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© 2023 by the National Academy of Sciences. National Academies of Sciences, Engineering, and Medicine and the graphical logo are trademarks of the National Academy of Sciences. All rights reserved. NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM Systematic, well-designed, and implementable research is the most effective way to solve many problems facing state departments of transportation (DOTs) administrators and engineers. Often, highway problems are of local or regional interest and can best be studied by state DOTs individually or in cooperation with their state universities and others. However, the accelerating growth of highway transportation results in increasingly complex problems of wide interest to highway authorities. These problems are best studied through a coordinated program of cooperative research. Recognizing this need, the leadership of the American Association of State Highway and Transportation Officials (AASHTO) in 1962 initiated an objective national highway research program using modern scientific techniquesâ the National Cooperative Highway Research Program (NCHRP). NCHRP is supported on a continuing basis by funds from participating member states of AASHTO and receives the full cooperation and support of the Federal Highway Administration (FHWA), United States Department of Transportation, under Agreement No. 693JJ31950003. COPYRIGHT INFORMATION Authors herein are responsible for the authenticity of their materials and for obtaining written permissions from publishers or persons who own the copyright to any previously published or copyrighted material used herein. Cooperative Research Programs (CRP) grants permission to reproduce material in this publication for classroom and not-for-profit purposes. Permission is given with the understanding that none of the material will be used to imply endorsement by TRB and any of its program sponsors of a particular product, method, or practice. It is expected that those reproducing the material in this document for educational and not-for-profit uses will give appropriate acknowledgment of the source of any reprinted or reproduced material. For other uses of the material, request permission from CRP. DISCLAIMER To facilitate more timely dissemination of research findings, this pre-publication document is taken directly from the submission of the research agency. The material has not been edited by TRB. The opinions and conclusions expressed or implied in this document are those of the researchers who performed the research. They are not necessarily those of the Transportation Research Board; the National Academies of Sciences, Engineering, and Medicine; the FHWA; or the program sponsors. The Transportation Research Board does not develop, issue, or publish standards or specifications. The Transportation Research Board manages applied research projects which provide the scientific foundation that may be used by Transportation Research Board sponsors, industry associations, or other organizations as the basis for revised practices, procedures, or specifications. The Transportation Research Board, the National Academies, and the sponsors of the National Cooperative Highway Research Program do not endorse products or manufacturers. Trade or manufacturersâ names appear herein solely because they are considered essential to the object of the report. This pre-publication document IS NOT an official publication of the Cooperative Research Programs; the Transportation Research Board; or the National Academies of Sciences, Engineering, and Medicine. Recommended citation: Gabauer, D. J., M. E. Dean, L. E. Riexinger, H. C. Gabler, and J. Stitzel. 2023. Evaluation and Comparison of Roadside Crash Injury Metrics. Pre-publication draft of NCHRP Research Report 1095. Transportation Research Board, Washington, DC.
ii Table of Contents List of Figures ........................................................................................................................... x List of Tables ............................................................................................................................ xv Acknowledgments ................................................................................................................... xxv Summary ............................................................................................................................... xxvi Background ...................................................................................................................... xxvi Research Approach ........................................................................................................ xxvii Recommendations ......................................................................................................... xxviii 1 Introduction ........................................................................................................................ 1 Research Problem Statement ................................................................................... 1 Objectives and Scope ................................................................................................. 2 Organization of Report ............................................................................................. 3 2 Synthesis of Existing and Potential Roadside Crash Injury Metrics and Identification of Relevant Data Sources ................................................................................................... 5 Scope and Approach ................................................................................................. 5 Existing Roadside Crash Injury Metrics ................................................................. 5 2.2.1 FSM Predecessors ............................................................................................... 6 2.2.2 FSM..................................................................................................................... 7 2.2.3 Theoretical Head Impact Velocity (THIV) and Post-Impact Head Deceleration (PHD) .......................................................................................... 22 2.2.4 ASI .................................................................................................................... 25 Potential Roadside Crash Injury Metrics ............................................................. 32 2.3.1 Crash Pulse Only Metrics ................................................................................. 33 2.3.2 VPI .................................................................................................................... 33 2.3.3 OLC................................................................................................................... 35 2.3.4 Crash Pulse + Actual Occupant Response ........................................................ 37 Occupant Compartment Intrusion and MASH Limits........................................ 39 Injury Characterization Metrics ............................................................................ 41 2.5.1 Injury Severity Score (ISS) ............................................................................... 41 2.5.2 Injury Impairment Scale (IIS) ........................................................................... 42 2.5.3 Functional Capacity Index (FCI) ...................................................................... 42 2.5.4 Measuring Societal Cost with Harm ................................................................. 42 2.5.5 Multi-Harm Approach ...................................................................................... 43 Potential Data Sources for Evaluating Roadside Crash Injury Metrics ............ 44 2.6.1 NASS/CDS ....................................................................................................... 44 2.6.2 NHTSA Crash Investigation Sampling System (CISS) .................................... 44
iii 2.6.3 Virginia Tech (VT) EDR Database ................................................................... 45 2.6.4 NCHRP 17-43 Road Departure Dataset ........................................................... 45 2.6.5 CIREN............................................................................................................... 45 2.6.6 NHTSA SCI ...................................................................................................... 45 2.6.7 National Trauma Data Bank (NTDB) ............................................................... 46 Conclusions .............................................................................................................. 46 2.7.1 Existing Roadside Hardware Crash Injury Metrics .......................................... 46 2.7.2 Potential Vehicle-Based Crash Injury Metrics ................................................. 47 2.7.3 Occupant Compartment Intrusion and MASH Limits ...................................... 48 2.7.4 Supplemental Injury Characterization Metrics ................................................. 48 2.7.5 Available Datasets to Assess Roadside Hardware Crash Injury Metrics ......... 49 Gaps and Research Needs ....................................................................................... 49 3 Research Approach .......................................................................................................... 51 Injury Metrics and Injury Severity Measurement ............................................... 52 3.1.1 Candidate Injury Metrics .................................................................................. 52 3.1.2 Injury Severity Measurement Methods ............................................................. 53 Build and Analyze the Injury Assessment Dataset (IAD) .................................... 54 3.2.1 Assemble the Available Data and Compute Candidate Injury Metric Values ................................................................................................... 54 3.2.2 Develop Models of Injury Severity for Each Candidate Injury Metric ............ 55 3.2.3 Validate the Injury Severity Models using Independent Datasets and Rank Order Candidate Metrics .................................................................................. 56 Correlate Intrusion with Real-World Injury ........................................................ 58 Assess Candidate Metric Ability to Predict Occupant Acceleration .................. 59 Compare FSM and Alternate Metrics in Roadside Crash Tests ........................ 60 Proposed Implementation of Results in MASH.................................................... 60 Future Roadmap for Updates to MASH Injury Risk Evaluation ....................... 60 4 Building the IAD .............................................................................................................. 61 In-Depth Crash Datasets Matched with EDR Data ............................................. 62 4.1.1 Background ....................................................................................................... 62 4.1.2 Case Selection ................................................................................................... 62 4.1.3 Classifying Crash Type Subsets........................................................................ 63 4.1.4 Results ............................................................................................................... 66 4.1.5 Conclusions ....................................................................................................... 72 NTDB ........................................................................................................................ 72 4.2.1 Background ....................................................................................................... 72 4.2.2 Methods............................................................................................................. 72 4.2.3 Results ............................................................................................................... 73 4.2.4 Discussion ......................................................................................................... 75 4.2.5 Conclusions ....................................................................................................... 75
iv 5 Analyze IAD for Frontal Crashes ................................................................................... 76 Introduction ............................................................................................................. 76 Methods .................................................................................................................... 76 5.2.1 Metric Computation and Validation ................................................................. 76 5.2.2 Injury Risk Modeling ........................................................................................ 77 5.2.3 Region-Specific Models.................................................................................... 78 5.2.4 Predictive Capability ......................................................................................... 79 Overall Injury Model Results ................................................................................. 79 5.3.1 Initial Injury Risk Models ................................................................................. 79 5.3.2 Final Injury Risk Models .................................................................................. 81 5.3.3 Injury Risk Curves ............................................................................................ 83 Body Region Model Results .................................................................................... 87 5.4.1 Initial Injury Risk Models ................................................................................. 87 5.4.2 Final Injury Risk Models .................................................................................. 87 5.4.3 Injury Risk Curves ............................................................................................ 90 Model Validation and Comparison........................................................................ 93 Discussion ................................................................................................................. 94 Limitations ............................................................................................................... 97 Conclusions .............................................................................................................. 97 6 Analyze IAD for Side Crashes ......................................................................................... 99 Introduction ............................................................................................................. 99 Methods .................................................................................................................... 99 6.2.1 Metric Computation and Validation ................................................................. 99 6.2.2 Injury Risk Modeling ........................................................................................ 99 6.2.3 Body Region-Specific Models ........................................................................ 100 6.2.4 Predictive Capability ....................................................................................... 101 Overall Injury Model Results ............................................................................... 101 6.3.1 Initial Injury Risk Models ............................................................................... 101 6.3.2 Final Injury Risk Models ................................................................................ 102 6.3.3 Injury Risk Curves .......................................................................................... 103 Body Region Model Results .................................................................................. 106 6.4.1 Initial Injury Risk Models ............................................................................... 106 6.4.2 Final Injury Risk Models ................................................................................ 106 6.4.3 Injury Risk Curves .......................................................................................... 107 Model Validation and Comparison...................................................................... 110 Discussion ............................................................................................................... 111 Limitations ............................................................................................................. 112 Conclusions ............................................................................................................ 112 7 Analyze IAD for Oblique Crashes ................................................................................. 114
v Introduction ........................................................................................................... 114 Methods .................................................................................................................. 114 7.2.1 Metric Computation and Validation ............................................................... 114 7.2.2 Injury Risk Modeling ...................................................................................... 114 7.2.3 Body Region-Specific Models ........................................................................ 116 7.2.4 Predictive Capability ....................................................................................... 116 Overall Injury Model Results ............................................................................... 116 7.3.1 Initial Injury Risk Models ............................................................................... 116 7.3.2 Final Injury Risk Models ................................................................................ 117 7.3.3 Injury Risk Curves .......................................................................................... 118 Model Validation and Comparison...................................................................... 121 Discussion ............................................................................................................... 122 Limitations ............................................................................................................. 123 Conclusions ............................................................................................................ 123 8 Build and Analyze Harm Analysis Dataset for Frontal, Oblique, and Side Crashes . 124 Introduction ........................................................................................................... 124 Methods .................................................................................................................. 124 8.2.1 Case Selection Criteria .................................................................................... 124 8.2.2 Consideration of Alternative Injury Measurement Methods .......................... 124 8.2.3 Calculating Harm Cost .................................................................................... 125 8.2.4 Injury Risk Modeling ...................................................................................... 125 8.2.5 Predictive Capability ....................................................................................... 126 Final Linear Predictive Models ............................................................................ 126 8.3.1 Final Frontal Crash Harm Models .................................................................. 126 8.3.2 Final Oblique Crash Harm Models ................................................................. 128 8.3.3 Final Side Crash Harm Models ....................................................................... 129 8.3.4 Linear Harm Prediction Curves ...................................................................... 130 Predictive Capability Evaluation and Validation............................................... 136 Discussion ............................................................................................................... 136 Limitations ............................................................................................................. 138 Conclusions ............................................................................................................ 139 9 Analyze the Effect of Pjoint and Two Modified VPI Metrics on the Frontal Crash Injury Prediction Models .......................................................................................................... 140 Introduction ........................................................................................................... 140 Methods .................................................................................................................. 140 9.2.1 Pjoint Definition ................................................................................................ 140 9.2.2 Modified VPI Metric....................................................................................... 141 9.2.3 Injury Risk Modeling ...................................................................................... 141 Overall Injury Model Results ............................................................................... 143
vi 9.3.1 Initial Injury Risk Models ............................................................................... 143 Conclusions ............................................................................................................ 146 10 Correlate MASH Intrusion Criteria with Real-World Injury ...................................... 147 Introduction ........................................................................................................... 147 Methods .................................................................................................................. 147 10.2.1 Analysis of Occupant Compartment Intrusion Magnitude and Occupant Injury .............................................................................................. 147 10.2.2 Evaluation of Current MASH Occupant Compartment Intrusion Limits ....... 149 10.2.3 Estimation of Real-World Frequency of Vehicle Damage Patterns ............... 151 Available Cases ...................................................................................................... 152 10.3.1 Analysis of Occupant Compartment Intrusion Magnitude and Occupant Injury .............................................................................................. 152 10.3.2 Evaluation of Current MASH Occupant Compartment Intrusion Limits ....... 153 10.3.3 Estimation of Real-World Frequency of Vehicle Damage Patterns ............... 153 Analysis Results ..................................................................................................... 154 10.4.1 Analysis of Occupant Compartment Intrusion Magnitude and Occupant Injury .............................................................................................. 154 10.4.2 Evaluation of Current MASH Occupant Compartment Intrusion Limits ....... 155 10.4.3 Estimation of Real-World Frequency of Vehicle Damage Patterns ............... 158 Discussion ............................................................................................................... 159 10.5.1 Analysis of Occupant Compartment Intrusion Magnitude and Occupant Injury .............................................................................................. 159 10.5.2 Evaluation of Current MASH Occupant Compartment Intrusion Limits ....... 159 10.5.3 Estimation of Real-World Frequency of Vehicle Damage Patterns ............... 160 Conclusions ............................................................................................................ 160 11 Assess Candidate Metric Ability to Predict Occupant Acceleration ............................ 162 Introduction ........................................................................................................... 162 Methods .................................................................................................................. 162 11.2.1 Determine Available Full-Scale Crash Test Data ........................................... 162 11.2.2 Compute ATD-based Injury Criteria .............................................................. 162 11.2.3 Compute FSM Parameters and Other Vehicle-Based Metrics ....................... 163 11.2.4 Determine FSM Ability to Predict Occupant Acceleration ............................ 165 Results ..................................................................................................................... 165 11.3.1 Available Full-Scale Crash Test Data ............................................................. 165 11.3.2 ATD-Based Injury Criteria Summary ............................................................. 165 11.3.3 Vehicle-Based Metrics Summary ................................................................... 166 11.3.4 Correlation of Vehicle-Based and ATD-Based Metrics ................................. 167 11.3.5 Discussion ....................................................................................................... 170 Conclusions ............................................................................................................ 170 12 Compare FSM and Alternate Metrics in Roadside Crash Tests .................................. 172
vii Introduction and Objective .................................................................................. 172 Methods .................................................................................................................. 172 12.2.1 Determine Available MASH Full-Scale Crash Tests and Associated Characteristics ................................................................................................ 172 12.2.2 Pilot MASH Crash Test Data and Computational Validation Procedure ....... 173 12.2.3 MASH Crash Test Sample Selection Process ................................................. 173 12.2.4 MASH Crash Test Sample: Computations and Preliminary Analysis............ 174 Results ..................................................................................................................... 175 12.3.1 Available MASH Full-Scale Crash Tests ....................................................... 175 12.3.2 Available MASH Full-Scale Crash Test Characteristics ................................ 175 12.3.3 MASH Test Selection ..................................................................................... 176 12.3.4 Validation of Occupant Risk Values for Pilot MASH Tests .......................... 177 12.3.5 Computation of Metrics for Sample MASH Tests ......................................... 180 Conclusions ............................................................................................................ 184 13 Proposed Implementation of Results in MASH ............................................................ 185 Introduction ........................................................................................................... 185 Approach to Developing Proposed MASH Modification Options .................... 185 13.2.1 Developed Models and Statistically Significant Predictors ............................ 185 13.2.2 Determine Occupant Injury Risk Bounds for Current MASH Thresholds ..... 185 13.2.3 Evaluate Candidate Injury Metric Performance ............................................. 186 13.2.4 Review Correlation of MASH Intrusion Criteria with Real-World Crash Injury .................................................................................................... 187 13.2.5 Injury Risk Curves for MASH Metric(s) ........................................................ 187 13.2.6 Develop Proposed Options for Updating MASH and Evaluate Potential Implications .................................................................................................... 187 13.2.7 Develop Suggested Modifications to Existing MASH Text ........................... 187 Results and Discussion .......................................................................................... 188 13.3.1 Developed Models and Statistically Significant Predictors ............................ 188 13.3.2 Determine Occupant Injury Risk Bounds for Current MASH Thresholds ..... 191 13.3.3 Evaluate Candidate Injury Metric Performance ............................................. 194 13.3.4 Review Correlation of MASH Intrusion Criteria with Real-World Crash Injury .................................................................................................... 199 13.3.5 Injury Risk Curves for MASH Metric(s) ........................................................ 202 13.3.6 Proposed Options for Updating MASH .......................................................... 206 13.3.7 Evaluate Potential Implications of Proposed Options for Updating MASH ............................................................................................. 207 MASH Modification Recommendation ............................................................... 211 14 Future Roadmap for Updates to MASH Injury Risk Evaluation ................................ 213 Introduction and Objective .................................................................................. 213 Summary of Future Occupant Risk Research Needs ......................................... 213 Occupant Risk Implications of a Changing Vehicle Fleet ................................. 213
viii 14.3.1 Passive Safety Improvements ......................................................................... 214 14.3.2 Vehicle Fleet Changes .................................................................................... 214 14.3.3 Research Objective ......................................................................................... 214 14.3.4 Implementation Considerations ...................................................................... 215 14.3.5 Recommended Research Funding and Research Period ................................. 215 Use of ATDs to Assess Occupant Risk in Roadside Hardware Testing ........... 215 14.4.1 Validation of THOR in Oblique Impacts ........................................................ 215 14.4.2 Development of Improved 5th Female ATD .................................................. 216 14.4.3 Research Objective ......................................................................................... 216 14.4.4 Implementation Considerations ...................................................................... 216 14.4.5 Recommended Research Funding and Research Period ................................. 217 Use of Computer Simulation to Assess Occupant Risk in Roadside Hardware Testing .................................................................................................................... 217 14.5.1 Human Body Models ...................................................................................... 217 14.5.2 Research Objective ......................................................................................... 218 14.5.3 Implementation Considerations ...................................................................... 218 14.5.4 Recommended Research Funding and Research Period ................................. 218 Implications of AVs on Roadside Hardware Occupant Risk Assessment ....... 219 14.6.1 HAVs and AVs ............................................................................................... 219 14.6.2 Research Objective ......................................................................................... 219 14.6.3 Implementation Considerations ...................................................................... 220 14.6.4 Recommended Research Funding and Research Period ................................. 220 15 Conclusions .................................................................................................................... 221 Summary of Current Practices ............................................................................ 221 15.1.1 Existing MASH Roadside Hardware Crash Testing and Occupant Risk Procedures ...................................................................................................... 221 15.1.2 Gaps and Research Needs ............................................................................... 221 Methods of Evaluation .......................................................................................... 222 15.2.1 Building the IAD............................................................................................. 222 15.2.2 Analysis of Candidate Injury Metrics in Frontal, Side, and Oblique Crashes ............................................................................................. 222 15.2.3 Investigation of MASH Occupant Compartment Intrusion Limits ................. 223 Research Findings ................................................................................................. 223 15.3.1 Analysis of the Candidate Injury Metrics in Frontal, Side, and Oblique Crashes ............................................................................................. 223 15.3.2 Investigation of MASH Occupant Compartment Intrusion Limits ................. 225 Recommendations ................................................................................................. 225 References.............................................................................................................................. 227 Appendix A: Literature Review Supplemental Material ...................................................... 235 Appendix B: Chapter 11 Supplemental Material ................................................................. 255 Appendix C: Chapter 4 Supplemental Material ................................................................... 272
ix C.1 Metric Cumulative Distribution Plots for Each Crash Mode ............................... 272 C.2 Frontal Crash Initial Models .................................................................................... 279 C.3 Frontal Crash Region-Specific Initial Models ........................................................ 280 Head and Face ................................................................................................................ 280 Neck and C-Spine .......................................................................................................... 282 Thorax, Abdomen, L-Spine, and T-Spine ...................................................................... 283 Appendix D: Chapter 6 Supplemental Material................................................................... 285 D.1 Side Crash Initial Models ......................................................................................... 285 D.2 Side Crash Region-Specific Initial Models ............................................................. 286 Head and Face ................................................................................................................ 286 Appendix E: Chapter 7 Supplemental Material ................................................................... 288 E.1 Oblique Crash Initial Models ................................................................................... 288 Appendix F: Links to Publicly Available Crash Datasets ................................................... 290
x List of Figures Figure 2-1. FSM assumptions and simplifications (Gabauer and Gabler 2008a). .......................... 8 Figure 2-2. Maximum occupant injury severity in 58 frontal collisions as a function of FSM risk values (Figure 2, Gabauer and Gabler 2004). ....................................................................... 16 Figure 2-3. Schematic of the VPI impact severity metric that explicitly models the occupant and restraints as a mass-spring system (Tsoi and Gabler 2015). ................................................. 34 Figure 2-4. Graphical representation of the OLC (Figure 4; Wusk and Gabler 2017). ................ 36 Figure 3-1. Assembling the IAD from four real-world crash databases. The available EDR data were used to compute the candidate metric values. .............................................................. 54 Figure 3-2. Graphical summary of injury risk curve development for each candidate injury risk metric. Because human injury tolerance is a strong function of impact direction, separate risk curves will be developed for pure frontal loading, side (lateral) loading, and oblique loading................................................................................................................................... 55 Figure 3-3. Graphical summary of the comparison of candidate injury metrics using the test dataset. .................................................................................................................................. 57 Figure 5-1. Frontal impact MDV injury risk curve for an occupant older than 12 and younger than 65. .................................................................................................................................. 84 Figure 5-2. Frontal impact OIV injury risk curve for an occupant at least 13 and younger than 65 years. ..................................................................................................................................... 84 Figure 5-3. Frontal impact OIV+RA injury risk curve for an occupant at least 13, younger than 65 years, and with an RA less than 15 g. .............................................................................. 85 Figure 5-4. Frontal impact OLC injury risk curve for an occupant at least 13 and younger than 65 with a PDOF of zero. ............................................................................................................ 85 Figure 5-5. Frontal impact ASI injury risk curve for an occupant at least 13 and younger than 65, regardless of belt status, with a PDOF of zero. ..................................................................... 86 Figure 5-6. Frontal impact VPI injury risk curve for an occupant at least 13 and younger than 65 years, regardless of belt status, with a PDOF of zero. .......................................................... 86 Figure 5-7. Frontal impact MDV injury risk curves for all three regions. The HF curves apply to all front-seated occupants in passenger cars. The N and TALT curves apply to occupants older than 12 and younger than 65. ....................................................................................... 91 Figure 5-8. Frontal impact OIV injury risk curves for all three regions. The HF curves apply to all front-seated occupants in passenger cars. The N and TALT curves apply to occupants older than 12 and younger than 65. ....................................................................................... 91 Figure 5-9. Frontal impact OLC injury risk curves for all three regions. The HF curves apply to all front-seated occupants in passenger cars. The N and TALT curves apply to occupants older than 12 and younger than 65. ....................................................................................... 92 Figure 5-10. Frontal impact ASI injury risk curves for all three regions. The HF curves apply to all front-seated occupants in passenger cars. The N and TALT curves apply to occupants older than 12 and younger than 65. ....................................................................................... 92 Figure 5-11. Frontal impact VPI injury risk curves for all three regions. The HF curves apply to all front-seated occupants in passenger cars. The N and TALT curves apply to occupants older than 12 and younger than 65. ....................................................................................... 93 Figure 6-1. Side impact MDV injury risk curves for drivers and right front passengers, at least 13 years old, in passenger vehicles. ......................................................................................... 104
xi Figure 6-2. Side impact OIV injury risk curves for drivers and right passengers, at least 13 years old, in passenger vehicles. .................................................................................................. 104 Figure 6-3. Side impact OLC injury risk curves for drivers and right front passengers, at least 13 years old, in passenger vehicles. ......................................................................................... 105 Figure 6-4. Side impact ASI injury risk curves for drivers and right front passengers, at least 13 years old, in passenger vehicles. ......................................................................................... 105 Figure 6-5. Side impact VPI injury risk curves for drivers and right front passengers, at least 13 years old, in passenger vehicles. ......................................................................................... 106 Figure 6-6. Side impact MDV injury risk curves for the HF region for front-seated occupants at least 13 years old. ................................................................................................................ 108 Figure 6-7. Side impact OIV injury risk curves for the HF region for front-seated occupants at least 13 years old. ................................................................................................................ 108 Figure 6-8. Side impact OLC injury risk curves for the HF region for front-seated occupants at least 13 years old. ................................................................................................................ 109 Figure 6-9. Side impact ASI injury risk curves for the HF region for front-seated occupants at least 13 years old. ................................................................................................................ 109 Figure 6-10. Side impact VPI injury risk curves for the HF region for front-seated occupants at least 13 years old. ................................................................................................................ 110 Figure 7-1. Oblique impact MDV injury risk curve for front row occupants in oblique crashes. These curves come from the final MDV model.................................................................. 119 Figure 7-2. Oblique impact OIV injury risk curves for front row occupants in oblique crashes. These curves come from the final OIV model. ................................................................... 119 Figure 7-3. Oblique impact OLC injury risk curve for front row occupants in oblique crashes. These curves come from the final OLC model. .................................................................. 120 Figure 7-4. Oblique impact ASI injury risk curve for front row occupants in oblique crashes. These curves come from the final ASI model. ................................................................... 120 Figure 7-5. Oblique impact VPI injury risk curve for front row occupants in oblique crashes. These curves come from the final VPI model. ................................................................... 121 Figure 8-1. Frontal crash MDV Harm prediction curves for male drivers, under the age of 65 with a BMI less than 25 kg/m2, who did not strike a tree or pole. MDV, belt status, sex, age, BMI, seating location, and object contacted explain 52% of the variance associated with the model................................................................................................................................... 131 Figure 8-2. Frontal crash OIV Harm prediction curves for passenger car drivers under the age of 65 with a BMI less than 25 kg/m2. OIV, belt status, sex, age, BMI, seating location, and object contacted explain 52% of the variance associated with the model. ......................... 131 Figure 8-3. Frontal crash OLC Harm prediction curves for passenger car drivers under the age of 65 with a BMI less than 25 kg/m2. OLC, age, BMI, seating location, and vehicle body type explain 46% of the variance associated with the model. .................................................... 132 Figure 8-4. Frontal crash ASI Harm prediction curves for male drivers under the age of 65 with a BMI less than 25 kg/m2. ASI, belt status, sex, age, BMI, and seating location type explain 51% of the variance associated with the model. ................................................................. 132 Figure 8-5. Frontal crash VPI Harm prediction curves for male drivers under the age of 65 with a BMI less than 25 kg/m2. VPI, belt status, sex, age, BMI, and seating location type explain 49% of the variance associated with the model. ................................................................. 132 Figure 8-6. Oblique crash MDV Harm prediction curves for front row occupants in vehicles that did not strike a tree or pole and have their GAD to the front of the vehicle. MDV, belt
xii status, GAD, and object contacted explain 42% of the variance associated with the model................................................................................................................................... 133 Figure 8-7. Oblique crash OIV Harm prediction curves for front row occupants in vehicles that did not strike a tree or pole. OIV, belt status, and object contacted explain 36% of the variance associated with the model. .................................................................................... 133 Figure 8-8. Oblique crash OLC Harm prediction curves for front row occupants in vehicles that did not strike a tree or pole. OLC, belt status, and object contacted explain 35% of the variance associated with the model. .................................................................................... 133 Figure 8-9. Oblique crash ASI Harm prediction curves for front row occupants in vehicles that did not strike a tree or pole. ASI, belt status, and object contacted explain 36% of the variance associated with the model. .................................................................................... 134 Figure 8-10. Oblique crash VPI Harm prediction curves for front row occupants in vehicles that did not strike a tree or pole. VPI, belt status, and object contacted explain 35% of the variance associated with the model. .................................................................................... 134 Figure 8-11. Side crash MDV Harm prediction curves for front row occupants younger than 65 years old. MDV, belt status, side impact type, and age explain 34% of the variance associated with the model. .................................................................................................. 134 Figure 8-12. Side crash OIV Harm prediction curves for front row occupants younger than 65 years old. OIV, belt status, side impact type, and age explain 34% of the variance associated with the model..................................................................................................................... 135 Figure 8-13. Side crash OLC Harm prediction curves for front row occupants younger than 65 years old. OLC, belt status, side impact type, and age explain 31% of the variance associated with the model. .................................................................................................. 135 Figure 8-14. Side crash ASI Harm prediction curves for front row occupants younger than 65 years old. ASI, belt status, side impact type, and age explain 34% of the variance associated with the model..................................................................................................................... 135 Figure 8-15. Side crash VPI Harm prediction curves for front row occupants younger than 65 years old. VPI, belt status, side impact type, and age explain 34% of the variance associated with the model..................................................................................................................... 136 Figure 12-1. Longitudinal and lateral occupant impact values in sample MASH tests (n = 124). ............................................................................................................................. 181 Figure 12-2. Longitudinal and lateral RA values in sample MASH tests (n = 124)................... 181 Figure 12-3. Longitudinal and lateral OLC values in sample MASH tests (n = 124). ............... 182 Figure 12-4. Longitudinal and lateral MDV values in sample MASH tests (n = 124). .............. 182 Figure 12-5. Longitudinal and lateral VPI values in sample MASH tests (n = 124). ................. 183 Figure 13-1. Longitudinal and lateral OIV values by test outcome for sample MASH tests compared to current threshold values. ................................................................................ 209 Figure 13-2. Longitudinal and lateral OLC values by test outcome for sample MASH tests compared to potential threshold values............................................................................... 210 Figure 14-1. There is currently no consensus regarding the use of FEM for the regulatory testing of occupant risk. However, future MASH iterations may wish to re-consider this method, if and when it is accepted by federal agencies such as NHTSA. ........................................... 217 Figure 14-2. Two concepts for innovative AV seating arrangements that may require updates to MASH occupant risk evaluation methods: rear-facing front seats with central table (left) and both inboard- and rear-facing seats (right). ......................................................................... 219
xiii Figure A-1. Summary of occupant clearance dimensions measured in NHTSA NCAP tests used to verify FSM occupant compartment dimensions (Table 1, Ray et al. 1986). .................. 235 Figure A-2. Summary of frontal and side sled tests to investigate the FSM (Table 2, Ray et al. 1986). .................................................................................................................................. 236 Figure A-3. Occupant injury from initial barrier impact as a function of lateral OIV based on 17 reconstructed real-world longitudinal barrier crashes (Figure 5, Ray et al. 1987b). .......... 237 Figure A-4. Summary of sled tests to evaluate different windshields (Table 3, Begeman et al. 1978). .................................................................................................................................. 238 Figure A-5. Summary of sled tests to evaluate an improved laminated windshield (Table 5, Kay et al. 1973). ......................................................................................................................... 241 Figure A-6. Summary of full-scale impact attenuator crash tests and associated FSM and ATD injury metrics (Table 3, Hinch et al. 1988). ........................................................................ 243 Figure A-7. Graphical summary of unrestrained ATD injury metric values as a function of OIV and RA from Hinch et al. (1988) full-scale impact attenuator crash tests. ......................... 245 Figure A-8. Summary of OIV, ASI, and delta-v injury risk curves for belted and unbelted occupants developed by Gabauer and Gabler (Figures 5-16, Gabauer and Gabler 2008). . 246 Figure A-9. Summary of ASI, delta-v, OIV, and VPI injury risk curves for belted and unbelted occupants developed by Tsoi and Gabler (Figure 2, Tsoi and Gabler 2015). .................... 248 Figure A-10. Graphical summary of ATD injury metric values as a function of longitudinal ASI computed from head-on Hinch et al. (1988) full-scale impact attenuator crash tests. ........ 249 Figure B-1. Scatter plots of HIC15 as a function of vehicle-based metrics: Frontal barrier tests. ......................................................................................................................... 255 Figure B-2. Scatter plots of HIC36 as a function of vehicle-based metrics: Frontal barrier tests. ......................................................................................................................... 256 Figure B-3. Scatter plots of Nij as a function of vehicle-based metrics: Frontal barrier tests. .... 257 Figure B-4. Scatter plots of 3-ms clip as a function of vehicle-based metrics: Frontal barrier tests. ......................................................................................................................... 258 Figure B-5. Scatter plots of chest deflection as a function of vehicle-based metrics: Frontal barrier tests. ......................................................................................................................... 259 Figure B-6. Scatter plots of maximum femur load as a function of vehicle-based metrics: Frontal barrier tests. ......................................................................................................................... 260 Figure B-7. Scatter plots of maximum tibia load as a function of vehicle-based metrics: Frontal barrier tests. ......................................................................................................................... 261 Figure B-8. Scatter plots of HIC15 as a function of vehicle-based metrics: Side impact tests. . 262 Figure B-9. Scatter plots of HIC36 as a function of vehicle-based metrics: Side impact tests. . 263 Figure B-10. Scatter plots of rib deflection as a function of vehicle-based metrics: Side impact tests. .................................................................................................................................... 264 Figure B-11. Scatter plots of rib deflection rate as a function of vehicle-based metrics: Side impact tests.......................................................................................................................... 265 Figure B-12. Scatter plots of lower spine resultant as a function of vehicle-based metrics: Side impact tests.......................................................................................................................... 266 Figure B-13. Scatter plots of HIC15 as a function of vehicle-based metrics: Side pole tests. ... 267 Figure B-14. Scatter plots of HIC36 as a function of vehicle-based metrics: Side pole tests. ... 268 Figure B-15. Scatter plots of rib deflection as a function of vehicle-based metrics: Side pole tests.............................................................................................................................. 269
xiv Figure B-16. Scatter plots of rib deflection rate as a function of vehicle-based metrics: Side pole tests. .................................................................................................................................... 270 Figure B-17. Scatter plots of lower spine resultant as a function of vehicle-based metrics: Side pole tests.............................................................................................................................. 271 Figure C-1. Cumulative distribution function for PDOF in the frontal crash dataset................. 272 Figure C-2. Cumulative distribution function for MDV and OIV in the frontal crash dataset... 272 Figure C-3. Cumulative distribution function for MDV and OIV in the side crash dataset. ...... 273 Figure C-4. Cumulative distribution function for OLC in the side crash dataset. ...................... 273 Figure C-5. Cumulative distribution function for ASI in the side crash dataset. ........................ 274 Figure C-6. Cumulative distribution function for VPI in the side crash dataset. ........................ 274 Figure C-7. Cumulative distribution function for OLC in the frontal crash dataset. .................. 275 Figure C-8. Cumulative distribution function for ASI in the frontal crash dataset. ................... 275 Figure C-9. Cumulative distribution function for VPI in the frontal crash dataset. ................... 276 Figure C-10. Cumulative distribution function for MDV and OIV in the oblique crash dataset. ................................................................................................................................ 276 Figure C-11. Cumulative distribution function for OLC in the oblique crash dataset. .............. 277 Figure C-12. Cumulative distribution function for ASI in the oblique crash dataset. ................ 277 Figure C-13. Cumulative distribution function for VPI in the oblique crash dataset. ................ 278
xv List of Tables Table 2-1. Historical summary of roadside hardware crash test procedures and associated occupant risk metrics. ............................................................................................................. 6 Table 2-2. NCHRP Report 153 redirection impact severity thresholds (Bronstad and Michie 1974). ...................................................................................................................................... 6 Table 2-3. NCHRP Report 86 equations relating vehicle damage, average vehicle deceleration, and occupant injury potential (Olson et al. 1970). .................................................................. 7 Table 2-4. Summary of sled tests to assess validity of the FSM (Ray et al. 1986, 1987b). ......... 10 Table 2-5. Summary of FSM threshold and preferred values. ...................................................... 12 Table 2-6. Summary of lateral sled tests and ATD-based injury risk (Ray et al. 1986, 1987b). .. 14 Table 2-7. Summary of EDR-based studies relating the FSM to real-world occupant injury. ..... 15 Table 2-8. Summary of crash/sled test studies relating the FSM to ATD-based occupant injury metrics. .................................................................................................................................. 18 Table 2-9. Summary of computer simulation studies relating the FSM injury metrics to ATD- based occupant injury metrics. .............................................................................................. 20 Table 2-10. Summary of THIV and PHD metric limiting values. ................................................ 23 Table 2-11. Summary of crash test and/or simulation studies relating THIV/PHD to other injury metrics. .................................................................................................................................. 24 Table 2-12. Summary of ASI metric limiting values. ................................................................... 26 Table 2-13. Summary of occupant acceleration limits by restraint type for ditch traversal (Weaver and Marquis 1974). ................................................................................................ 27 Table 2-14. Summary of EDR-based studies relating the ASI to real-world occupant injury. .... 28 Table 2-15. Summary of crash test studies relating ASI to ATD-based occupant injury metrics. 29 Table 2-16. Summary of crash test/simulation studies relating ASI to ATD-based injury metrics. .................................................................................................................................. 31 Table 2-17. Summary of potential vehicle-based crash injury metrics. ....................................... 32 Table 2-18. Summary of crash test/simulation studies relating OLC to ATD-based occupant injury metrics. ....................................................................................................................... 36 Table 2-19. Summary of MASH occupant compartment deformation limits (AASHTO 2016). . 40 Table 2-20. Summary of previously published studies related to occupant compartment intrusion. ............................................................................................................................... 41 Table 2-21. Average cost per injury (normalized to the cost of a fatal injury)............................. 43 Table 2-22. Summary of potential data sources to evaluate roadside crash injury metrics. ......... 44 Table 3-1. Existing and potential roadside crash injury metrics to be evaluated. ........................ 52 Table 3-2. AIS ISS. ....................................................................................................................... 53 Table 4-1. Real-world crash data sources to evaluate candidate injury metrics. .......................... 61 Table 4-2. Cases available for the IAD by impact type. ............................................................... 61 Table 4-3. Inclusion criteria for the final IAD. ............................................................................. 63 Table 4-4. Five possible categories for the cases with frontal damage. ....................................... 64 Table 4-5. Five possible categories for the cases with side damage. ............................................ 65 Table 4-6. Additional inclusion criteria for the final frontal, side, and oblique crash datasets. ... 66 Table 4-7. Training and test dataset case composition for frontal injury models. ........................ 67 Table 4-8. Dataset case composition for the side crash injury models. ........................................ 69 Table 4-9. Dataset case composition for oblique injury model. ................................................... 71 Table 4-10. IDC E codes indicating a vehicle crash was the cause of injury. .............................. 73
xvi Table 4-11. ICD E sub codes indicating the injured individual. ................................................... 73 Table 4-12. The top 10 most common traffic crashes to result in an occupant treated at a trauma center. .................................................................................................................................... 74 Table 4-13. Distribution of injury severity for occupants at trauma centers after a single vehicle collision (E816). .................................................................................................................... 74 Table 4-14. AIS 3+ injury distribution by body region for occupants at trauma centers after a single vehicle collision (E816). ............................................................................................. 74 Table 5-1. Validation method used for each injury metric. .......................................................... 77 Table 5-2. NASS/CDS body regions used to form the model body regions. ............................... 79 Table 5-3. Parameters for the ORA initial frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 81 Table 5-4. Parameters for the ORA initial frontal logistic regression model, accounting for OIV*ORA interaction, used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............................................................................................... 81 Table 5-5. Parameters for the MDV final frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 82 Table 5-6. Parameters for the OIV final frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 82 Table 5-7. Parameters for the OIV+RA final frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 82 Table 5-8. Parameters for the OLC final frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 83 Table 5-9. Parameters for the ASI final frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ........................... 83 Table 5-10. Parameters for the VPI final frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 83 Table 5-11. Parameters for the MDV logistic regression frontal HF model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 88 Table 5-12. Parameters for the OIV logistic regression frontal HF model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ........................... 88 Table 5-13. Parameters for the OLC logistic regression frontal HF model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 88 Table 5-14. Parameters for the ASI logistic regression frontal HF model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ........................... 88 Table 5-15. Parameters for the VPI logistic regression frontal HF model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ........................... 88 Table 5-16. Parameters for the MDV logistic regression frontal N model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ........................... 89 Table 5-17. Parameters for the OIV logistic regression frontal N model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ........................... 89 Table 5-18. Parameters for the OLC logistic regression frontal N model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ........................... 89 Table 5-19. Parameters for the ASI logistic regression frontal N model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ........................... 89 Table 5-20. Parameters for the VPI logistic regression frontal N model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ........................... 89
xvii Table 5-21. Parameters for the MDV logistic regression frontal TALT model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 90 Table 5-22. Parameters for the OIV logistic regression frontal TALT model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 90 Table 5-23. Parameters for the OLC logistic regression frontal TALT model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 90 Table 5-24. Parameters for the ASI logistic regression frontal TALT model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 90 Table 5-25. Parameters for the VPI logistic regression frontal TALT model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............ 90 Table 5-26. F2 scores for the six metricsâ frontal models. These values come from the final model..................................................................................................................................... 94 Table 5-27. F2 scores for the region-specific frontal models. ...................................................... 94 Table 5-28. Injury risk associated with the current OIV and ASI thresholds for the best- and worst-case frontal crash scenarios. ....................................................................................... 97 Table 5-29. Injury risk by body region associated with the current OIV and ASI thresholds for the best- and worst-case frontal impact scenarios................................................................. 97 Table 6-1. NASS/CDS body regions used to form the side body regions models. .................... 101 Table 6-2. Parameters for the MDV final side logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 102 Table 6-3. Parameters for the OIV final side logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 103 Table 6-4. Parameters for the OLC final side logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 103 Table 6-5. Parameters for the ASI final side logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 103 Table 6-6. Parameters for the VPI final side logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 103 Table 6-7. Parameters for the MDV logistic regression side HF model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 107 Table 6-8. Parameters for the OIV logistic regression side HF model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 107 Table 6-9. Parameters for the OLC logistic regression side HF model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 107 Table 6-10. Parameters for the ASI logistic regression side HF model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 107 Table 6-11. Parameters for the VPI logistic regression side HF model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 107 Table 6-12. F2 scores for the five metricsâ side models. These values come from the final model. ......................................................................................................................... 111 Table 6-13. F2 scores for the side HF models. ........................................................................... 111 Table 6-14. Injury risk associated with the current OIV and ASI thresholds for the best- and worst-case side impact scenarios. ....................................................................................... 112 Table 6-15. HF injury risk associated with the current OIV and ASI thresholds for the best- and worst-case side impact scenarios. ....................................................................................... 112 Table 7-1. NASS/CDS body regions used to form the oblique body region models. ................ 116
xviii Table 7-2. Parameters for the MDV final oblique logistic regression oblique model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 118 Table 7-3. Parameters for the OIV final oblique logistic regression oblique model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 118 Table 7-4. Parameters for the OLC final oblique logistic regression oblique model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 118 Table 7-5. Parameters for the ASI final oblique logistic regression oblique model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 118 Table 7-6. Parameters for the VPI final oblique logistic regression oblique model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 118 Table 7-7. F2 scores for the five metricsâ models. These values come from the final model. ... 122 Table 7-8. Injury risk associated with the current OIV and ASI thresholds for the best- and worst-case oblique impact scenarios. .................................................................................. 123 Table 8-1. Average cost per injury in thousands of U.S. dollars. ............................................... 125 Table 8-2. Parameters for the MDV final frontal linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). ......................................................... 127 Table 8-3. Parameters for the OIV final frontal linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). ......................................................... 127 Table 8-4. Parameters for the OLC final frontal linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). ......................................................... 127 Table 8-5. Parameters for the ASI final frontal linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). ......................................................... 127 Table 8-6. Parameters for the VPI final frontal linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). ......................................................... 127 Table 8-7. Parameters for the MDV final oblique linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). ......................................................... 129 Table 8-8. Parameters for the OIV final oblique linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). ......................................................... 129 Table 8-9. Parameters for the OLC final oblique linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). ......................................................... 129 Table 8-10. Parameters for the ASI final oblique linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). ......................................................... 129 Table 8-11. Parameters for the VPI final oblique linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). ......................................................... 129 Table 8-12. Parameters for the MDV final side linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). ......................................................... 129 Table 8-13. Parameters for the OIV final side linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). .............................................................. 130 Table 8-14. Parameters for the OLC final side linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). .............................................................. 130 Table 8-15. Parameters for the ASI final side linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). .............................................................. 130 Table 8-16. Parameters for the VPI final side linear regression model used to predict âHarm. ** indicates statistical significance (p-value < 0.05). .............................................................. 130 Table 8-17. RMSE for the frontal, oblique, and side Harm models for each metric. ................. 136
xix Table 8-18. Summary of real-world occupant Harm cost associated with current roadside hardware injury metric thresholds: Overall MAIS2+F injury in frontal crashes. ............... 138 Table 8-19. Summary of real-world occupant Harm cost associated with current roadside hardware injury metric thresholds: Overall MAIS2+F injury in side crashes. ................... 138 Table 8-20. Summary of real-world occupant Harm cost associated with current roadside hardware injury metric thresholds: Overall MAIS2+F injury in oblique crashes. ............. 138 Table 9-1. Parameters for the MDV-Pjoint frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 144 Table 9-2. Parameters for the OIV-Pjoint frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 144 Table 9-3. Parameters for the OLC-Pjoint frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 144 Table 9-4. Parameters for the ASI-Pjoint frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 144 Table 9-5. Parameters for the VPI-Pjoint frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 145 Table 9-6. Parameters for the vehicle-specific VPI frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 145 Table 9-7. Parameters for the occupant-specific VPI frontal logistic regression frontal model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 145 Table 10-1. Summary of MASH occupant compartment deformation limits (AASHTO 2016).147 Table 10-2. Summary of NASS/CDS intrusion magnitude. ....................................................... 149 Table 10-3. Mapping of MASH occupant compartment deformation areas to NASS/CDS intruding component. .......................................................................................................... 150 Table 10-4. Summary of weighted (unweighted) front row nearside occupants by maximum relevant intrusion level and object struck for available NASS/CDS cases (2000 â 2015, inclusive). ............................................................................................................................ 153 Table 10-5. Summary characteristics of weighted and unweighted occupants for inclusion in the MASH intrusion evaluation dataset (NASS/CDS 2000-2015 inclusive). .......................... 154 Table 10-6. Summary of unweighted (weighted) NASS/CDS single vehicle roadside hardware impacts by object struck...................................................................................................... 154 Table 10-7. Summary of statistically significant chi-square test results comparing maximum relevant intrusion level to nearside occupant maximum linked injury level. ..................... 155 Table 10-8. Parameters for the binary logistic regression model used to predict occupant MAIS1+F injuries using overall MASH intrusion. ** indicates statistical significance (p- value < 0.05). ...................................................................................................................... 156 Table 10-9. Parameters for the binary logistic regression model used to predict occupant MAIS2+F injuries using overall MASH intrusion. ** indicates statistical significance (p- value < 0.05). ...................................................................................................................... 156 Table 10-10. Parameters for the binary logistic regression model used to predict occupant MAIS3+F injuries using overall MASH intrusion. ** indicates statistical significance (p- value < 0.05). ...................................................................................................................... 156 Table 10-11. Summary of odds ratio results for the MAIS1+F, MAIS2+F, and MAIS3+F injury models. ................................................................................................................................ 157
xx Table 10-12. Abbreviated summary of odds ratio results for the MAIS1+F, MAIS2+F, and MAIS3+F injury models with area specific intrusion variables. ........................................ 158 Table 10-13. Summary of intrusion rates for roadside safety hardware devices. ....................... 158 Table 11-1. Summary of typical ATD type and seating position by NHTSA test type. ............ 163 Table 11-2. Included ATD injury metrics and applicable NHTSA test types. ........................... 163 Table 11-3. Summary of late model year vehicle crash test data available in the NHTSA vehicle crash test database. .............................................................................................................. 165 Table 11-4. Summary of occupant injury metrics for late model frontal barrier vehicle crash tests. ........................................................................................................................... 166 Table 11-5. Summary of occupant injury metrics for late model side impact pole crash tests. . 166 Table 11-6. Summary of occupant injury metrics for late model side impact vehicle crash tests. ........................................................................................................................... 166 Table 11-7. Summary of vehicle-based metrics for late model vehicle crash tests by test type. 167 Table 11-8. Summary of linear regression fits for frontal barrier tests: Drivers. ....................... 168 Table 11-9. Summary of linear regression fits for frontal barrier tests: Right front passengers. 168 Table 11-10. Summary of linear regression fits for side impact tests: Drivers. ......................... 169 Table 11-11. Summary of linear regression fits for side impact tests: Left rear passengers. ..... 169 Table 11-12. Summary of linear regression fits for side pole tests: Drivers. ............................. 170 Table 12-1. Summary of MASH test selection considerations. .................................................. 173 Table 12-2. Summary of non-proprietary MASH tests conducted by device type and test facility. .......................................................................................................................... 175 Table 12-3: Summary of occupant risk parameter values for available MASH tests by testing facility. ................................................................................................................................ 175 Table 12-4: Summary of occupant risk parameter values for available MASH tests by test vehicle. ................................................................................................................................ 176 Table 12-5. Summary of selected non-proprietary MASH tests by device type and test facility. .......................................................................................................................... 177 Table 12-6: Summary of occupant risk parameter values for minimum MASH test sample. .... 177 Table 12-7. Summary of pilot MASH crash test data obtained. ................................................. 178 Table 12-8. Summary of computed and reported OIV values for MASH pilot tests. ................ 179 Table 12-9. Summary of computed and reported RA values for MASH pilot tests. .................. 179 Table 12-10. Summary of computed and reported ASI values for MASH pilot tests. ............... 180 Table 12-11. Summary of computed occupant metric values for sample MASH tests. ............. 183 Table 13-1. High-level summary of comparison of candidate roadside crash injury metrics. ... 186 Table 13-2. Case counts and statistically significant variables for the final frontal impact MAIS2+F models. .............................................................................................................. 188 Table 13-3. Case counts and statistically significant variables for the final oblique impact MAIS2+F models. .............................................................................................................. 188 Table 13-4. Case counts and statistically significant variables for the final side impact MAIS2+F models. ................................................................................................................................ 189 Table 13-5. Case counts and statistically significant variables for the final Harm models. ....... 191 Table 13-6. Summary of real-world occupant injury risk associated with current roadside hardware injury metric thresholds: Overall MAIS2+F injury in frontal crashes. ............... 192 Table 13-7. Summary of real-world occupant injury risk associated with current roadside hardware injury metric thresholds: Body region MAIS2+F injury in frontal crashes. ....... 192
xxi Table 13-8. Summary of real-world occupant injury risk associated with current roadside hardware injury metric thresholds: Overall MAIS2+F injury in side crashes. ................... 192 Table 13-9. Summary of real-world occupant injury risk associated with current roadside hardware injury metric thresholds: Body region MAIS2+F injury in side crashes. ........... 193 Table 13-10. Summary of real-world occupant injury risk associated with current roadside hardware injury metric thresholds: Overall MAIS2+F injury in oblique crashes. ............. 193 Table 13-11. Summary of real-world occupant Harm cost associated with current roadside hardware injury metric thresholds: Frontal crashes. ........................................................... 193 Table 13-12. Summary of real-world occupant Harm cost associated with current roadside hardware injury metric thresholds: Side crashes. ............................................................... 193 Table 13-13. Summary of real-world occupant Harm cost associated with current roadside hardware injury metric thresholds: Oblique crashes. .......................................................... 193 Table 13-14. Summary of final candidate metric model performance for overall MAIS2+F injury: Frontal crash mode. ................................................................................................. 195 Table 13-15. Summary of final candidate metric model performance for body region MAIS2+F injury: Frontal crash mode. ................................................................................................. 195 Table 13-16. Summary of final candidate metric model performance for overall MAIS2+F injury: Side crash mode. ..................................................................................................... 195 Table 13-17. Summary of final candidate metric model performance for body region MAIS2+F injury: Side crash mode. ..................................................................................................... 196 Table 13-18. Summary of final candidate metric model performance for overall MAIS2+F injury: Oblique crash mode. ................................................................................................ 196 Table 13-19. Summary of final candidate metric model performance for Harm predictions: Frontal, oblique, and side crash modes. .............................................................................. 197 Table 13-20. Summary of final candidate metric model R2 values for ATD-based injury: Frontal, side, and side pole crash tests. ............................................................................................ 198 Table 13-21. Summary of statistically significant chi-square test results comparing maximum relevant intrusion level to nearside occupant maximum linked injury level. ..................... 199 Table 13-22. Summary of odds ratio results for the MAIS1+F, MAIS2+F, and MAIS3+F intrusion-injury models. ...................................................................................................... 201 Table 13-23. Abbreviated summary of odds ratio results for the MAIS1+F, MAIS2+F, and MAIS3+F intrusion-injury models with area specific intrusion variables. ........................ 201 Table 13-24. Summary of binary logistic regression MAIS2+F model equations based on candidate vehicle-based metrics. ........................................................................................ 203 Table 13-25. Summary of MAIS2+F frontal impact logistic regression model parameters by candidate injury metric. ...................................................................................................... 204 Table 13-26. Summary of MAIS2+F side impact logistic regression model parameters by candidate injury metric. ...................................................................................................... 204 Table 13-27. Summary of MAIS2+F oblique impact logistic regression model parameters by candidate injury metric. ...................................................................................................... 204 Table 13-28. Summary of binary logistic regression model equations for MAIS2+F intrusion- based model. ....................................................................................................................... 205 Table 13-29. Summary of MAIS2+F frontal impact logistic regression model parameters for intrusion-based model. ........................................................................................................ 205 Table 13-30. Summary of MASH occupant risk modification options. ..................................... 206
xxii Table 13-31. Summary of potential implications of modifying the lateral OIV threshold (Option 1). ........................................................................................................................... 208 Table 13-32. Summary of occupant MAIS2+F injury risk for various lateral OIV thresholds (Option 1). ........................................................................................................................... 208 Table 13-33. Summary of occupant MAIS2+F injury risk for OIV and potential OLC thresholds (Option 2). ........................................................................................................................... 210 Table 13-34. Summary of potential implications of replacing RA with ASI (Option 3). .......... 211 Table 14-1. Potential MASH occupant injury criteria research needs. ....................................... 213 Table A-1. High-level summary of existing vehicle-based injury metric studies. ..................... 250 Table A-2. High-level summary of potential vehicle-based injury metric studies. .................... 254 Table C-1. Parameters for the MDV initial frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 279 Table C-2. Parameters for the OIV initial frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 279 Table C-3. Parameters for the OIV initial frontal logistic regression model with RA as a binary covariate used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 279 Table C-4. Parameters for the OLC initial frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 279 Table C-5. Parameters for the ASI initial frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 280 Table C-6. Parameters for the VPI initial frontal logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 280 Table C-7. Parameters for the MDV logistic regression model used to predict occupant MAIS2+F HF injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 280 Table C-8. Parameters for the OIV logistic regression model used to predict occupant MAIS2+F HF injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). ........ 281 Table C-9. Parameters for the OLC logistic regression model used to predict occupant MAIS2+F HF injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). ........ 281 Table C-10. Parameters for the ASI logistic regression model used to predict occupant MAIS2+F HF injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). ........ 281 Table C-11. Parameters for the VPI logistic regression model used to predict occupant MAIS2+F HF injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). ........ 281 Table C-12. Parameters for the MDV logistic regression model used to predict occupant MAIS2+F N injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 282 Table C-13. Parameters for the OIV logistic regression model used to predict occupant MAIS2+F N injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 282 Table C-14. Parameters for the OLC logistic regression model used to predict occupant MAIS2+F N injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 282 Table C-15. Parameters for the ASI logistic regression model used to predict occupant MAIS2+F N injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05)............ 282
xxiii Table C-16. Parameters for the VPI logistic regression model used to predict occupant MAIS2+F N injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05)............ 282 Table C-17. Parameters for the MDV logistic regression model used to predict occupant MAIS2+F TALT injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 283 Table C-18. Parameters for the OIV logistic regression model used to predict occupant MAIS2+F TALT injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 283 Table C-19. Parameters for the OLC logistic regression model used to predict occupant MAIS2+F TALT injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 283 Table C-20. Parameters for the ASI logistic regression model used to predict occupant MAIS2+F TALT injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). ... 283 Table C-21. Parameters for the VPI logistic regression model used to predict occupant MAIS2+F TALT injuries in frontal crashes. ** indicates statistical significance (p-value < 0.05). ... 284 Table D-1. Parameters for the MDV initial side logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .......... 285 Table D-2. Parameters for the OIV initial side logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 285 Table D-3. Parameters for the OLC initial side logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 285 Table D-4. Parameters for the ASI initial side logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 285 Table D-5. Parameters for the VPI initial side logistic regression model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ......................... 286 Table D-6. Parameters for the MDV logistic regression model used to predict occupant MAIS2+F injuries in side crashes. ** indicates statistical significance (p-value < 0.05). . 286 Table D-7. Parameters for the OIV logistic regression model used to predict occupant MAIS2+F injuries in side crashes. ** indicates statistical significance (p-value < 0.05). ................... 286 Table D-8. Parameters for the OLC logistic regression model used to predict occupant MAIS2+F injuries in side crashes. ** indicates statistical significance (p-value < 0.05). ................... 286 Table D-9. Parameters for the ASI logistic regression model used to predict occupant MAIS2+F injuries in side crashes. ** indicates statistical significance (p-value < 0.05). ................... 287 Table D-10. Parameters for the VPI logistic regression model used to predict occupant MAIS2+F injuries in side crashes. ** indicates statistical significance (p-value < 0.05). ................... 287 Table E-1. Parameters for the MDV initial oblique logistic regression oblique model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 288 Table E-2. Parameters for the OIV initial oblique logistic regression oblique model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 288 Table E-3. Parameters for the OLC initial oblique logistic regression oblique model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). .................................................................................................................. 288
xxiv Table E-4. Parameters for the ASI initial oblique logistic regression oblique model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............................................................................................................................................. 288 Table E-5. Parameters for the VPI initial oblique logistic regression oblique model used to predict occupant MAIS2+F injuries. ** indicates statistical significance (p-value < 0.05). ............................................................................................................................................. 289
xxv Acknowledgments The research team would like to express their gratitude to the NCHRP 17-90 Project Panel for their feedback on the earlier white papers from which this report was developed. We also want to thank Suphanat Juengprasertsak, Jincheng Yao, and Chad Gamler, Bucknell University students who helped analyze the real-world cases with intrusion and the NHTSA crash tests. We would like to thank Jordan Moon, a Virginia Tech student who helped collect and analyze the necessary NHTSA crash test data to construct the vehicle-specific Vehicle Pulse Index model. Special thanks go to the late Dr. Clay Gabler, the original principal investigator on this project. He was a former mentor and advisor to the authors of this report: Dr. Douglas J. Gabauer, Dr. Luke E. Riexinger, and Morgan E. Dean. His contributions were integral to this work, and his mentorship and friendship are dearly missed. This work would not have been possible without his passion, curiosity, integrity, and heart.
xxvi Summary Background The crash performance of roadside safety hardware, such as guardrails, is typically evaluated using full-scale crash tests with vehicles striking the device in representative worst-case impact scenarios. Each test is evaluated based on vehicle response, device response, and potential for injury to vehicle occupants. In the U.S., roadside hardware crash tests are conducted according to procedures outlined in AASHTOâs Manual for Assessing Safety Hardware (MASH). Canadian provinces have generally adopted the MASH testing procedures, while Australia, New Zealand, and Europe have developed similar hardware crash test procedures. The assessment of occupant risk is crucial, as the purpose of these devices is to minimize occupant injury. Unlike vehicle crashworthiness testing, an anthropomorphic test device (ATD; i.e., crash test dummy) is not used to assess occupant risk in crash tests involving roadside hardware due to the oblique nature of roadside impacts and the significant cost increase of ATDs for crash testing. As an alternative, the potential for occupant injury is assessed through metrics derived from vehicle kinematics measured during the crash test. MASH procedures currently assess occupant risk using the simplified point-mass flail space model (FSM). The European procedures use a variation of the FSM along with the Acceleration Severity Index (ASI) to gauge occupant risk. Despite significant changes in passive safety and vehicle design since the inception of these metrics, the current roadside hardware occupant risk procedures have remained essentially unchanged for nearly 40 years. There is little information correlating either FSM or ASI to occupant injury in the current vehicle fleet. FSM predictions might be unrepresentative of the injury risk experienced by occupants in modern vehicles. The newer ASI was designed for belted occupants but has not been validated for occupant injury risk in the current vehicle fleet. In addition, both FSM and ASI are acceleration-based measures, which are most suited to head and chest impacts. These metrics are less than ideal for predicting the risk of leg injuries, such as those observed in some end terminal collisions. Also, neither metric is suited for predicting injury in crashes where the occupant compartment is compromised, including broken side windows in rigid and semi-rigid barrier impacts and A-pillar cutting that can occur in crashes with a cable barrier. Because of advancements in passive safety and vehicle design, these methods to evaluate occupant injury risk in roadside hardware crashes should be reevaluated. The goal of NCHRP Project 17-90 is to evaluate existing roadside crash injury metrics and propose enhanced crash injury metrics that better reflect the occupant characteristics and vehicle fleet of the 2020s. The project considered the ability of the following metrics to predict real-world crash injury: FSM, ASI, maximum delta-v (MDV), occupant load criterion (OLC), and Vehicle Pulse Index (VPI). The objective of this research was to compare predictions from the current MASH occupant risk model and alternative models with the injury outcomes in real-world crash events and with crash tests where longitudinal and lateral decelerations were measured by instrumentation in vehicles impacting roadside safety hardware. The research program considered the following: 1. Three different crash impact types (frontal, side, and oblique impacts);
xxvii 2. Relevant crash and occupant characteristics, other than the value of the injury metric, that may affect injury risk; 3. The impact tolerance of all major body regions; and 4. The injury potential of intrusion into the occupant compartment. Research Approach The research team first synthesized the state of the practice and engineering rationale for existing and potential crash injury metrics and then identified potential data sources for assessment of roadside crash injury metrics. This information served as a basis for the development of the research plan, which is described briefly below. The overall approach was to first determine the candidate vehicle-based metrics for evaluating and the methods for quantifying occupant injury severity. A dataset of suitable real-world crash cases was then assembled to evaluate the candidate vehicle-based metrics and split into training and test subsets. The dataset included detailed occupant injury information, associated vehicle kinematics data, and other relevant crash and occupant characteristics. Data from the training subset was then used in conjunction with binary logistic regression to develop injury risk curves for each candidate injury metric for frontal, side, and oblique impacts. Injury risk curves were developed for overall injury and body regionâspecific injury when sufficient data were available. The developed injury risk curves were then used to predict injury for the test subset. The candidate metrics were ranked primarily based on how well they predicted the observed occupant injury from the test data subset using applicable statistical metrics. Two related investigations were also conducted: (1) to determine if considering vehicle-specific restraint performance significantly improved the real-world injury prediction capabilities of vehicle-based metrics, and (2) to compare how well the candidate injury risk metrics predicted ATD-based injury metrics used in full-scale vehicle crash tests. The results from all these analyses were synthesized and used to propose some new or modified injury risk procedures that may be considered for inclusion in a future version of MASH. A sample of previously conducted MASH crash tests was used to assess the possible implications of any proposed new or modified occupant injury risk criteria. In addition to the use of vehicle-based injury metrics, MASH specifies limits on occupant compartment intrusion. A separate analysis was conducted to evaluate the current MASH occupant compartment intrusion limits using available real-world crash data. The analysis consisted of three parts: (1) updating the previous FHWA intrusion study, (2) evaluating the current MASH location- specific vehicle occupant compartment intrusion limits using real-world crashes, and (3) estimating the frequency of certain vehicle damage patterns in real-world crashes with roadside hardware. Finally, the study findings were used to develop suggested modifications to current MASH language related to injury criteria as a means to implement any proposed new or modified injury criteria developed as part of the study. A proposed roadmap of research needs was then developed to aid with future efforts to update the MASH occupant injury risk evaluation procedures.
xxviii Recommendations This study investigated the ability of existing MASH occupant risk metrics, as well as several vehicle-based alternative methods, to predict real-world occupant crash injury in the current vehicle fleet. The analyses conducted build on previous studies using real-world frontal crashes and expand knowledge in other less explored areas (i.e., side and oblique crashes). The developed statistical models include other relevant crash and occupant factors that influence injury risk and can be employed by MASH users to compare devices more comprehensively with differing occupant risk values based on full-scale crash testing results. The research effort also provided a first evaluation of the MASH area-specific intrusion thresholds using real-world crash data. Based on these findings, following are the recommendations of this study: ⢠Consider retaining FSM for an updated MASH. Although the FSM assumptions do not necessarily align with the occupants of the current vehicle fleet, no other alternative metric consistently outperformed the model, especially the OIV. As a result, the FSM is recommended for inclusion for an update to MASH. To better align with the vehicle safety community and aid with end usersâ interpretation of computed occupant risk values from crash tests, inclusion of the developed injury risk curves is also recommended in an update to MASH. These injury risk curves allow users to incorporate other crash and occupant factors that influence injury risk potential. ⢠Consider retaining occupant compartment intrusion limits for an updated MASH. In general, the current region-specific MASH intrusion thresholds were found to have a strong correlation to occupant injury risk both in aggregate and individually. These findings serve as support for the current MASH intrusion threshold values, and additional MASH text has been proposed. ⢠Revisit vehicle-based metrics periodically using more recent real-world crash data. The vehicle fleet and associated technologies are continually evolving. Similar to the ongoing efforts to update the MASH test vehicles, real-world crash data should be examined periodically to (1) update the injury risk curves developed as part of this research effort, and (2) evaluate the need to update the vehicle-based injury metrics and/or associated threshold values used to evaluate occupant injury risk. ⢠Considerations for future studies examining vehicle-based occupant risk metrics. Findings of interest from this study include that the delta-v and VPI metrics would likely introduce additional subjectivity into the crash test procedures, as these metrics are sensitive to the selected analysis window and thus are not likely good candidates for future occupant risk procedures. Although not found to consistently outperform OIV in predicting real-world crash injury, OLC continues to be a metric of interest as it specifically models a belted occupant and was found to generally outperform the other candidate metrics in predicting ATD-based injury metrics. Additional study of ridedown acceleration (RA) is needed given the current event data recorder data limitations. While the RA has been omitted in analogous international crash test procedures in favor of the ASI metric, making this switch in MASH would result in relatively large changes to the currently accepted MASH hardware.
