Browsing by Author "Riexinger, Luke E."

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    Applications of Event Data Recorder Derived Crash Severity Metrics to Injury Prevention
    Dean, Morgan Elizabeth (Virginia Tech, 2023-05-25)
    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.
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    Assessing Effects of Object Detection Performance on Simulated Crash Outcomes for an Automated Driving System
    Galloway, 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.
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    Characterizing Bicyclists Behavior in Overtaking Scenarios Over Different Road Infrastructures
    Crump 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.
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    In-Depth Evaluation of Association between Crash and Hand Arthritis via Naturalistic Driving Study
    Almannaa, Mohammed H.; Bareiss, Max G.; Riexinger, Luke E.; Guo, Feng (MDPI, 2022-11-25)
    Severe arthritis can limit a driver’s range of motion and increase their crash risk. The high prevalence of arthritis among the US driver population, especially among senior drivers, makes it a public safety concern. In this study, we evaluate the impact of arthritis on driving behavior and crash risk using the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS), which collected continuous driving data through data acquisition systems installed on participant’s vehicles. A detailed questionnaire survey was administered on demographic, health conditions, and personality information at the time of recruitment. The dataset includes 3563 participants. Among them, 78 drivers were identified to have severe arthritis, and they contributed to 414 out of 1641 crashes. We systematically evaluated the impact of severe arthritis on crash risk, secondary task engagement, and fitness-to-drive metrics. The results show there is a significant relationship between arthritis and crash risk, with an odds ratio of 1.99 with adjustment for age effects, which indicates that individuals with arthritis are twice as likely to be involved in a crash. There is no statistically significant association between arthritis and secondary task engagement, as well as the sensation-seeking scores, a personality trait.
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    Modeling Driver Behavior and I-ADAS in Intersection Traversals
    Kleinschmidt, Katelyn Anne (Virginia Tech, 2023-12-20)
    Intersection Advance Driver Assist Systems (I-ADAS) may prevent 25 to 93% of intersection crashes. The effectiveness of I-ADAS will be limited by driver's pre-crash behavior and other environmental factors. This study will characterize real-world intersection traversals to evaluate the effectiveness of I-ADAS while accounting for driver behavior in crash and near-crash scenarios. This study characterized real-world intersection traversals using naturalistic driving datasets: the Second Strategic Highway Research Program (SHRP-2) and the Virginia Traffic Cameras for Advanced Safety Technologies (VT-CAST) 2020. A step-by-step approach was taken to create an algorithm that can identify three different intersection traversal trajectories: straight crossing path (SCP); left turn across path opposite direction (LTAP/OD); and left turn across path lateral direction (LTAP/LD). About 140,000 intersection traversals were characterized and used to train a unique driver behavior model. The median average speed for all encounter types was about 7.2 m/s. The driver behavior model was a Markov Model with a multinomial regression that achieved an average 90.5% accuracy across the three crash modes. The model used over 124,000 total intersection encounters including 301 crash and near-crash scenarios. I-ADAS effectiveness was evaluated with realistic driver behavior in simulations of intersection traversal scenarios based on proposed US New Car Assessment Program I-ADAS test protocols. All near-crashes were avoided. The driver with I-ADAS overall helped avoid more crashes. For SCP and LTAP the collisions avoided increased as the field of view of the sensor increased in I-ADAS only simulations. There were 18% crash scenarios that were not avoided with I-ADAS with driver. Among near-crash scenarios, where NHTSA expects no I-ADAS activation, there were fewer I-ADAS activations (58.5%) due to driver input compared to the I-ADAS only simulations (0%).
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    Pedal Misapplication: Past, Present, and Future
    Smith, Colin P. (Virginia Tech, 2022)
    Pedal misapplication (PM) is an error in which a driver unintentionally presses the wrong pedal. When drivers mistake the accelerator pedal for the brake pedal, the vehicle experiences a sudden unintended acceleration, and the consequences can be severe. A brief history of PM is covered, and several novel studies of PM are described. The goals of these studies were as follows: 1. Identify and analyze multiple samples of PM crashes from a variety of data sources using both established and novel methods to gain new insight into the characteristics and frequency of PM crashes. 2. Use the confirmed, real-world PM crash data to develop a custom vehicle dynamics simulation and evaluate the overall potential safety benefit of a theoretical PM advanced driver assistance system. Using an established keyword search identification method and two unique crash datasets, a PM crash frequency of approximately 0.2% of all crashes was found. These PM crashes were typically rear-end or road departure crashes in moderate- to low-speed commercial or residential areas. Female drivers and elderly drivers were more often involved in these PM crashes, which generally featured slightly lower injury severities and often involved inattention or fatigue. Anecdotally, PM crash narratives contained repeated evidence of unexpected events, driver inexperience, distraction, shoe-malfunction, extreme stress, and medical conditions/emergencies. A novel PM crash identification algorithm was developed to detect PMs from time-series pre-crash data. This algorithm was applied to a sample of crashes with event data recorder data available, and a frequency of 4.3% of eligible crashes were found to have exhibited PM behavior, suggesting that PM crashes may be more prevalent than previously thought. While the data from these crashes suggested that a PM occurred, this dataset lacked sufficient data regarding driver intention, which is necessary to confirm each crash as PMs. The characteristics of these PM-like crashes were analyzed and found to be largely similar to those of previous samples, with notable exceptions for higher proportions of male drivers, higher travel speeds, and higher maximum injury severities. More robust data from a naturalistic driving study (NDS) was acquired, and the novel algorithm was applied to all of the sample’s eligible crashes. Because the NDS data contained more data elements such as driver-facing video, crashes that exhibited PM behavior were individually inspected to confirm PM. This produced a PM crash frequency of 1.1%. The characteristics of these confirmed PM crashes were investigated, but a small sample size limits the generalizability of the results. Lastly, crash data from confirmed, real-world PM crashes was used to inform a custom vehicle dynamics model into which a theoretical PM advanced driver assistance system was simulated. The effect of the accelerator suppression system on crash avoidance and mitigation was evaluated to assess its potential safety benefit, which was found to be highly dependent on system threshold values and largely underwhelming in the absence of supplemental braking. The results indicated that a system that detected PM, suppressed acceleration, and applied braking could provide a substantially higher safety benefit.
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    Residual Crashes and Injured Occupants with Lane Departure Prevention Systems
    Riexinger, Luke E. (Virginia Tech, 2021-04-19)
    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.