35 crosswalks were identified in the VT-CAST 2020 dataset with sufficient trajectory information, yielding 1,488 trajectories of drivers interacting with pedestrians. K-means clustering was used to group these trajectories into a family of 16 trajectory clusters. Distinct groups of accelerating, constant speed, and decelerating trajectories were present, including trajectory clusters which described vehicles slowing down around pedestrians. Constant speed trajectories occurred the most often, followed by accelerating trajectories and decelerating trajectories. As with the prior investigation, this finding suggests that roadside data may be used in the development of driver-pedestrian interaction models for automated vehicles and other use cases involving a combination of pedestrians and vehicles. Overall, this dissertation demonstrates the utility of standard traffic camera data for use in traffic safety research. As evidence, there are already three current studies (beyond this dissertation) using the video data and trajectories from the VT-CAST 2020 dataset. Potential future studies include analyzing the mobile phone use of pedestrians, analyzing mid-block pedestrian crossings, automatically performing roadway safety assessments, considering the behavior of drivers following congested driving, evaluating the effectiveness of work zone hazard countermeasures, and understanding roadway encroachments.
- Description of the SHRP 2 Naturalistic Database and the Crash, Near-Crash, and Baseline Data SetsHankey, Jonathan M.; Perez, Miguel A.; McClafferty, Julie A. (Virginia Tech Transportation Institute, 2016-04)The focus of this project was to identify and prepare crash, near-crash, and baseline data sets extracted from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) trip files, then to make that information available to researchers for use in their analysis projects. A dozen trigger algorithms were executed on 5,512,900 trip files in the SHRP 2 NDS, and a manual validation of these algorithms identified 1,549 crashes and 2,705 near-crashes. A longitudinal deceleration-based algorithm produced the highest percentage of valid crashes and near-crashes. Baselines were selected via a random sample stratified by participant and proportion of time driven. Triggered epochs and the resulting crashes and near-crashes were reviewed and analyzed by a large team of data reductionists and quality control coordinators following a rigorous training, testing, and monitoring protocol. As a result, 20,000 baselines, including all drivers in the SHRP 2 NDS, were prepared and are recommended for researchers using a case-cohort design. An additional 12,586 baselines are also available for researchers who may require more power in their analyses but are able to forego a fully proportional representation of all drivers in the study. Researchers using this data set are encouraged to review the data dictionaries on the InSight website prior to doing analysis and to be particularly careful in selecting the best subset of crashes, near-crashes, and baselines that informs their research questions.
- Differences in Balance and Limb Loading Symmetry in Postpartum and Nulliparous Women During Childcare Related ActivitiesLibera, Theresa L. (Virginia Tech, 2024-10-02)Every year, over 3.5 million women give birth in the United States, with about 67.9% delivering vaginally. Over 80% of postpartum (PP) women experience chronic pain in the pelvis, lower back, hip, and legs at 24 weeks after birth, and 20% continue to experience these issues 3 years later. PP women often face pelvic instability and weakness, which disturb balance and lead to asymmetric loading in the pelvis and legs. This imbalance makes daily tasks, such as lifting and carrying a car seat during childcare, more difficult, and increases the risk of chronic pain and injury. This study aimed to explore how different groups – PP and nulliparous (NP) women – and different ways of holding a car seat while standing – no holding, symmetrical holding with two hands in front, and asymmetrical holding with one arm by the side – affect balance and limb loading symmetry. Results showed that postpartum women struggled more with balance as the task became more challenging, with asymmetrical holding showing large differences between groups. PP women also exhibited greater asymmetric limb loading compared to NP women with asymmetrical holding creating the greatest level of asymmetric limb loading. The study also aimed to explore how the two groups – PP and NP – and the different ways of lifting a car seat – symmetrically and asymmetrically – affect balance and limb loading. Both groups had more asymmetric limb loading and worse balance with asymmetrical lifting, though NP women showed larger movements during asymmetrical lifting, likely reflecting the movement of the body during the condition. These results highlight the importance to further research balance and limb loading in PP compared to NP women. Understanding whether pelvic instability and weakness may contribute to differences in balance and limb loading is crucial as it may help explain how and why postpartum women face higher risk of injury and chronic pain. Ultimately, such work may find ways to improve postpartum health during daily activities.
- Distraction Index Framework: Final ReportPerez, Miguel A.; Hankey, Jonathan M. (National Surface Transportation Safety Center for Excellence, 2013-03-01)The usage of a radio while driving has long been considered socially acceptable. There is recent concern, however, about radio usage, in its ever-changing context, remaining a relatively low-risk activity to perform while driving. This investigation examined how often drivers with access to an advanced and novel infotainment system for about four weeks were involved in crash and near crash situations. Results suggest a trend of very slight overrepresentation of infotainment system use in near crash events. Furthermore, use of infotainment systems had measurable demands on the driver's visual resources and tended to result in a reduced propensity of response to unexpected events on the forward roadway, albeit the use had limited or no measurable effect on the control of the vehicle.
- Driver Behavior in Car Following - The Implications for Forward Collision AvoidanceChen, Rong (Virginia Tech, 2016-07-13)Forward Collision Avoidance Systems (FCAS) are a type of active safety system which have great potential for rear-end collision avoidance. These systems use either radar, lidar, or cameras to track objects in front of the vehicle. In the event of an imminent collision, the system will warn the driver, and, in some cases, can autonomously brake to avoid a crash. However, driver acceptance of the systems is paramount to the effectiveness of a FCAS system. Ideally, FCAS should only deliver an alert or intervene at the last possible moment to avoid nuisance alarms, and potentially have drivers disable the system. A better understanding of normal driving behavior can help designers predict when drivers would normally take avoidance action in different situations, and customize the timing of FCAS interventions accordingly. The overall research object of this dissertation was to characterize normal driver behavior in car following events based on naturalistic driving data. The dissertation analyzed normal driver behavior in car-following during both braking and lane change maneuvers. This study was based on the analysis of data collected in the Virginia Tech Transportation Institute 100-Car Naturalistic Driving Study which involved over 100 drivers operating instrumented vehicles in over 43,000 trips and 1.1 million miles of driving. Time to Collision in both braking and lane change were quantified as a function of vehicle speed and driver characteristics. In general, drivers were found to brake and change lanes more cautiously with increasing vehicle speed. Driver age and gender were found to have significant influence on both time to collision and maximum deceleration during braking. Drivers age 31-50 had a mean braking deceleration approximately 0.03 g greater than that of novice drivers (age 18-20), and female drivers had a marginal increase in mean braking deceleration as compared to male drivers. Lane change maneuvers were less frequent than braking maneuvers. Driver-specific models of TTC at braking and lane change were found to be well characterized by the Generalized Extreme Value distribution. Lastly, driver's intent to change lanes can be predicted using a bivariate normal distribution, characterizing the vehicle's distance to lane boundary and the lateral velocity of the vehicle. This dissertation presents the first large scale study of its kind, based on naturalistic driving data to report driver behavior during various car-following events. The overall goal of this dissertation is to provide a better understanding of driver behavior in normal driving conditions, which can benefit automakers who seek to improve FCAS effectiveness, as well as regulatory agencies seeking to improve FCAS vehicle tests.
- Effectiveness of Automatic Emergency Braking for Protection of Pedestrians and Bicyclists in the U.S.Haus, Samantha Helen (Virginia Tech, 2021-11-16)In the United States, there were 36,560 traffic-related fatalities in 2018, of which 20% were pedestrians, bicyclists, and other vulnerable road users (VRUs) [1]. Vulnerable road users are non-vehicle occupants who, because they are not enclosed in a vehicle, are at higher risk of injury in traffic crashes. While overall traffic fatalities in the US have been decreasing, pedestrian and bicyclist fatalities have been trending upward. Vehicle-based active safety features could avoid or mitigate crashes with VRUs, but are highly dependent on the ability to detect a VRU with enough time or distance. This work presents methods to examine the characteristics of vehicle-pedestrian and vehicle-bicycle crashes and near-crashes using a variety of data sources, assess the potential effectiveness of Automatic Emergency Braking (AEB) in avoiding and mitigating VRU crashes through modeling and simulation, and estimate the future benefits of AEB for VRU safety in the United States. Additionally, active safety features are most effective when behavior of VRUs can be anticipated, however, the behavior of pedestrians and bicyclists is notoriously unpredictable. Therefore, an approach to examine and categorize pedestrian behavior in response to near-crashes and crashes events is presented. Overall, findings suggest that AEB has great potential to avoid and mitigate collisions with pedestrians and bicyclists, but it cannot avoid all crashes even when an idealized AEB system is assumed. Most pedestrians and bicyclists were found to be visible for at least one second prior to the crash, but obstructions, the unpredictability of VRUs, and adverse weather/lighting conditions still pose challenges in avoiding and mitigating crashes with VRUs.
- Effectiveness of Compensatory Vehicle Control Techniques Exhibited by Drivers after Arthroscopic Rotator Cuff SurgeryMetrey, Mariette Brink (Virginia Tech, 2023-07-10)Current return-to-drive recommendations for patients following rotator cuff repair (RCR) surgery are not uniform due to a lack of empirical evidence relating driving safety and time-after-surgery. To address the limitations of previous work, Badger et al. (2022) evaluated, on public roads, the driving fitness of patients prior to RCR and at multiple post-operative timepoints. The goal of the Badger, et al. study was to make evidence-based return-to-drive recommendations in an environment with higher fidelity than that of a simulator and not subject to biases inherent to surveys. Badger et al., however, do not fully investigate the driving practices exhibited by subjects, overlooking the potential presence of compensatory driver behaviors. Further investigation of these behaviors through observation of direct driving techniques and practices over time can specifically answer how drivers may modify their behaviors to address a perceived state of impairment. Additionally, the degree of success in vehicle operation by comparing an ideal turn to the path taken by the driver allows for a level of quantification of the effectiveness of the compensatory techniques. Moreover, driver trajectories inferred from the vehicle Controller Area Network (CAN) metrics and from global positioning system (GPS) coordinates are contrasted with the ideal turn to assess minimum requirements for future sensors that are used to make these trajectory comparisons. This investigation leverages pre-existing data collected by the Virginia Tech Transportation Institute (VTTI) and Carilion Clinic as used in the analysis performed by Badger et al. (2022). RCR patients (n=27) executed the same prescribed driving maneuvers and drove the same route in a preoperative state and at 2-, 4-, 6-, and 12-weeks post operation. Behavioral data were annotated to extract key characteristics of interest and related them to relevant vehicle sensor readings. To construct vehicle paths, data was obtained from the on-board data acquisition system (DAS). Behavioral metrics considered the use of ipsilateral vehicle controls, performance of non-primary vehicle tasks, and steering techniques utilized to assess the impact of mobility restrictions due to sling use. Sling use was found to be a significant factor in use of the non-ipsilateral hand associated with the operative extremity (i.e., operative hand) on vehicle functions and, in particular, difficulty with the gear shifting control. Additionally, when considering the performance of non-primary vehicle tasks as assessed through a prescribed visor manipulation, sling use was not a significant factor for the task duration or completion of the task in a fluid motion. Sling use was, however, significant with respect to operative hand position prior to the completion of the visor manipulation: the operative hand was often not on the steering wheel prior to the visor maneuver. In addition, the operative hand was never used to manipulate the visor when the sling was worn. One-handed steering was also more frequent with the presence of the sling. Further behavioral analysis assessed the presence of compensatory behavior exhibited by subjects during periods in which impairment was perceived. Perceived impairment was observed as a function of the different experimental timepoints. Findings indicated a significant decrease in the lateral vehicle jerk during post-operative weeks 6 and 12. Significant differences, however, were not observed in body position alteration to avoid contact with the interior vehicle cabin, in over-the-shoulder checks, and in forward leans during yield and merge maneuvers. Regarding trajectory analysis, sling use did not produce a significant difference in the error metrics between the actual and ideal paths. In completion of turning maneuvers, however, operative extremity was significant for left turns, with greater error against the ideal path observed from those in the left operative cohort compared to those in the right operative cohort. For the right turn, however, operative extremity was not found to be a significant factor. In addition, the GPS data accuracy proved insufficient to support comparison against the ideal path. Overall, findings from this study provide metrics beyond those used in Badger, et al. that can be used in answering when it is safe for rotator cuff repair patients to return-to-drive. With the limited differences observed as a function of study timepoint and sling use, it is recommended that patients are able to safely return-to-drive at two weeks post-operation. If anything, results suggest that overcompensation, as inferred from observation of safer driving behaviors than normal, is present during some experimental timepoints, particularly post-operative week 2.
- Effects of Intersection Lighting Design on Driver Visual Performance, Perceived Visibility, and GlareBhagavathula, Rajaram (Virginia Tech, 2016-01-12)Nighttime intersection crashes account for nearly half of all the intersection crashes, making them a major traffic safety concern. Although providing lighting at intersections has proven to be a successful countermeasure against these crashes, existing approaches to designing lighting at intersections are overly simplified. Current standards are based on recommending lighting levels, but do not account for the role of human vision or vehicle headlamps or the numerous pedestrian-vehicle conflict locations at intersections. For effective intersection lighting design, empirical evidence is required regarding the effects of lighting configuration (part of the intersection illuminated) and lighting levels on nighttime visibility. This research effort had three goals. The first was to identify an intersection lighting design that results in the best nighttime visibility. The second goal was to determine the effect of illuminance on visual performance at intersections. The third goal was to understand the relationships between object luminance, contrast, and visibility. To achieve these goals, three specific configurations were used, that illuminated the intersection approach (Approach), intersection box (Box), and both the intersection approach and box (Both). Each lighting configuration was evaluated under five levels of illumination. Visibility was assessed both objectively (visual performance) and subjectively (perceptions of visibility and glare). Illuminating the intersection box led to superior visual performance, higher perceived visibility, and lower perceived glare. For this same configuration, plateaus in visual performance and perceived visibility occurred between 8 and 12 lux illuminance levels. A photometric analysis revealed that the Box lighting configuration rendered targets in sufficient positive and negative contrasts to result in higher nighttime visibility. Negatively contrast targets aided visual performance, while for targets rendered in positive contrast visual performance was dependent on the magnitude of the contrast. The relationship between pedestrian contrast and perceived pedestrian visibility was more complex, as pedestrians were often rendered in multiple contrast polarities. These results indicate that Box illumination is an effective strategy to enhance nighttime visual performance and perceptions of visibility while reducing glare, and which may be an energy efficient solution as it requires fewer luminaires.
- Emergency Response to Vehicle Collisions: Feedback from Emergency Medical Service ProvidersValente, Jacob T.; Perez, Miguel A. (MDPI, 2020-10-20)(1) Background: The purpose of this study is to identify emergency medical technicians’ perceptions of the most pressing issues that they experience when responding to motor vehicle collisions and record their opinions about what information is needed to improve the efficiency and effectiveness of the care they provide. (2) Methods: Emergency medical technicians participated in one-on-one structured interviews about their experiences responding to motor vehicle collisions. Their feedback on dispatching procedures and protocols, travel to and from the scene, and the response process was collected. (3) Results: Participants reported experiencing difficulties related to lack of or inaccuracies in information, interactions with traffic, incompatibility in communication technology, scene safety, resource management, and obtaining timely notifications of motor vehicle collisions. Regarding the type of information most needed to improve emergency medical response, respondents indicated a desire for additional data related to the vehicle and its occupants. (4) Conclusions: The early and widespread availability of this information is expected to aid emergency responders in coordinating necessary resources faster and more optimally, help service optimization in situations with multiple motor vehicle collisions in close temporal proximity, and improve on-scene safety for first responders and other necessary personnel.
- Empirical Evaluation of Models Used to Predict Torso Muscle Recruitment PatternsPerez, Miguel A. (Virginia Tech, 1999-09-24)For years, the human back has puzzled researchers with the complex behaviors it presents. Principally, the internal forces produced by back muscles have not been determined accurately. Two different approaches have historically been taken to predict muscle forces. The first relies on electromyography (EMG), while the second attempts to predict muscle responses using mathematical models. Three such predictive models are compared here. The models are Sum of Cubed Intensities, Artificial Neural Networks, and Distributed Moment Histogram. These three models were adapted to run using recently published descriptions of the lower back anatomy. To evaluate their effectiveness, the models were compared in terms of their fit to a muscle activation database including 14 different muscles. The database was collected as part of this experiment, and included 8 participants (4 male and 4 female) with similar height and weight. The participants resisted loads applied to their torso via a harness. Results showed the models performed poorly (average R2's in the 0.40's), indicating that further improvements are needed in our current low back muscle activation modeling techniques. Considerable discrepancies were found between internal moments (at L3/L4) determined empirically and measured with a force plate, indicating that the maximum muscle stress selected and/or the anatomy used were faulty. The activation pattern database collected also fills a gap in the literature by considering static loading patterns that had not been systematically varied before.
- Estimating Crash Consequences for Occupantless Automated VehiclesWitcher, Christina; Henry, Scott; McClafferty, Julie A.; Custer, Kenneth; Sullivan, Kaye; Sudweeks, Jeremy D.; Perez, Miguel A. (Virgina Tech Transportation Institute, 2021-02)Occupantless vehicles (OVs) are a proposed application of automated vehicle technology that would deliver goods from merchants to consumers with neither a driver nor passengers onboard. The purpose of this research was to understand and estimate how the increased presence of OVs in the United States fleet may influence crash risk and associated injuries and fatalities. The approach used to estimate potential modifications in crash risk consequences was a counterfactual simulation, where real-world observations were modified as if alternate events had occurred. This analysis leveraged several U.S. national crash databases, along with the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) dataset. The analysis required the derivation of parameters that could be used to modify existing crash estimates as OVs enter the fleet in greater numbers. The team estimated benefit parameters pertaining to (1) the crashes that could be ultimately avoided altogether based on the OV’s smaller size, (2) benefits that could be obtained from the improved crashworthiness characteristics of the OV, and (3) benefits due to the lack of occupants in the OV. Results showed that of the 58,852 fatalities in the national databases examined, a full-scale market penetration of OVs was estimated to reduce fatalities by 34,284, a reduction of 58.2%. Most of this reduction (83%) would come from the lack of occupants in the OVs. Similarly, of the 6,615,117 injured persons in the national databases examined, a full-scale penetration of OVs was estimated to reduce injured persons by 4,088,935, a reduction of 61.8%. As was observed for fatalities, most of this reduction (72.1%) would come from the lack of occupants in the OVs. The results of this investigation, however, should not be taken as definitive benefit estimates. There are important assumptions inherent in the parameters that were used, and some of these assumptions may not be immediately realized. Rather, the results are meant to support critical thinking into how innovative technologies such as OVs may offer benefits that transcend the typical approaches used in vehicle safety, including passive and active safety measures.
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