Browsing by Author "Sudweeks, Jeremy D."

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    Estimating Crash Consequences for Occupantless Automated Vehicles
    Witcher, 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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    Geospatial analysis of high-crash intersections and rural roads using naturalistic driving data : final report
    Cannon, Brad R.; Sudweeks, Jeremy D. (National Surface Transportation Safety Center for Excellence, 2011-09-26)
    Despite the fact that overall road safety continues to improve, intersections and rural roads persist as trouble areas or hotspots. Using a previously developed method, naturalistic driving data were identified through intersection and rural road hotspots and compared to naturalistic driving data through similar intersections and rural road locations, but with low crash counts. Few significant differences were found between driver behaviors in the low-crash and high-crash areas of study. For the few significant differences, there was not an apparent consistent pattern. -- Report website.
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    The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data
    Klauer, Charlie; Dingus, Thomas A.; Neale, Vicki L.; Sudweeks, Jeremy D.; Ramsey, D. J. (United States. National Highway Traffic Safety Administration, 2006-04)
    The purpose of this report was to conduct in-depth analyses of driver inattention using the driving data collected in the 100-Car Naturalistic Driving Study. An additional database of baseline epochs was reduced from the raw data and used in conjunction with the crash and near-crash data identified as part of the original 100-Car Study to account for exposure and establish near-crash/crash risk. The analyses presented in this report are able to establish direct relationships between driving behavior and crash and near-crash involvement. Risk was calculated (odds ratios) using both crash and near-crash data as well as normal baseline driving data for various sources of inattention. The corresponding population attributable risk percentages were also calculated to estimate the percentage of crashes and near-crashes occurring in the population resulting from inattention. Additional analyses involved: driver willingness to engage in distracting tasks or driving while drowsy; analyses with survey and test battery responses; and the impact of driver’s eyes being off of the forward roadway. The results indicated that driving while drowsy results in a four- to six-times higher near-crash/crash risk relative to alert drivers. Drivers engaging in visually and/or manually complex tasks have a three-times higher near-crash/crash risk than drivers who are attentive. There are specific environmental conditions in which engaging in secondary tasks or driving while drowsy is more dangerous, including intersections, wet roadways, and areas of high traffic density. Short, brief glances away from the forward roadway for the purpose of scanning the driving environment are safe and actually decrease near-crash/crash risk. Even in the cases of secondary task engagement, if the task is simple and requires a single short glance the risk is elevated only slightly, if at all. However, glances totaling more than 2 seconds for any purpose increase near-crash/crash risk by at least two times that of normal, baseline driving.
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    Using Functional Classification to Enhance Naturalistic Driving Data Crash/Near Crash Algorithms
    Sudweeks, Jeremy D. (National Surface Transportation Safety Center for Excellence, 2015-01-20)
    A persistent challenge in using naturalistic driving data is identifying events of interest from a large data set in a cost-effective manner. A common approach to this problem is to develop kinematic thresholds against which kinematic data is compared to identify potential events or kinematic triggers. Trained video analysts are then used to determine if any of the kinematic triggers have successfully identified events of interest. Video validation for a large number of kinematic triggers is time consuming, expensive, and possibly prone to error. The use of video analysis to review a large number of kinematic triggers is due to an inability to effectively discriminate between innocuous driving situations and safety-relevant events in an automated manner. A potential solution to this problem is the development of classification models that would reduce the number of kinematic triggers submitted for video validation through a process of pre-validation trigger classification. A functional yaw rate classifier was developed that retains a majority of safety relevant events (92% of crashes, 81% of near-crashes) while reducing the number of invalid or erroneous yaw rate triggers by 42%. For large-scale studies such a reduction in the number of invalid triggers submitted for video validation allows video analysis resources to be focused on the aspect of driving research in which it add the greatest value: providing contextual information that cannot be derived from kinematic and parametric data.