cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27467836
title: Bridging the gap: Mechanistic-based cyclist injury risk curves using two decades of crash data

description: Injury risk curves are vital in quantifying the relative safety consequences of real-world collisions. Previous injury risk curves for bicycle-passenger vehicle crashes have predominantly focused on frontal impacts. This creates a gap in cyclist injury risk assessment for other geometric crash configurations. The goal of this study was to create an “omnidirectional” injury risk model, informed by known injury causing mechanisms, that is applicable to most geometric configurations. We used data from years 1999–2022 of the German In-Depth Accident Study (GIDAS). We describe the pattern of injuries for cyclists involved in collisions with passenger vehicles, and we developed injury risk functions at various AIS levels for these collisions. A mechanistic-based approach accounting for biomechanically-relevant variables was used to select model parameters a priori. Cyclist age (including children) and sex were regarded as relevant predictors of injury risk. Speed and impact geometry were captured through a novel predictor, <i>Effective Collision Speed</i>, which transforms the vehicle and cyclist speeds into a single value and incorporates frictional considerations observed during side engagements. Cyclist engagement with the vehicle was captured with a variable demonstrating the potential for a normal projection. We additionally present analyses weighted toward German nationwide data. We identified 6,576 cyclists involved in collisions with passenger vehicles. AIS3+ cyclist injuries occurred most often in the head, thorax, and lower extremities. <i>Effective Collision Speed</i> was a strong predictor of injury risk. Collisions with a potential for a normal projection were associated with increased risk, though this was only significant at the MAIS2+F severity level. Younger children had slightly higher injury risk compared to young adults, while elderly cyclists had the highest risk of AIS3+ injury. Sex was a significant predictor only for the MAIS2+F injury risk curves. U.S. cyclist fatalities increased 55% from 2010 to 2021. To reduce injuries and fatalities, it is crucial to understand cyclist injury risk. This study builds on previous analyses by including children, incorporating additional mechanistic predictors, broadening the scope of included crashes, and using weighting to generalize these estimates toward national German statistics.
DOI: 10.6084/m9.figshare.27467836.v1
title: Bridging the gap: Mechanistic-based cyclist injury risk curves using two decades of crash data

description: Injury risk curves are vital in quantifying the relative safety consequences of real-world collisions. Previous injury risk curves for bicycle-passenger vehicle crashes have predominantly focused on frontal impacts. This creates a gap in cyclist injury risk assessment for other geometric crash configurations. The goal of this study was to create an “omnidirectional” injury risk model, informed by known injury causing mechanisms, that is applicable to most geometric configurations. We used data from years 1999–2022 of the German In-Depth Accident Study (GIDAS). We describe the pattern of injuries for cyclists involved in collisions with passenger vehicles, and we developed injury risk functions at various AIS levels for these collisions. A mechanistic-based approach accounting for biomechanically-relevant variables was used to select model parameters a priori. Cyclist age (including children) and sex were regarded as relevant predictors of injury risk. Speed and impact geometry were captured through a novel predictor, <i>Effective Collision Speed</i>, which transforms the vehicle and cyclist speeds into a single value and incorporates frictional considerations observed during side engagements. Cyclist engagement with the vehicle was captured with a variable demonstrating the potential for a normal projection. We additionally present analyses weighted toward German nationwide data. We identified 6,576 cyclists involved in collisions with passenger vehicles. AIS3+ cyclist injuries occurred most often in the head, thorax, and lower extremities. <i>Effective Collision Speed</i> was a strong predictor of injury risk. Collisions with a potential for a normal projection were associated with increased risk, though this was only significant at the MAIS2+F severity level. Younger children had slightly higher injury risk compared to young adults, while elderly cyclists had the highest risk of AIS3+ injury. Sex was a significant predictor only for the MAIS2+F injury risk curves. U.S. cyclist fatalities increased 55% from 2010 to 2021. To reduce injuries and fatalities, it is crucial to understand cyclist injury risk. This study builds on previous analyses by including children, incorporating additional mechanistic predictors, broadening the scope of included crashes, and using weighting to generalize these estimates toward national German statistics.
DOI: 10.6084/m9.figshare.27466539
title: Efficient injury risk predictions for a diverse population using parametric human modeling and inducing points in Gaussian processes

description: The objective of this study is to use parametric human modeling and machine learning methods to identify representative occupants that can account for injury variations among a more diverse population with a limited simulation budget. A maximal projection method was used to sample 100 occupants, considering the variations in stature, weight, and sitting height. An automated mesh morphing method was used to morph the THUMS v4.1 midsize male model into the target geometries. US-NCAP frontal crash simulations were conducted with morphed human models and validated vehicle/restraint models. Surrogate models based on the Gaussian Process (GP) method were trained to find <i>inducing points</i> (IP), here defined as a small number of representative occupants whose outcomes could be used to accurately estimate the variations in the injury risks and patterns throughout the population. Statistical analysis was conducted to validate the IPs’ coverage of total variation by illustrating the IP distribution. Restraint optimization was performed at IPs to yield an enhanced restraint system. The method was validated through comparisons among the predicted injury risks under the optimal and baseline designs. Only 20 IPs were needed to sufficient to properly represent the variations in the injury risks and patterns in the whole population with acceptable accuracy. Compared to the surrogate model built from 100 crash simulations, the IP-based surrogate models incurred only 0.4% and 1.8% errors in head injury risks for males and females, respectively. Regarding the injury risks on the chest and lower extremities, the IP-based surrogate models resulted in less than 0.1% and 0.5% errors for males and females, respectively. The FE simulations indicated that the optimal restraint system design reduced the injury risk by relatively 16% and 13% for male and female respectively when delta-<i>V</i> = 25 (mph), and 47% and 27% for male and female when delta-<i>V</i> = 35 (mph). The study proposed a method to generate more accurate injury risk predictions for a more diverse population under a limited simulation budget. Simulations using IPs may enable restraint system optimization to be conducted more efficiently while reducing injury risks across a more diverse population.
DOI: 10.6084/m9.figshare.27466539.v1
title: Efficient injury risk predictions for a diverse population using parametric human modeling and inducing points in Gaussian processes

description: The objective of this study is to use parametric human modeling and machine learning methods to identify representative occupants that can account for injury variations among a more diverse population with a limited simulation budget. A maximal projection method was used to sample 100 occupants, considering the variations in stature, weight, and sitting height. An automated mesh morphing method was used to morph the THUMS v4.1 midsize male model into the target geometries. US-NCAP frontal crash simulations were conducted with morphed human models and validated vehicle/restraint models. Surrogate models based on the Gaussian Process (GP) method were trained to find <i>inducing points</i> (IP), here defined as a small number of representative occupants whose outcomes could be used to accurately estimate the variations in the injury risks and patterns throughout the population. Statistical analysis was conducted to validate the IPs’ coverage of total variation by illustrating the IP distribution. Restraint optimization was performed at IPs to yield an enhanced restraint system. The method was validated through comparisons among the predicted injury risks under the optimal and baseline designs. Only 20 IPs were needed to sufficient to properly represent the variations in the injury risks and patterns in the whole population with acceptable accuracy. Compared to the surrogate model built from 100 crash simulations, the IP-based surrogate models incurred only 0.4% and 1.8% errors in head injury risks for males and females, respectively. Regarding the injury risks on the chest and lower extremities, the IP-based surrogate models resulted in less than 0.1% and 0.5% errors for males and females, respectively. The FE simulations indicated that the optimal restraint system design reduced the injury risk by relatively 16% and 13% for male and female respectively when delta-<i>V</i> = 25 (mph), and 47% and 27% for male and female when delta-<i>V</i> = 35 (mph). The study proposed a method to generate more accurate injury risk predictions for a more diverse population under a limited simulation budget. Simulations using IPs may enable restraint system optimization to be conducted more efficiently while reducing injury risks across a more diverse population.
DOI: 10.7483/opendata.delphi.0l7o.amcj
title: DELPHI simulation data xs_baba_e091_r98_1l_d2
DOI: 10.6084/m9.figshare.27466544.v1
title: Benchmarks for retrospective automated driving system crash rate analysis using police-reported crash data

description: With fully automated driving systems (ADS; SAE level 4) ride-hailing services expanding in the U.S., we are now approaching an inflection point in the history of vehicle safety assessment. The process of retrospectively evaluating ADS safety impact (as seen with seatbelts, airbags, electronic stability control, etc.) can start to yield statistically credible conclusions. An ADS safety impact measurement requires a comparison to a “benchmark” crash rate. Most benchmarks generated to-date have focused on the current human-driven fleet, which enable researchers to understand the impact of the introduced ADS technology on the current crash record status quo. This study aims to address, update, and extend the existing literature by leveraging police-reported crashes to generate human crash rates for multiple geographic areas with current ADS deployments. <b>Methods:</b> All of the data leveraged is publicly accessible, and the benchmark determination methodology is intended to be repeatable and transparent. Generating a benchmark that is comparable to ADS crash data is associated with certain challenges, including data selection, handling underreporting and reporting thresholds, identifying the population of drivers and vehicles to compare against, choosing an appropriate severity level to assess, and matching crash and mileage exposure data. Consequently, we identify essential steps when generating benchmarks, and present our analyses amongst a backdrop of existing ADS benchmark literature. One analysis presented is the usage of established underreporting correction methodology to publicly available human driver police-reported data to improve comparability to publicly available ADS crash data. We also identified several important crash rate dependencies (geographic region, road type, and vehicle type), and show how failing to account for these features in ADS comparisons can bias results. Working with police-reported crash data to create crash rate benchmarks is fraught with challenges. Researchers should be cautious in their selection of crash rate benchmarks. We present these challenges, discuss their consequences, and provide analytical guidance for addressing them. This body of work aims to contribute to the ability of the community - researchers, regulators, industry, and experts - to reach consensus on how to estimate accurate benchmarks.
DOI: 10.6084/m9.figshare.27466544
title: Benchmarks for retrospective automated driving system crash rate analysis using police-reported crash data

description: With fully automated driving systems (ADS; SAE level 4) ride-hailing services expanding in the U.S., we are now approaching an inflection point in the history of vehicle safety assessment. The process of retrospectively evaluating ADS safety impact (as seen with seatbelts, airbags, electronic stability control, etc.) can start to yield statistically credible conclusions. An ADS safety impact measurement requires a comparison to a “benchmark” crash rate. Most benchmarks generated to-date have focused on the current human-driven fleet, which enable researchers to understand the impact of the introduced ADS technology on the current crash record status quo. This study aims to address, update, and extend the existing literature by leveraging police-reported crashes to generate human crash rates for multiple geographic areas with current ADS deployments. <b>Methods:</b> All of the data leveraged is publicly accessible, and the benchmark determination methodology is intended to be repeatable and transparent. Generating a benchmark that is comparable to ADS crash data is associated with certain challenges, including data selection, handling underreporting and reporting thresholds, identifying the population of drivers and vehicles to compare against, choosing an appropriate severity level to assess, and matching crash and mileage exposure data. Consequently, we identify essential steps when generating benchmarks, and present our analyses amongst a backdrop of existing ADS benchmark literature. One analysis presented is the usage of established underreporting correction methodology to publicly available human driver police-reported data to improve comparability to publicly available ADS crash data. We also identified several important crash rate dependencies (geographic region, road type, and vehicle type), and show how failing to account for these features in ADS comparisons can bias results. Working with police-reported crash data to create crash rate benchmarks is fraught with challenges. Researchers should be cautious in their selection of crash rate benchmarks. We present these challenges, discuss their consequences, and provide analytical guidance for addressing them. This body of work aims to contribute to the ability of the community - researchers, regulators, industry, and experts - to reach consensus on how to estimate accurate benchmarks.
DOI: 10.7483/opendata.delphi.sex7.2w1u
title: DELPHI simulation data xs_hzha03pyth6156ha4g_e188.6_m190_m4_c98_1l_e1
DOI: 10.15468/dl.e4gdxb
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pandalus nipponensis Yokoya, 1933",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 23 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058615-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27466557
title: Adult-supervised practice in learner’s permit phase has a significant but limited ability to improve safe driving skills

description: Most U.S. states require adult-supervised practice for adolescent learner permit holders intending to obtain a driver’s license before 18. However, the effectiveness of adult-supervised practice in imparting safe driving skills had been inconclusive prior to the recent development of consistent measures of adolescent learners’ driving performance. We examined the relationship between the number of adult-supervised practice hours and driving performance and skill deficits for 441 learner permit holders ages 16 and 17 in Pennsylvania. Data came from a virtual driving assessment (VDA) deployed across Children’s Hospital of Philadelphia’s Primary Care Network and a self-reported survey at the time of the VDA. Based on a previous study, we defined two VDA Driving Classes that describe driving performance: Major Issues or Major Issues with Dangerous Behavior Class, and Minor Issues or No Issues Class. The response options for adult-supervised practice hours were presented to adolescents as categories (none, &lt;15 h, 15–&lt;65 h, 65 h, and &gt;65 h). We grouped those with 65 h with those with &gt;65 h given the low numbers of responses in these categories. The analysis used a Chi-square test and a binomial logit to investigate how adult-supervised practice hours correspond to adolescents’ VDA Driving Classes. Chi-square test showed weak associations between VDA Driving Classes and adult-supervised practice hours. The binomial logit found that adolescents who reported zero adult-supervised practice hours were more likely to be classified into the Major Issues or Major Issues with Dangerous Behavior Class than those who reported adult-supervised practice hours. For those who reported adult-supervised practice, the differences in VDA Driving Class were not statistically significant across the three practice categories (i.e., &lt;15 h, 15–&lt;65 h, and ≥65 h). The model found no significant associations between VDA Driving Class and whether adolescent learners reported practicing with a formal instructor. Our findings suggest that under current adult-supervised practice routines, longer practice hours have limited associations with improvements in adolescents’ VDA Driving Class. Some adolescents might not be ready to deal with the more dangerous settings the VDA presents even after completing the required hours of adult-supervised practice.
DOI: 10.6084/m9.figshare.27466557.v1
title: Adult-supervised practice in learner’s permit phase has a significant but limited ability to improve safe driving skills

description: Most U.S. states require adult-supervised practice for adolescent learner permit holders intending to obtain a driver’s license before 18. However, the effectiveness of adult-supervised practice in imparting safe driving skills had been inconclusive prior to the recent development of consistent measures of adolescent learners’ driving performance. We examined the relationship between the number of adult-supervised practice hours and driving performance and skill deficits for 441 learner permit holders ages 16 and 17 in Pennsylvania. Data came from a virtual driving assessment (VDA) deployed across Children’s Hospital of Philadelphia’s Primary Care Network and a self-reported survey at the time of the VDA. Based on a previous study, we defined two VDA Driving Classes that describe driving performance: Major Issues or Major Issues with Dangerous Behavior Class, and Minor Issues or No Issues Class. The response options for adult-supervised practice hours were presented to adolescents as categories (none, &lt;15 h, 15–&lt;65 h, 65 h, and &gt;65 h). We grouped those with 65 h with those with &gt;65 h given the low numbers of responses in these categories. The analysis used a Chi-square test and a binomial logit to investigate how adult-supervised practice hours correspond to adolescents’ VDA Driving Classes. Chi-square test showed weak associations between VDA Driving Classes and adult-supervised practice hours. The binomial logit found that adolescents who reported zero adult-supervised practice hours were more likely to be classified into the Major Issues or Major Issues with Dangerous Behavior Class than those who reported adult-supervised practice hours. For those who reported adult-supervised practice, the differences in VDA Driving Class were not statistically significant across the three practice categories (i.e., &lt;15 h, 15–&lt;65 h, and ≥65 h). The model found no significant associations between VDA Driving Class and whether adolescent learners reported practicing with a formal instructor. Our findings suggest that under current adult-supervised practice routines, longer practice hours have limited associations with improvements in adolescents’ VDA Driving Class. Some adolescents might not be ready to deal with the more dangerous settings the VDA presents even after completing the required hours of adult-supervised practice.
DOI: 10.7483/opendata.delphi.inn6.0ldl
title: DELPHI simulation data xs_hiwp_e188_g98_1l_d2
DOI: 10.7483/opendata.delphi.a4do.g8x8
title: DELPHI simulation data xs_ha50_e189_sa98_1l_d2
DOI: 10.7483/opendata.delphi.ke5o.j2in
title: DELPHI simulation data xs_hzha03pyth6156hattbb_e188.6_m30_m40_c98_1l_e1
DOI: 10.7483/opendata.delphi.8lef.xnth
title: DELPHI simulation data xs_ggm5_e189_r98_1l_d2
DOI: 10.15468/dl.prgkgg
title: Occurrence Download

description: A dataset containing 88660 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-118.34362 34.20028,-118.24491 34.20028,-118.24491 34.26672,-118.34362 34.26672,-118.34362 34.20028))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 88660 records from 102 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058613-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g822gw
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pandalus latirostris M.J.Rathbun, 1902",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 12 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058617-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d9vexd
title: Occurrence Download

description: A dataset containing 37450 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is UFG - Herbário da Universidade Federal de Goiás"
 ]
}

 The dataset includes 37450 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058616-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.delphi.1p70.h1qe
title: DELPHI simulation data xs_hzha03pyth6156ha4b_e188.6_m12_m170_r98_1l_e1
DOI: 10.15468/dl.pxhkf4
title: Occurrence Download

description: A dataset containing 115 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pleurogrammus azonus Jordan & Metz, 1913",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 115 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058619-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ncja9c
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pandalus borealis Krøyer, 1838",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 10 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058620-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.