This thesis presents the results of a research effort investigating the effect of belt usage reporting errors of National Automotive Sampling System-Crash Data System (NASS-CDS) investigators on injury risk estimates. Current estimates of injury risk are developed under the assumption that NASS-CDS investigators are always accurate at determining seat belt usage. The primary purpose of this research is to determine the accuracy of NASS-CDS investigators using event data recorders (EDRs) as the baseline for accuracy, and then recalculating injury risk estimates based on our findings. The analysis of a 107 EDR dataset, from vehicle tests conducted by the National Highway Traffic Safety Administration (NHTSA) and the Insurance Institute for Highway Safety (IIHS), was conducted to determine the accuracy of Chrysler, Ford, GM and Toyota EDRs. This accuracy was examined by both EDR module type and vehicle make. EDR accuracy was determined for crash delta-V, seat belt buckle status, pre-impact speed, airbag deployment status and front seat position. From this analysis we were able to conclude that EDRs were accurate, within 4.5%, when comparing maximum delta-V of EDRs that recorded the entire crash pulse length. We also determined that EDRs were 100% accurate when reporting driver seat belt status for EDRs that completely recorded the event and recorded a status for the driver's seat belt. All GM, Ford and Chrysler EDRs in our database reported a pre-impact velocity less than 6 mph different than the NHTSA and IIHS reported pre-impact velocities. We also found that all but 2 (101 out of 103) of the GM, Ford, and Toyota EDRs correctly reported airbag deployment status. Lastly we were able to conclude that seat position status was useful in determining when a smaller sized occupant was the driver or right front occupant. EDRs reported seat position of 5% Hybrid III females as "forward" in every case that seat position was recorded for this smaller occupant size. Based on the analysis of seat belt status accuracy, a comparison of NASS-CDS investigator driver seat belt status and EDR driver seat belt status was conducted to determine the accuracy of the NASS-CDS investigators. This same comparison was conducted on reports of driver seat belt status provided by police. We found that NASS-CDS investigators had an overall error of 9.5% when determining driver seat belt status. When the EDR stated that the driver was unbuckled, investigators incorrectly coded buckled in of 29.5% of the cases. When the EDR stated that the driver was buckled, NASS-CDS error was only 1.2%. Police officers were less accurate than NASS-CDS investigators, with an overall error of 21.7%. When the EDR stated that the driver was buckled, police had an error of 2.4%. When the EDR stated that the driver's belt was unbuckled, police had an error of around 69%. In 2008, NASS-CDS investigators reported that drivers had an overall belt usage rate during accidents of 82%. After correcting for the errors we discovered, we estimate that the driver belt buckle status during a crash is around 72.6%. Injury risk estimates and odd ratio point estimates were then calculated for NASS-CDS investigator and EDR buckled versus unbuckled cases. The cases included only frontal collisions in which there was no rollover event or fire. Injury was defined as AIS 2+. The risk ratios and point estimates were then compared between investigators and EDRs. We found that injury risk for unbelted drivers may be over estimated by NASS-CDS investigators. The unbuckled to buckled risk ratio for EDRs was 8%-12% lower than the risk ratio calculated for NASS-CDS investigators. / Master of Science

Event data recorders (EDRs) are an invaluable data source that have begun to, and will increasingly, provide novel insight into motor vehicle crash characteristics. The "black boxes" in automobiles, EDRs directly measure precrash and crash kinematics. This data has the potential to eclipse the many traditional surrogate measures used in vehicle safety that often rely upon assumptions and simplifications of real world crashes. Although EDRs have been equipped in passenger vehicles for over two decades, the recent establishment of regulation has greatly affected the quantity, resolution, duration, and accuracy of the recorded data elements. Thus, there was not only a demand to reestablish confidence in the data, but a need to demonstrate the potential of the data. The objectives of the research presented in this dissertation were to (1) validate EDR data accuracy in full-frontal, side-impact moving deformable barrier, and small overlap crash tests; (2) evaluate EDR survivability beyond regulatory crash tests, (3) determine the seat belt accuracy of current databases, and (4) assess the merits of other vehicle-based crash severity metrics relative to delta-v. This dissertation firstly assessed the capabilities of EDRs. Chapter 2 demonstrated the accuracy of 176 crash tests, corresponding to 29 module types, 5 model years, 9 manufacturers, and 4 testing configurations from 2 regulatory agencies. Beyond accuracy, Chapter 3 established that EDRs are anecdotally capable of surviving extreme events of vehicle fire, vehicle immersion, and high delta; although the frequency of these events are very rare on U.S. highways. The studies in Chapters 4 and 5 evaluated specific applications intended to showcase the potential of EDR data. Even single value data elements from EDRs were shown to be advantageous. In particular, the seat belt use status may become a useful tool to supplement crash investigators, especially in low severity crashes that provide little forensic evidence. Moreover, time-series data from EDRs broadens the number of available vehicle-based crash severity metrics that can be utilized. In particular, EDR data was used to calculate vehicle pulse index (VPI), which was shown to have modestly increased predictive abilities of serious injury compared to the widely used delta-v among belted occupants. Ultimately, this work has strong implications for EDR users, regulatory agencies, and future technologies. / Ph. D.

Since 2015, there have been more than 35,000 fatalities annually due to crashes on United States roads [1], [2]. Typically, road departure crashes account for less than 10% of all annual crash occupants yet comprise nearly one third of all crash fatalities in the US [3]. In the year 2020, road departure crashes accounted for 50% of crash fatalities [2]. Road departure crashes are characterized by a vehicle leaving the intended lane of travel, departing the roadway, and striking a roadside object, such as a tree or pole, or roadside condition, such as a slope or body of water. One strategy currently implemented to mitigate these types of crashes is the use of roadside barriers. Roadside barriers, such as metal guardrails, concrete barriers, and cable barriers, are designed to reduce the severity of road departure crashes by acting as a shield between the departed vehicle and more hazardous roadside obstacles. Much like new vehicles undergo regulatory crash tests, barriers must adhere to a set of crash test procedures to ensure the barriers perform as intended. Currently, the procedures for full-scale roadside barrier crash tests used to evaluate the crash performance of roadside safety hardware are outlined in The Manual for Assessing Safety Hardware (MASH) [4]. During roadside barrier tests, the assessment of occupant injury risk is crucial, as the purpose of the hardware is to prevent the vehicle from colliding with a more detrimental roadside object, all the while minimizing, and not posing additional, risk to the occupants. Unlike the new vehicle regulatory crash tests conducted by the National Highway Traffic Safety Administration (NHTSA), MASH does not require the use of instrumented anthropomorphic test devices (ATD). Instead, one of the prescribed occupant risk assessment methods in MASH is the flail space model (FSM), which was introduced in 1981 and models an occupant as an unrestrained point mass. The FSM is comprised of two crash severity metrics that can be calculated using acceleration data from the test vehicle. Each metric is prescribed a maximum threshold in MASH and if either threshold is exceeded during a crash test the test fails due to high occupant injury risk. Since the inception of the FSM metrics and their thresholds, the injury prediction capabilities of these metrics have only been re-investigated in the frontal crash mode, despite MASH prescribing an oblique 25-degree impact angle for passenger vehicle barrier tests. The focus of this dissertation was to use EDR data from real-world crashes to assess the current relevance of roadside barrier crash test occupant risk assessment methods to the modern vehicle fleet and occupant population. Injury risk prediction models were constructed for the two FSM-based metrics and five additional crash severity metrics for three crash modes: frontal, side, and oblique. For each crash mode and metric combination, four injury prediction models were constructed: one to predict probability of injury to any region of the body and three to predict probability of injury to the head/face, neck, and thorax regions. While the direct application of these models is to inform future revisions of MASH crash test procedures, the developed models have valuable applications for other areas of transportation safety besides just roadside safety. The final two chapters of this dissertation explore these additional applications: 1) assessing the injury mitigation effectiveness of an advanced automatic emergency braking system, and 2) informing speed limit selection that supports the safe system approach. The findings in this dissertation indicate that both the FSM and additional crash severity metrics do a reasonable job predicting occupant injury risk in oblique crashes. One of the additional metrics performs better than the two FSM metrics. Additionally, several occupant factors, such as belt status and age, play significant roles in occupant risk prediction. These findings have important implications for future revisions of MASH, which could benefit from considering additional metrics and occupant factors in the occupant risk assessment procedures. / Doctor of Philosophy / Every year, there are more than 35,000 fatalities due to crashes on United States roads. While there are many different types of crashes, there is a small collection of crash types that are responsible for the majority of these fatalities. One of the worst crash types is a road departure crash. Road departure crashes describe when a vehicle leaves the roadway and collides with an object off the roadway (such as a tree, pole, or ditch). Road departure crashes typically comprise 10% of crashes but are responsible for more than 30% of the annual crash fatalities. In 2020, road departure crashes were responsible for 50% of the 39,000 fatalities. One strategy that is currently used to reduce road departure fatalities is the use of roadside barriers. Common roadside barrier types include metal guardrails, concrete barriers, and cable guardrails, and are used to prevent vehicles that are departing the roadway from hitting an object that would be more dangerous than the barrier. To ensure barriers successfully protect the vehicle and vehicle occupants from heightened danger, they are crash tested in scenarios that are designed to mimic real-world crashes. The Manual for Assessing Safety Hardware (MASH) is the document that currently outlines the details necessary to conduct one of these crash tests. During roadside barrier tests, it is crucial to determine whether occupants are at risk of injury or fatality. For a variety of reasons, barrier tests do not use the traditional crash test dummies, which are designed to replicate human presence in a crash vehicle. Instead, MASH recommends using vehicle velocity data to assess how much risk is posed to an occupant. Using this velocity data, two values can be computed and if either value exceeds the maximum values provided in MASH, the crash test fails due to high occupant risk. The suggestion to use velocity data to assess occupant risk was first introduced in 1981. Since then, there have been significant advances in vehicle design, barrier design, and occupants' willingness to partake in safe habits, such as wearing seatbelts. Therefore, it is necessary to determine if the occupant risk values used in MASH are still applicable today. The focus of this dissertation was to use real-world crash data to assess the current relevance of roadside barrier crash test occupant risk values. The results presented in this dissertation can be used to select new occupant risk values in future versions of MASH. The findings within this dissertation show that the current methods in MASH do a good job estimating an occupant's risk of injury. Additionally, the findings show that certain occupant factors, such as the age of an occupant and whether the occupant is belted, help to more accurately estimate occupant injury risk. This finding has important implications for MASH, which does not currently consider different occupant conditions.

Every year, approximately 34,000 individuals are fatally injured in crashes on US roads [1]. These fatalities occur across many types of crash scenarios, each with its own causation factors. One way to prioritize research on a preventive technology is to compare the number of occupant fatalities relative to the total number of occupants involved in a crash scenario. Four crash modes are overrepresented among fatalities: single vehicle road departure crashes, control loss crashes, cross-centerline head-on crashes, and pedestrian/cyclist crashes [2]. Interestingly, three of these crash scenarios require the subject vehicle to depart from the initial lane of travel. Lane departure warning (LDW) systems track the vehicle lane position and can alert the driver through audible and haptic feedback before the vehicle crosses the lane line. Lane departure prevention (LDP) systems can perform an automatic steering maneuver to prevent the departure. Another method of prioritizing research is to determine factors common among the fatal crashes. In 2017, 30.4% of passenger vehicle crash fatalities involved a vehicle rollover [1]. Half of all fatal single vehicle road departure crashes resulted in a rollover yet only 12% of fatal multi-vehicle crashes involved a rollover [1]. These often occur after the driver has lost control of the vehicle and departed the road. Electronic stability control (ESC) can provide different braking to each wheel and allow the vehicle to maintain heading. While ESC is a promising technology, some rollover crashes still occur. Passive safety systems such as seat belts, side curtain airbags, and stronger roofs work to protect occupants during rollover crashes. Seat belts prevent occupants from moving inside the occupant compartment during the rollover and both seat belts and side curtain airbags can prevent occupants from being ejected from the vehicle. Stronger roofs ensure that the roof is not displaced during the rollover and the integrity of the occupant compartment is maintained to prevent occupant ejection. The focus of this dissertation is to evaluate the effectiveness of vehicle-based countermeasures, such as lane departure warning and electronic stability control, for preventing or mitigating single vehicle road departure crashes, cross-centerline head-on crashes, and single vehicle rollover crashes. This was accomplished by understanding how drivers respond to both road departure and cross-centerline events in real-world crashes. These driver models were used to simulate real crash scenarios with LDW/LDP systems to quantify their potential crash reduction. The residual crashes, which are not avoided with LDW/LDP systems or ESC, were analyzed to estimate the occupant injury outcome. For rollover crashes, a novel injury model was constructed that includes modern passive safety countermeasures such as seat belts, side curtain airbags, and stronger roofs. The results for road departure, head-on, and control loss rollover crashes were used to predict the number of crashes and injured occupants in the future. This work is important for identifying the residual crashes that require further research to reduce the number of injured crash occupants. / Doctor of Philosophy / Every year in the US, approximately 34,000 individuals are fatally injured in many different types of crashes. However, some crash types are more dangerous than other crash types. Drift-out-of-lane (DrOOL) road departure crashes, control loss road departure crashes, head-on crashes, and pedestrian crashes are more likely to result in an occupant fatality than other crash modes. In three of these more dangerous crash types, the vehicle departs from the lane before the crash occurs. Lane departure warning (LDW) systems can detect when the vehicle is about to cross the lane line and notify the driver with beeping or vibrating the steering wheel. A different system, called lane departure prevention (LDP), can provide automatic steering to prevent the vehicle from leaving the lane or return lane. In control loss crashes, the vehicle's motion is in a different direction than the vehicle's heading. During control loss, it is easier for the vehicle to roll over which is very dangerous. Electronic stability control (ESC) can prevent control loss by applying selective braking to each tire to keep the vehicle's motion in the same direction as the vehicle's heading. If a rollover still occurs, vehicles are equipped with passive safety systems and designs such as seat belts, side curtain airbags, and stronger roofs to protect the people inside. Seat belts can prevent occupants from striking the vehicle interior during the rollover and both seat belts and side curtain airbags can prevent occupants from being ejected from the vehicle. Stronger roofs ensure that the roof is not displaced during the rollover to prevent occupants from being ejected from the vehicle. The focus of this dissertation is to estimate how many crashes LDW, LDP, and ESC systems could prevent. This was accomplished by understanding how drivers respond after leaving their lane in real crashes. Then, these real crash scenarios were simulated with an LDW or LDP system to estimate how many crashes were prevented. The occupants of residual crashes, which were not prevented by the simulated systems, were analyzed to estimate the number of occupants with at least one moderate injury. Understanding which crashes and injuries that were not prevented with this technology can be used to decide where future research should occur to prevent more fatalities in road departure, head-on and control loss crashes.