Automobile crashes are the largest cause of death for pregnant females and the leading cause of traumatic fetal injury mortality in the United States. The first way to protect the fetus is to protect the mother considering that maternal death has a near 100% fetal loss rate. If the mother survives, protection of the fetus may best be accomplished by preventing placental abruption. Placental abruption, which is the premature separation of the placenta from the uterus, has been shown to account for 50% to 70% of fetal losses in motor vehicle crashes. Since real world crash data for pregnant occupants is limited to a retrospective analysis and pregnant cadaver studies are not feasible, crash test dummies and computational modeling have been utilized to evaluate the risk of adverse fetal outcome. Although pregnant occupant research has progressed with these tools, they are based on limited tissue data. In order to have more accurate research tools, better pregnant tissue material data are needed. Therefore, the purpose of this dissertation is to provide material properties for the placenta and pregnant uterine tissue in dynamic tension. / Ph. D.

This thesis has evaluated the effectiveness of GDL programs both in New Jersey and across the United States using several metrics. The New Jersey GDL program was analyzed because it is considered one of the most stringent programs in the country. It was found that GDL indeed reduces the per capita rate of crashes for teen drivers in New Jersey. However, no statistical difference was seen in the rate of fatalities in teen driver crashes. The per capita rate of violations for 16 and 17 year old drivers was lower after GDL, but the rate of point-carrying violations increased for 19 and 20 year old drivers who were licensed under GDL. The September, 2008 directive by the New Jersey Attorney General banning plea-agreements for teens significantly reduced the rate of violations further for 16 and 17 year old GDL drivers. The factors that led to teen crashes did not change in the United States after GDL. Teen drivers are still prone to distractions and inappropriate behavior while driving. Teen drivers also have higher rates of control loss and road departure crashes when compared to adults. Finally, it was found changes in the number teen driver crashes and fatalities are associated with similar changes in travel exposure. Teen crashes and fatalities have dropped with the implementation of GDL but teen VMT has also dropped. Graduated driver's licensing did not change the reasons for teen driver crashes. Also, it is likely that any reductions in the number of teen crashes or fatalities are associated with reductions in exposure and not changes in teen driver behavior. / Ph. D.

Non-volatile main memory (NVM) technologies are revolutionizing the entire computing stack thanks to their storage-and-memory-like characteristics. The ability to persist data in memory provides a new opportunity to build crash-consistent software without paying a storage stack I/O overhead. A crash-consistent NVM program can recover back to a consistent state from a persistent NVM in the event of a software crash or a sudden power loss. In the presence of a volatile cache, data held in a volatile cache is lost after a crash. So NVM programming requires users to manually control the durability and the persistence ordering of NVM writes. To avoid performance overhead, developers have devised customized persistence mechanisms to enforce proper persistence ordering and atomicity guarantees, rendering NVM programs error-prone. The problem statement of this dissertation is how one can effectively detect persistence bugs from NVM programs. However, detecting persistence bugs in NVM programs is challenging because of the huge test space and the manual consistency validation required. The thesis of this dissertation is that we can detect persistence bugs from NVM programs in a scalable and automatic manner by inferring likely-correctness conditions from programs. A likely-correctness condition is a possible correctness condition, which is a condition a program must maintain to make the program crash-consistent. This dissertation proposes to infer two forms of likely-correctness conditions from NVM programs to detect persistence bugs. The first proposed solution is to infer likely-ordering and likely-atomicity conditions by analyzing program dependencies among NVM accesses. The second proposed solution is to infer likely-linearization points to understand a program's operation-level behavior. Using these two forms of likely-correctness conditions, we test only those NVM states and thread interleavings that violate the likely-correctness conditions. This significantly re- duces the test space required to examine. We then leverage the durable linearizability model to validate consistency automatically without manual consistency validation. In this way, we can detect persistence bugs from NVM programs in a scalable and automatic manner. In total, we detect 47 (36 new) persistence correctness bugs and 158 (113 new) persistence performance bugs from 20 single-threaded NVM programs. Additionally, we detect 27 (15 new) persistence correctness bugs from 12 multi-threaded NVM data structures. / Doctor of Philosophy / Non-volatile main memory (NVM) technologies provide a new opportunity to build crash-consistent software without incurring a storage stack I/O overhead. A crash-consistent NVM program can recover back to a consistent state from a persistent NVM in the event of a software crash or a sudden power loss. NVM has been and will further be used in various computing services integral to our daily life, ranging from data centers to high-performance computing, machine learning, and banking. Building correct and efficient crash-consistent NVM software is therefore crucial. However, developing a correct and efficient crash-consistent NVM program is challenging as developers are now responsible for manually controlling cacheline evictions in NVM programming. Controlling cacheline evictions makes NVM programming error-prone, and detecting persistence bugs that lead to inconsistent NVM states in NVM programs is an arduous task. The thesis of this dissertation is that we can detect persistence bugs from NVM programs in a scalable and automatic manner by inferring likely-correctness conditions from programs. This dissertation proposes to infer two forms of likely-correctness conditions from NVM programs to detect persistence bugs, i.e., likely-ordering/atomicity conditions and likely-linearization points. In total, we detect 47 (36 new) persistence correctness bugs and 158 (113 new) persistence performance bugs from 20 single-threaded NVM programs. Additionally, we detect 27 (15 new) persistence correctness bugs from 12 multi-threaded NVM data structures.

Reducing the number of motor vehicle crashes is one of the major challenges of our times. Current strategies to reduce crash rates can be divided into two groups: identifying risky driving behavior prior to crashes to proactively reduce risk and automating some or all human driving tasks using intelligent vehicle systems such as Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). For successful implementation of either strategy, a deeper understanding of human driving behavior is essential. This dissertation characterizes human driving behavior through an analysis of a large naturalistic driving study and offers four major contributions to the field. First, it describes the creation of the Surface Accelerations Reference, a catalog of all longitudinal and lateral surface accelerations found in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS). SHRP 2 NDS is the largest naturalistic driving study in the world with 34.5 million miles of data collected from over 3,500 participants driving in six separate locations across the United States. An algorithm was developed to detect each acceleration epoch and summarize key parameters, such as the mean and maxima of the magnitude, roadway properties, and driver inputs. A statistical profile was then created for each participant describing their acceleration behavior in terms of rates, percentiles, and the magnitude of the strongest event in a distance threshold. The second major contribution is quantifying the effect of several factors that influence acceleration behavior. The rate of mild to harsh acceleration epochs was modeled using negative binomial distribution-based generalized linear mixed effect models. Roadway speed category, driver age, driver gender, vehicle class, and location were used as fixed effects, and a unique participant identifier was as the random effect. Subcategories of each fixed effect were compared using incident rate ratios. Roadway speed category was found to have the largest effect on acceleration behavior, followed by driver age, vehicle class, and location. This methodology accounts for the major influences while simultaneously ensuring that the comparisons are meaningful and not driven by coincidences of data collection. The third major contribution is the extraction of acceleration-based long-term driving styles and determining their relationship to crash risk. Rates of acceleration epochs experienced on ≤ 30 mph roadways were used to cluster the participants into four groups. The metrics to cluster the participants were chosen so that they represent long-term driving style and not short-term driving behavior being influenced by transient traffic and environmental conditions. The driving style was also correlated to driving risk by comparing the crash rates, near-crash rates, and speeding behavior of the participants. Finally, the fourth major contribution is the creation of a set of interactive analytics tools that facilitate quick characterization of human driving during regular as well as safety-critical driving events. These tools enable users to answer a large and open-ended set of research questions that aid in the development of ADAS and ADS components. These analytics tools facilitate the exploration of queries such as how often do certain scenarios occur in naturalistic driving, what is the distribution of key metrics during a particular scenario, or what is the relative composition of various crash datasets? Novel visual analytics principles such as video on demand have been implemented to accelerate the sense-making loop for the user. / Doctor of Philosophy / Naturalistic driving studies collect data from participants driving their own vehicles over an extended period. These studies offer unique perspectives in understanding driving behavior by capturing routine and rare events. Two important aspects of understanding driving behavior are longitudinal acceleration, which indicates how people speed up or slow down, and lateral acceleration, which shows how people take turns. In this dissertation, millions of miles of driving data were analyzed to create an open access acceleration database representing the driving profiles of thousands of drivers. These profiles are useful to understand and model human driving behavior, which is essential for developing advanced vehicle systems and smart roadway infrastructure. The acceleration database was used to quantify the effect of various roadway properties, driver demographics, vehicle classification, and environmental factors on acceleration driving behavior. The acceleration database was also used to define distinct driving styles and their relationship to driving risk. A set of interactive analytics tools was developed that leverage naturalistic driving data by enabling users to ask a large set of questions and facilitate open-ended analysis. Novel visualization and data presentation techniques were developed to help users extract deeper insight about driving behavior faster than previously exiting tools. These tools will aid in the development and testing of automated driving systems and advanced driver assistance systems.

Each year roughly there are roughly 40,000 traffic-related fatalities. Common roadside objects such as trees, poles, guardrails, embankments, culverts, and fences result account for roughly 46% of these fatalities. Efforts to reduce to injury risk and risk exposure in these crashes have been hampered by the difficulty in performing reconstructions. To address the need for accurate reconstructions in order to assess injury risk, a vehicle-specific stiffness database was added to the WinSmash reconstruction program. This single modification increased the average estimated delta-V by 8% and reduced error from 23% to 13% on average. A method to extend the WinSmash energy-based reconstruction approach to poles and trees that were damaged or broken was implemented to provide delta-V estimates for these crashes. The error of the pole and tree reconstruction component was roughly 44% but still represented a significant step forward for these crashes which previously could not be reconstructed. The use of strong-post w-beam guardrail along roadsides is the primary method by which exposure to risk is reduced. Efforts to model guardrails using finite element methods were hampered by the large number of unknowns and lack of knowledge about the sensitivity of the crash outcome to each variable. Through a parametric study the soil properties and rail mesh density were identified as the most significant influences in simulation outcome. This knowledge was applied to finite element models of damaged guardrail to identify when the damage compromises the guardrail ability to prevent risk exposure. Models of guardrail with rail deflection, missing posts, and missing blockouts identified rail deflection over 6 inches and any number of missing posts as hazardous conditions. The removal of a single blockout was found to be acceptable if not desirable. These findings have far-reaching implications. The enhanced WinSmash reconstruction program has been adopted by NASS/CDS to generate delta-V estimates used by researchers in all areas of transportation research. The identification of hazardous guardrail was of great interest to transportation agencies responsible for prioritizing and performing repairs of damaged guardrail. / Ph. D.

The combination of airbag and seatbelt is considered to be the most effective vehicle safety system. However, despite the widespread availability of airbags and a belt use rate of over 85% U.S. drivers involved in crashes continue to be at risk of serious thoracic injury. One hypothesis is that this risk may be due to the lack of airbag deployment or the airbag \'bottoming-out\' in some cases, causing drivers to make contact with the steering. The objective of this study is to determine the influence of various advanced airbags on occupant injury risk in frontal automobile crash. The analysis is based upon cases extracted from the National Automotive Sampling System Crashworthiness Data System (NASS/CDS) database for case years 1993-2011. The approach was to compare the frontal crash performance of advanced airbags against depowered airbags, first generation airbags, and vehicles with no airbag equipped. NASS/CDS steering wheel deformation measurements were used to identify cases in which thoracic injuries may have been caused due to steering wheel impact and deformation. The distributions of injuries for all cases were determined by body region and injury severity. These distributions were used to compare and contrast injury outcomes for cases with frontal airbag deployment for both belted and unbelted drivers. Among frontal crash cases with belted drivers, observable steering wheel deformation occurred in less than 4% of all cases, but accounted for 29% of all serious-to-fatally injured belted drivers and 28% of belted drivers with serious thoracic injuries (AIS3+). Similarly, observable steering wheel deformation occurred in approximately 13% of all cases with unbelted drivers involved in frontal crashes, but accounted for 58% of serious-to-fatally injured unbelted drivers and 66% of unbelted drivers with serious thoracic injuries. In a frontal crash, the factors which were statistically significant in the probability of steering wheel deformation were: longitudinal delta-V, driver weight, and driver belt status. Seatbelt pre-tensioner and load limiters were not significant factors in influencing steering wheel deformation. Furthermore, belted drivers in vehicles with no airbag equipped were found to have 3 times higher odds of deforming the steering wheel, as compared to driver in similar crash scenario. Similarly, unbelted drivers were found to have 2 times greater odds of deforming the steering wheel in vehicles with no airbags equipped as compared to vehicles with advanced airbag. The result also showed no statistically significant difference in the odds of deforming the steering wheel between depowered and advanced airbag. After controlling for crash severity, and driver weight, the study showed that crashes with steering wheel deformation results in greater odds of injury in almost all body regions for both belted and unbelted drivers. Moreover, steering wheel deformation is more likely to occur in unbelted drivers than belted drivers, as well as higher severity crashes and with heavier drivers. Another potential factor in influencing driver crash injury is the knee airbag. After comparing the odds of injury between vehicles with and without knee airbags equipped, belted drivers in vehicles equipped with knee airbag were found to have statistically smaller odds of injury in the thorax, abdomen, and upper extremity. Similarly, the findings showed that unbelted drivers benefited from knee airbag through statistically significant lower odds of chest and lower extremity injuries. However, the results should be considered with caution as the study is limited by its small sample of vehicles with knee airbags. / Master of Science

This research investigates factors that influence severity of speed-related crashes on mainline roadway segments, with a particular emphasis on comparing single-vehicle and multiple-vehicle incidents and distinguishing between crashes involving legal speed limit violations and those exceeding the maximum safe travel speed as determined by law enforcement. Additionally, it examines significant factors related to classifying a crash as exceeding the maximum safe travel speed. Using crash data from the Traffic Records Electronic Data System (TREDS) for Virginia for 2023, the research employs both Ordinal and Nominal Logistic Regression models for analysis. The findings reveal that higher vehicle speeds before a crash significantly increase crash severity level across all scenarios. Rain and snow/sleet weather conditions exhibit significant impacts on crash outcomes, with adverse conditions often leading to increased severity levels. Roadway characteristics in terms of presence of medians and road surface conditions, are also found to be significant, as are. the driver-related factors of age, safety equipment used, EMS transport after the crash, and vehicle type. The study's comparative analysis between single and multiple vehicles speeding crashes, as well as speeding beyond legal limits and exceeding maximum safe travel speed highlights the contextual differences in crash severity determinants. The findings on classifying crashes as exceeding maximum safe travel speed highlight conditions that influence this designation as well as factors that can lead to inconsistencies in that classification. For example, environmental conditions like rain or snow, certain crash types, and work zone crashes may result in subjective assessments rather than objective determinations. The research offers valuable insights for informing targeted road safety strategies within the Safe System framework – targeted at reducing the severity of speed-related crashes for mainline road segments. The findings support implementing comprehensive strategies that address the complex interplay of speed, road conditions, vehicle characteristics, and driver factors in mitigating crash severity. / Master of Science / This research explores how speeding affects the severity of car crashes, seeking to understand why some accidents are more dangerous than others. By analyzing crash data from Virginia in 2023, the study looks at different types of crash scenarios – those involving just one vehicle and those involving multiple vehicles – and examines how factors like weather, road conditions, vehicle and driver characteristics contribute to the seriousness of these crashes. The research compares crashes where drivers exceed the legal speed limit with those where they drive faster than is safe under the given road conditions. Additionally, it investigates key factors that potentially influence law enforcement at the scene to designate that a crash involves a driver exceeding the safest speed for road and traffic conditions. The findings show that driving at higher speeds before a crash significantly increases the chances of severe injuries or fatalities. The study indicates how weather conditions, design characteristics of roads, or the condition of the road surface, impact crash severity. Driver age and whether drivers were under the influence of alcohol or drugs, and whether vehicle safety equipment like seatbelts were used, are significant in determining the severity of a crash. The findings on classifying crashes as exceeding maximum safe travel speed highlight conditions that influence this designation as well as factors that can lead to inconsistencies in that classification. This research is important because it provides insights for improving road safety.

Lane change or "Side" Crash Avoidance Systems (SCAS) technologies are becoming available to help alleviate the lane change crash problem. They detect lane change crash hazards and warn the driver of the presence of such hazards. This thesis examines driver lane change behavior and evaluates the potential effectiveness of five warning onset rules for lane change or "side" crash avoidance system (SCAS) technologies. The ideal SCAS should warn the driver only when two conditions are met: (1) positive indication of lane change intent and (2) positive detection of a proximal vehicle in the adjacent lane of concern. Together, these two conditions create a crash hazard. The development of SCAS technologies depends largely on an understanding of driver behavior and performance during lane change maneuvers. By quantifying lane change behavior, real world crash hazard scenarios can be simulated. This provides an opportunity to evaluate potential warning onset rules or algorithms of driver intent to change lanes. Five warning onset rules for SCAS were evaluated: turn-signal onset (TSO), minimum separation (MS), line crossing (LC), time-to-line crossing (TLC), and tolerance limit (TL). The effectiveness of each rule was measured by the maximum response time available (tavailable) to avoid a crash for a particular lane change crash scenario, and by the crash outcome, crashed or crash avoided, of a particular lane change crash scenario. / Master of Science

La certification des aéronefs au crash ou à l’atterrissage dur nécessite de concevoir et dimensionner des structureslégères vérifiant les exigences d’absorption d’énergie. Le critère de performance est l’énergie d’absorptionspécifique (Specific Energy Absorption, SEA). Nos travaux expérimentaux et numériques visent une meilleurecompréhension de la contribution favorable ou défavorable des modes de ruine à la stabilité et à l’amplitude del’énergie consommée. Le travail expérimental, réalisé sur des échantillons plaques stratifiées en T700/M21 faible grammage et interlock 55% ou 100%, compare les niveaux et les évolutions des forces d’écrasement avec l’apparition et le maintien desmodes de ruine majeurs que sont l’évasement, les fragmentations en coeur de plis et localisée en bout de pli.L’observation et la mesure des processus dynamiques de fragmentation représentent un verrou contourné ici parune analyse point à point de la synchronisation entre les films des essais et les courbes force-déplacement, et parl’observation post-mortem des échantillons, des débris et des fragments. Les plaques ont une performance àl’écrasement sensible à l’épaisseur des plis et aux vitesses de déformation. Pour les interlocks, c’est le sens detissage qui a le plus d’effet sur l’amplitude et la stabilité de la SEA, et génère un évasement global plus instable.La simulation numérique dynamique transitoire non-linéaire est utilisée comme outil complémentaire de mesureet d’analyse des essais sur plaques T700/M21 [0°/90°]. La morphologie d’écrasement est bien reproduite.L’analyse des processus de ruine à l’échelle du pli fait apparaître l’interaction entre la résistance mécanique encompression transverse du matériau (Yc) et la résistance à la déchirure en cisaillement de la structure (GIIc), etl’articulation et/ou la compétition entre évasement et fragmentation en cœur de pli qui en découlent. La mesurede la contribution des trois modes de ruine dans l’énergie consommée effectuée au travers de l’évolution desseuils de ruine permet de suivre l’évolution correspondante de l’effort d’écrasement. Une étude a été menée surla robustesse du modèle, et permet d’évaluer plus généralement la sensibilité en amplitude et en stabilité de laSEA aux propriétés de résistance mécanique identifiées comme influentes. / The certification of aircrafts to hard landing or crash situations needs to design lightweight structures meetingrequirements in term of energy absorption. The Specific Energy Absorption (SEA) is used to compare theperformance of different structures. Experimental and numerical studies led in our work aim to improve theunderstanding of the influence of ruin modes on the crushing stability and the energy absorption capacity.Crushing experimental tests are carried on low-weight T700/M21 CFRP laminated plates and on 55% or 100%Interlock configurations. The crushing force value and its variations are compared to the proportion of inside plyfragmentation, localized fragmentation and splaying mode observed during the crushing process. The observationand the measure of the dynamic process of fragmentation are lock problems circumvented by two means. First, astep by step observation of synchronized tests’ pictures and force-displacement points is conducted. Second, apost-mortem observation of the specimen and a collect of debris and fragments is carried out. It is shown thatcomposite laminates behaviour is influenced by the ply thickness and the strain-rate parameters. For the Interlock,the woven directions have the most important effect on the SEA value and stability and can produce a globalfragmented splaying with some instability. Nonlinear transient dynamic numerical simulations are used as an additional tool to measure and analyse the experimental tests on T700/M21 [0°/90°] plates. The crushing morphology is correctly reproduced. The analysis of damage at the mesoscale shows the interaction between the mechanical transverse compressive strength of thematerial (Yc) and the shear strength of interfaces between plies (GIIc), and the link and/or the competition betweensplaying and inside ply fragmentation. The measure of the contribution of the three ruin modes in the dissipatedenergy is performed and linked to the variations of the crushing force. A study is carried out on the robustness ofthe model and allows linking the SEA value and stability to the mechanical strength properties identified asinfluential parameters.

One of the principal energy absorbing structural components that influences the crashworthiness of a vehicle is the side-rail, which is also commonly referred to as an s-rail due to its shape that is reminiscent of an “s”. To improve the crashworthiness of a vehicle, in the wake of significant environmental pressures requiring vehicle light-weighting, the parameters that govern the crash response of the s-rail and the implications of light-weight material substitution need to be better understood. In this work, the main parameters that govern the crash response of an s-rail and the variables that influence them were identified and assessed through a combined experimental and numerical modelling programme. In particular, the as-formed properties of aluminium alloy s-rails, due to the tube bending and hydroforming fabrication route were examined. Tube bending, hydroforming and crash experiments were conducted to examine and assess the effects of initial tube thickness, strength, geometry, bend severity, work hardening, thickness changes and residual stresses on the crash response of the s-rail. The forming process variables, springback, thickness, strains, and force and energy response measured in the experiments were used to validate the finite element models developed herein. The validated numerical models of tube bending, hydroforming and crash provided additional insight and also allowed further investigation of the parameters governing the crash response of s-rails. The relevant parameters governing the crash response of s-rails were isolated and the basis for a set of design guidelines, in terms of maximizing energy absorption or minimizing mass, was established. The overall size is the most influential design parameter affecting the energy absorption capability of the s-rail, followed by the initial thickness, material strength, cross-sectional geometry, bend severity and hydroforming process employed, and finally boost in bending. The most significant conclusion made based on this research is that the effects of forming history must be considered to accurately predict the crash response of the s-rail. There are additional conclusions with respect to the tube bending and hydroforming processes, as well as s-rail crash response, that will contribute to improving the design of s-rails for better crashworthiness of vehicles.