Accident Analysis Methodology and Development of Injury Scenarios

Transcription

1 PROPOSED REDUCTION OF CAR CRASH INJURIES THROUGH IMPROVED SMART RESTRAINT DEVELOPMENT TECHNOLOGIES Accident Analysis Methodology and Development of Injury Scenarios Report Reference: R & R5 Author(s): Richard Frampton (VSRC) Richard Morris (MIRA) Gabrielle Cross (MIRA) Marianne Page (VSRC) Date: January 006 Number of pages: 74 Number of appendices: 7 Number of figures: 4 Number of tables: 4 December 005 PRISM

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3 Table of Contents Report Overview / Summary...5 Introduction...7. Overview of Available Crash Injury Databases Levels of Detail Available in Crash Databases Database Limitations Data Sample Sizes... 0 Selection of Databases for Development of Injury Scenarios...4. Choice of Analysis Technique Real World Sample Criteria for Scenario Development Sample Relationship to the Population of Crashes Accident Data Characteristics Drivers Front Seat Passengers Injury Impact Scenarios Injury Scenario Methodology Injury Scenario Results Injury Scenario Weighting Linking Injury Outcome Between Crash Scenarios and Simulation Use of the Injury Severity Score Chest Injury Risk Predictor Conclusions Crash injury databases CCIS data characteristics Injury impact scenarios Linking simulation injury assessment to real world injuries... 8 References Acknowledgements Appendices Appendix A: General Accident Information Appendix B: Vehicle, Occupant and Injury Specific Information Appendix C: Analysis of DGT Spanish Road Accidents Database (Report Reference R) Appendix D: Background to German In-depth Crash Injury Study (GIDAS) Appendix E: Case Details for Drivers with AIS + Head Injury Appendix F: Variables Related to Driver Femur Fractures Appendix F: Variables Related to AIS + Front Seat Passenger Chest Injury 7 December 005 PRISM

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5 Report Overview / Summary The main objectives of the PRISM project are to a) assess the injury reduction potential of SMART restraint technologies and b) provide guidelines on how to assess this technology with real or virtual testing protocol. By definition, SMART restraints are aimed at addressing the widest possible variety of situations, which arise in real crashes. The diversity of impact speeds, front-end overlaps, occupant sizes, ages and seating postures need to be defined in order that the restraint system knows how it needs to perform. Real-world accident data provides the necessary baseline for development of SMART systems and the means to assess their benefits. The contribution of accident data concerned; a) provision of information on crash conditions, injury outcome and injury mechanisms in production vehicles with current safety technology and b) quantification of potential injury reduction with the application of new technologies, using the current accident situation as a baseline. To provide a European perspective, it was necessary to draw on accident studies representing several European countries. Overall, accident data was to provide in-depth information on the crash conditions and vehicle/occupant variables related to crashes with serious injury outcome, that is, categorised as occupants receiving a Maximum Abbreviated Injury Score (MAIS) of at least (AAAM, 990). All this information was then to be categorised into a limited number of scenarios which could be replicated with computer simulation. SMART technologies would then be introduced into the simulations to gain knowledge of their injury reduction potential. Finally, these results would be fed back into the accident data in order to assess the potential benefits of fitting such SMART technologies. Several European databases were considered as donors of relevant crash injury data. On closer examination of those databases, the UK data was chosen because it fulfilled all the criteria needed for the PRISM analysis. It contained the level of detailed information required, it was considered representative of the general population of serious injury crashes and it had good availability of information. A sample of 75 drivers and 44 belted front passengers; all with serious/fatal injury was selected as the PRISM sample. All were in frontal crashes in cars equipped with airbags and pre-tensioned seatbelts. Of this sample, cases were selected which could be simulated using MADYMO and Human Body models. The final sample to undergo simulation consisted of 4 drivers and 5 front seat passengers. Due to the impracticality of simulating every case, it was necessary to group the crashes into a number of Scenarios. Detailed examination of each case allowed the grouping of each casualty into 0 injury scenarios. These were based on occupant kinematics and contact and load points associated with serious injury outcome to the head, neck, chest and thigh. A sophisticated and time-intensive case examination system was successfully developed, taking into account all aspects of the vehicle crash performance, occupant stature, and age, seating posture, kinematics and injuries. The scenarios were then developed as computer simulations into which SMART restraint systems could be incorporated. Each scenario was weighted according to its likelihood of occurrence in order to achieve accurate and realistic calculation of the magnitude of benefit likely to occur with the introduction of SMART restraint systems. The read-across from simulation output to real-world injuries necessitated careful consideration. The change in injury risk determined from the simulation was converted to a probable AIS value using an established technique. Chest injury outcome was considered separately for the average person and for weaker-boned occupants, by developing a set of December PRISM

6 Combined Thoracic Index (CTI) risk curves based directly on cadaver data. Overall risk of mortality was assessed using the ISS. As explained in the PRISM R8 report, the ISS values derived from the simulations were based on only three of the standard five ISS body regions (PRISM ISS) since the injuries to the other two could not be assessed using the tools available. In order to validate the use of PRISM ISS as the metric for mortality assessment, overall ISS mortality risk curves were developed from,88 car occupants involved in crashes between 995 and 004 in the UK crash injury database. A comparison of standard ISS values versus the PRISM ISS for these showed no statistical difference in ISS scores. Overall it is concluded that the process of setting up crash injury data for simulation is viable, providing certain precautionary steps are taken. The necessity of having available detailed, easily available real world data was highlighted. It should be noted however that the complexities of real accidents still pose a considerable challenge for simulation. Some of the perceived requirements for similar studies in the future are highlighted in other PRISM reports (R9 and R0). December PRISM

7 Introduction The overall objectives of the PRISM project are to a) assess the injury reduction potential of SMART restraint technologies and b) provide guidelines on how to assess this technology with real or virtual testing protocol. By definition, SMART restraints are aimed at addressing the widest possible variety of situations which arise in real crashes. The diversity of impact speeds, front end overlaps, occupant sizes, ages and seating postures need to be defined in order that the restraint system knows how it needs to perform. Real-world accident data provides the necessary baseline for development of SMART systems and the means to assess their benefits. The contribution of accident data concerned; a) provision of information on crash conditions, injury outcome and injury mechanisms in production vehicles with current safety technology and b) quantification of potential injury reduction with the application of new technologies, using the current accident situation as a baseline. To provide a European perspective, it was necessary to draw on accident studies representing several European countries. Overall, accident data was to provide in-depth information on the crash conditions and vehicle/occupant variables related to crashes with serious injury outcome that is, categorised as occupants receiving a Maximum Abbreviated Injury Score (MAIS) of at least (AAAM, 990). All this information was then to be categorised into a limited number of scenarios which could be replicated with computer simulation. SMART technologies would then be introduced into the simulations to gain knowledge of their injury reduction potential. Finally, these results would be fed back into the accident data in order to assess the potential benefits of fitting such SMART technologies. This report covers PRISM deliverables R and R5, Accident Analysis Methodology and Development of Accident Scenarios. The first part of the report deals with data preparation while the second describes the development of accident/injury scenarios for computer simulation. Reference material is presented in the form of appendices at the end of this report.. Overview of Available Crash Injury Databases The databases to be examined were defined in the PRISM project proposal. These were; the Fatals IDB (UK), the CCIS (UK), the DSD court database (Austria), the DGT accident database (Spain) and the GIDAS (Germany).. Levels of Detail Available in Crash Databases The initial step in data preparation was to identify what information was contained in each database and the level at which it represented the accident situation in each particular country. In this way, each database could be used according to its strengths. A template was drawn up to elicit the information. Tables and show the initial information requested for each database. Table describes the questions concerning general accident information. Table describes questions concerning data specific to the vehicles, occupants and injuries. It was initially agreed that information available from 997 onwards would be examined because of the need to commence analysis with relatively modern vehicles. The information contained in each of the databases is shown in appendices A and B. December PRISM

8 DATABASE Name of database. Country. TYPE OF DATABASE National, in-depth or court cases? Who collects the data? Number of accidents (997 onward). FUNDING Who provides funding for the data collection? Table : General Accident Information DATA COLLECTION PROTOCOL Region in which data is collected How are cases chosen for investigation? What is the sampling procedure? What is the relationship of cases examined to the national population of accidents? What years of data are available? ACCIDENT INFORMATION Date and time Day Road type Road speed limit Weather conditions Road surface conditions Cause of accident Number of vehicles involved Types of vehicles (car, motorcycle etc) Bodystyle (eg: hatchback, estate) Impact type for each vehicle Object struck for each vehicle Occupant injury severity Occupant seating position Airbag fitted/deployed? December PRISM

9 Table : Vehicle, Occupant and Injury Specific Information VEHICLE Make/model Age (registration year or year of manufacture) Crash Severity (Delta-V, EES ) Damage classification (collision deformation classification or other method) Number of impacts Severity of impacts (if more than ). If >, how is severity ranked according to occupant injury outcome or vehicle damage? Overlap calculated for frontal impacts? How is rollover classified? Intrusion how and where is this measured for frontal, side, rear and rollover crashes? Is the steering wheel movement measured and if so, in what directions? Object struck how are these classified? OCCUPANTS Seating position Seat track position Belt use Pretensioner (deployed/not deployed) Load limiter (deployed/not deployed) Airbag (deployed/not deployed) Type of airbag (steering wheel/facia/side head/side thorax) Age, Height, Weight, Gender Occupant loading from rear or side by other occupant/luggage INJURIES What is the scaling system used (AIS?) and if so, what version of the scale is used? How are the body regions defined? Can we get maximum injury severity for the cranium, face, neck, shoulder, arm, wrist/hand, chest, abdomen, pelvis, thigh, knee, leg, ankle, foot. In the case of limbs, can we get information for the left and right limbs separately? Are there descriptors and severity for each injury sustained by the occupant. That is, a detailed code identifying each individual injury? Is the cause of injury recorded and how many times is this known?. Database Limitations Each of the databases selected for study showed different characteristics. The DGT database provided a national picture of accidents in Spain but lacked detailed information on impact configuration, restraint fitment and performance and individual injuries (Appendix C). The IDB fatals database contained no cause of injury, no occupant height or weight and very few vehicle details. Additionally, most (90%) crashes in this database occurred between 990 and 995. Since the PRISM brief was to examine SMART restraint effectiveness from the starting point of current vehicles, due to the lack of occupant and vehicle information, it was decided to reject the IDB for further analysis. The DSD Austrian court data contained detailed accident reconstruction, occupant and vehicle information but no measure of how representative it was of the accident population in Austria. It was decided to refer to this database, only if necessary, to evaluate occupant kinematics and injury outcome at the Scenario generation phase of data analysis. CCIS and GIDAS databases were shown to contain in-depth vehicle, occupant and injury information together with methods to relate the crash samples to the population of accidents in the UK and Germany. As such, these databases held the most promise for developing the PRISM project. December PRISM

10 .4 Data Sample Sizes As with any analysis of accident data, it is necessary to know how representative the data is of the population from which it is drawn. It is also important to examine datasets large enough to stand up to robust statistical analysis. The PRISM working group on accident data devised a way to broadly categorise crash types into those that would need different types of SMART solution. Tables to 5 show vehicle crash types against seatbelt use by occupants for GIDAS (German), CCIS (UK) and DGT (Spanish) data. The DGT was only able to classify 5 of the0 impact types due to limitations of the database (see Appendix C). Tables to 5 also show occupant injury severity level, based on police classifications of injury. Not injured is self-explanatory. Slight injuries involves cuts and bruises whereas serious injuries usually involve bone fracture and / or organ injury. Fatality is classed as death occurring within 0 days of the crash. December PRISM

11 Table : GIDAS Data for Vehicle Model Years Data Collection Belted Driver OCCUPANT STATUS Unbelted Belted Unbelted Driver FSP FSP Belted RSP Unbelted RSP Occupants < yrs old IMPACT n n n n n n n Single Front head-on NI SER Single Front oblique NI SER Single Struck side perpendicular NI SER Single Struck side oblique NI SER Single Non struck side perpendicular NI SER Single Non struck side oblique NI SER Single Rear Roll only Roll + impact > impacts (no roll) NI SER NI SER NI SER NI SER December 005 PRISM

12 Table 4: CCIS Data for Vehicle Model Years Data Collection Belted Driver OCCUPANT STATUS Belted FSP Unbelted Driver Unbelted FSP Belted RSP Unbelted RSP Occupants < yrs old IMPACT n n n n n n n Single Front head-on NI SER Single Front oblique NI SER Single Struck side perpendicular NI SER Single Struck side oblique NI SER Single Non struck side perpendicular NI SER Single Non struck side oblique NI SER Single Rear Roll only Roll + impact > impacts (no roll) NI SER NI SER NI SER NI SER December 005 PRISM

13 Table 5: DGT National Data for Crashes Belted Driver OCCUPANT STATUS Belted FSP Unbelted Driver Unbelted FSP Belted RSP Unbelted RSP Occupants < yrs old IMPACT n n n n n n n Single Front head-on NI SER Single Struck side perpendicular NI SER Single Struck side oblique NI SER Single Rear Roll Only NI SER NI SER Upon initial examination, both CCIS and GIDAS appear to contain adequate numbers of data for analysis. The DGT data set is very large, as expected from a national data set. One of the benefits of national data is that it represents the whole population of crashes reported to the police. The DGT data was not however, able to classify all impact types. December 005 PRISM

14 Selection of Databases for Development of Injury Scenarios The development of SMART restraints from accident data requires detailed knowledge of the crash types, crash injuries and safety systems already fitted to each vehicle. Additionally, the baseline from which to develop SMART systems should consist of vehicles with current safety features added. CCIS and GIDAS data were examined for numbers of data concerning vehicles fitted with current safety features. At this point, DGT data was excluded because it could not provide detailed information on the vehicles safety features and crash injuries. Tables 6 and 7 show the numbers of cases available for analysis where full information on vehicle safety features was available in CCIS and GIDAS data. For a comprehensive description of The CCIS study methodology, the reader is referred to Mackay et al, 985. The methodology behind the GIDAS study is described in Appendix D. December PRISM

15 Table 6: CCIS Data for Vehicle Model Years Occupants with Airbag GROUP Drivers in frontal impacts with airbag + pretensioner MAIS Belted driver Unbelted driver Occupant Type Driver belt use NK Belted FSP Unbelted FSP FSP belt use NK Total MAIS MAIS + alive Fatal Total MAIS FSP in frontal MAIS impacts with alive pretensioner only Fatal 4 5 Total MAIS Drivers in front MAIS + alive 8 4 oblique impacts with airbag + pretensioner FSP in front oblique impacts with pretensioner only Struck-side occupants in perpendicular impacts with side airbag Fatal 4 Total 4 54 MAIS 7 8 MAIS + alive 4 4 Fatal Total MAIS 4 MAIS + alive Fatal 4 Total 6 0 MAIS Struck-side MAIS occupants in 4 + alive oblique impacts with side airbag Fatal Total 6 0 Non-struck side MAIS 5 occupants in MAIS perpendicular + alive 6 0 impacts without Fatal 6 8 side airbag Total 4 4 MAIS Non-struck side occupants in MAIS oblique side + alive 4 8 impacts without airbag Fatal Total December PRISM

16 Table 7: GIDAS Data for Vehicle Model Years Occupants with Airbag Belted Driver Unbelted Driver Occupant Type Belted FSP Unbelted FSP Belted RSP Unbelted RSP Occupants < yrs old Group n n n n n n n Single Front head-on 4 NI SER Single Front oblique NI SER Single Struck side perpendicular NI SER Single Struck side oblique NI SER 44 8 Single Non struck side perpendicular NI SER 0 6 Single Non struck side oblique NI SER Single Rear Roll only Roll + impact > impacts (no roll) NI SER NI SER NI SER NI SER December PRISM

17 GIDAS data show very small numbers available once airbag and pretensioner fitment criteria were considered. For example, the number of seriously injured, belted drivers in single frontal crashes (head-on and oblique) fell from 55 to 0. In CCIS, only 44 equivalent cases were available for analysis. In effect, there were insufficient cases available in either GIDAS or CCIS to enable development of crash scenarios using traditional grouping methods based on vehicle crash descriptors. Additionally, numbers of side impacts with vehicles containing the latest structures and safety features were low in both CCIS and GIDAS. The PRISM sub-group on accident data therefore revised its methodology. Since there were insufficient side crashes it was decided to base the PRISM work on frontal crashes only. In order to increase the numbers of frontal crashes without including too many older vehicle designs it was agreed to extend the accident data analysis back to vehicles registered from 995 onwards. It was also decided to include the vehicles classed as multiple impact given that the majority of these will only have sustained one major impact (Lenard et al, 000) and to include them if that major impact was classed as frontal. Frontal crashes were to be chosen where belted drivers or front seat passengers had sustained any serious injury (MAIS +) since these are the injuries, which need to be addressed as the first order of priority.. Choice of Analysis Technique Since it was impractical to simulate every individual frontal crash resulting in serious injury outcome it was necessary to put crashes into groups or scenarios with a common theme related to injury outcome. A new methodology was devised for developing the crash injury data to be simulated. This was based on the premise that data numbers were too low to carry out a traditional analysis, grouping cases together by crash severity variables. Instead an injury based analysis was devised which included the benefit of classifying injuries by their causation. This method involved working out injury mechanisms by body regions and looking for causation trends. It was necessary to interrogate each case separately and the method was extremely time intensive necessitating examination of all the case details including photographs. In that respect, because CCIS was more accessible than GIDAS, it was decided to further develop the injury scenarios based on the UK data. Preparation of a new UK database to add data from was undertaken. This, together with the inclusion of vehicle registration years resulted in an almost doubling of case numbers over the original sample. For example, the number of drivers with AIS + head injury rose from to 45 and those with AIS + chest injury rose from 5 to 86.. Real World Sample Criteria for Scenario Development CCIS in-depth crash injury data Passenger cars registered 995 onwards Crashes which occurred between 995 and 004 Most severe impact to vehicle front Equipped with airbag and pre-tensioner in the appropriate seat Belted driver or front passenger with AIS + injury. These cases, plus additional new variables and case photographs were put together in preparation for the generation of simulation scenarios. The variables presented were: case number, vehicle number, equivalent test speed, delta-v, airbag deployment, object hit, impact angle, percentage overlap, facia intrusion, steering wheel intrusion, make/model, registration year, occupant age, height and gender, body region injury, injury severity and December PRISM

18 initial stated injury cause. Vehicles used were those manufactured from 995 onwards. An example of these starting point variables is shown in Appendix E for drivers with AIS + head injury.. Sample Relationship to the Population of Crashes In-depth crash injury data from the UK Co-operative Crash Injury Study (CCIS) was used to develop the injury scenarios and to provide information for crash simulations. The CCIS study selects cases for investigation using a stratified random sampling procedure based on injury severity (Mackay et al, 985). CCIS accident sampling gives a bias toward serious injury crashes. It examines about 80% of serious injury crashes and all fatalities, which occur in towed-away cars, less than 7 years old, in accidents occurring in the CCIS sample regions. The sample regions contain a mixture of urban and rural roads. One of the assumptions of the PRISM analysis is therefore that the crash analyses represent the majority of situations where belted front seat occupants currently receive serious injury in frontal crashes. December PRISM

19 4 Accident Data Characteristics This section describes the CCIS crash injury data used to develop the injury scenarios. The study assesses injury outcome using the Abbreviated Injury Scale (AAAM, 990). The Equivalent Test Speed (ETS) was used as a measure of crash severity. ETS is the vehicle delta-v, calculated on the assumption that deformation was caused by impact with a rigid barrier. The calculation assumes the force was directed through the centre of the crush area. It does not assume the vehicle was brought to rest. ETS is different to the Equivalent Energy Speed (EES) used in other in-depth studies because the EES calculation assumes the force to be through the vehicle centre of mass and that the vehicle was brought to rest. ETS is therefore always less than or equal to EES. There are a number of factors which affect the accuracy of ETS so it is best used to place crashes into groups of similar severity rather than to compare individual crashes. Passenger compartment intrusion refers to the residual or static deformation. Dynamic deformation during the crash is usually higher than the measured static value available. 4. Drivers There were 77 drivers who had sustained MAIS + injury and/or died. All these drivers were in cars equipped with a driver s airbag and seat belt pre-tensioners. The breakdown of MAIS by survival status is shown in Table 8. Table 8: MAIS by Survival Status (drivers) MAIS Fatals (N) Survivors (N) Total Total The majority of fatally injured drivers sustained MAIS 4 or 5 (4/57). The majority of survivors sustained MAIS (99/0). One driver died with MAIS and one with MAIS. The first sustained only minor abrasions and lacerations and had drowned. The second sustained a small liver laceration, petechial haemorrhaging in both kidneys, fractures of the radius and ulna to both left and right limbs and a de-gloving of the right thigh. The cause of death was Adult Respiratory Distress Syndrome. Excluding the two fatalities with MAIS < left 75 drivers with MAIS + injury. The breakdown of body regions injured to AIS + for those drivers is shown in Table 9. AIS + head, chest, thigh injury combinations are shown for drivers with AIS + injuries to those body regions. Body regions injured to AIS + are shown for drivers without AIS + head, chest or thigh injuries. December PRISM

20 Table 9: Body Regions Injured to AIS + (drivers) Drivers with AIS + to head All Drivers (n) or chest or thigh Head + Chest + Thigh 0 Head + Chest 0 Head + Thigh 8 Chest + Thigh 0 Head only 7 Chest only 46 Thigh only 4 AIS + injuries to Drivers without AIS + to head or chest or thigh Leg only Arms/Hand only Knee only Abdomen only Neck only Arms/Hand + Pelvis Pelvis only Ankle/Foot only Fatal Drivers (n) Total Of the 75 drivers with MAIS + those with AIS + head, chest or thigh injuries accounted for the majority, 4/75 (8%). This group also included all the fatalities. The remaining drivers sustained MAIS only and those injuries were mainly to the extremities only, 4/. The priority group in this sample contained the 4 drivers with AIS + injuries to the head, chest or thigh due to the risk of fatality. In this group, AIS + injuries to only the chest accounted for 46/4 drivers (0%). AIS + injuries to only the thigh accounted for 4/4 drivers (9%). AIS + injuries to only the head accounted for just 7/4 drivers (5%). AIS + head injuries were implicated in 45/4 (%) of the 4 drivers whereas AIS + chest injuries were sustained by 86/4 (6%) of those drivers. AIS + thigh injuries occurred in 69/4 (49%) of the drivers in the priority group. Of the 55 fatally injured drivers, 6% sustained serious head injury, 87% sustained serious chest injury. 4. Front Seat Passengers There were 44 front passengers who had sustained MAIS + injury and/or died. All of these passengers were in cars equipped with seat belt pre-tensioners but only 7/44 (6%) were equipped with passenger airbags. The breakdown of MAIS by survival status is shown in Table 0. Table 0: MAIS by Survival Status (front passengers) MAIS Fatals (N) Survivors (N) Total Total 44 December PRISM

21 The majority of fatally injured front passengers sustained MAIS 4 or 5 (8/). The majority of survivors sustained MAIS (6/44). No front passengers died with MAIS <. Of the 44 front passengers with MAIS + injury, the breakdown of body regions injured to AIS + is shown in Table. AIS + head, chest, thigh injury combinations are shown for passengers with AIS + injuries to those body regions. Body regions injured to AIS + are shown for passengers without AIS + head, chest or thigh injuries. Table : Body Regions Injured to AIS + Passengers with AIS + to All Passengers Fatal head or chest or thigh (n) Passengers (n) Head + Chest + Thigh Head + Chest Head + Thigh Chest + Thigh Head only Chest only Thigh only AIS + injuries to Passengers without AIS + to head or chest or thigh Neck only Arms/Hand only Arms/Hand + Leg Leg only Abdomen only 6 Total 44 Of the 44 front passengers with MAIS + those with AIS + head, chest or thigh injuries accounted for the majority, 4/44 (77%). This group also included all the fatalities. The remaining passengers sustained MAIS only and those injuries were mainly to the extremities only, 8/0. The priority group in this sample contained the 4 front passengers with AIS + injuries to the head, chest or thigh due to the risk of fatality; a situation very similar to that with drivers. For the front passengers, AIS + injuries to only the chest accounted for 0/4 (59%). AIS + injuries to only the thigh accounted for /4 (9%). AIS + injuries to only the head accounted for 6/4 passengers (8%). AIS + head injuries were implicated in 0/4 (9%) of the 4 passengers whereas AIS + chest injuries were sustained by 5/4 (74%) of those passengers. AIS + thigh injuries occurred in 5/4 (5%) of the passengers in the priority group. Of the 0 fatally injured passengers, 40% sustained serious head injury, 00% sustained serious chest injury. December 005 PRISM

22 5 Injury Impact Scenarios The process for generating injury impact scenarios is described below. 5. Injury Scenario Methodology The set of CCIS crashes was analysed to determine the likely occupant kinematics, contact and load points associated with serious injury outcome to the head, chest and thigh (femur). It was necessary to sort by injury mechanism trends and consider injury severities, occupant populations, and clusters of crash types that occurred within these trends. Each kinematic trend leading to an injury trend (with other key aspects identified) was called an injury impact scenario A team of 4 complimentary experts reviewed each case together, using increasing levels of detailed information. Hypotheses were put forward about likely kinematics and injury causes which were then tested by reviewing more detailed data and evidence. The hypotheses were refined and retested, usually with several iterations before a conclusion was drawn and that conclusion clustered with similar cases. As a result of the process development the final sequence for each case was: ) Read out vehicle type, collision partner, estimated impact speed, overlap, driver age, height, gender and weight. ) Each person made a hypothesis about likely kinematics and injuries of the driver (and front seat passenger if applicable). ) The primary AIS for head and chest were revealed and the hypotheses modified or not. 4) Then the detailed injury list was read out and the hypotheses were further refined or modified to take into account known anatomical contact points including grazes, bruising etc., as well as major injuries. 5) Using the hypotheses, suspected contact sites on the interior of the vehicle were then assessed for evidence of loading and deformation. Other evidence, such as abrasion marks on the seat belt webbing, was also assessed with respect to location, to determine likely occupant kinematics. 6) If agreement was reached on the kinematics and biomechanical loading hypothesis, this was accepted and noted in summary. 7) Any other interesting or unusual aspects were also noted in case these were seen again in other cases. 8) Cases that were unclear were classed as possible in the several respective injury impact scenarios. A certain degree of re-assessment was required when particular kinematics were observed that had not been previously considered. Similar past cases were reassessed to determine if the kinematics hypothesis was still valid or if the new one was more suitable. Some practical measures were developed to assist the various participants. It was necessary to consider a wide range of data on each case, simultaneously. This was achieved using multiple PCs. The best arrangement used PCs and projectors projecting onto walls at the same time (general vehicle, crash and occupant data on one, detailed injury descriptions on the second and vehicle photos on the third.) Additional supporting material was also useful a height chart (with comparison to 5thF, 50thM and 95thM dummies), a model skeleton to identify anatomical features and positions December 005 PRISM

23 when seated in a car seat. A vehicle seat simulator with a length of seat belt webbing (a short and a long end) was used to allow approximate determination of occupant position when the abrasion marks were made on the belt. Also various reference books, especially for medical terms, were found useful. Of front seat occupants with MAIS + injury, 69 drivers and 5 front seat passengers sustained femur fracture. Because of the large number of drivers with femur fracture an overview analysis was carried out to establish the reasons for occurrence (Appendix F). The results show few cases with crash severity exceeding 56 km/h, no major facia intrusion and no pattern of driver age or proximity issues. The major reasons for femur fracture therefore remained unclear and further work was recommended to establish factors related to injury causation. Of front seat passengers with MAIS + injury, 5 sustained AIS + chest injury. An overview analysis was carried out to try and establish the reasons for occurrence (Appendix G). The results showed that most of the casualties were women approaching, during, or after the menopause. Crash severities were very low with static facia intrusion below 5cm in most cases. The overall conclusion to this was that loading of the seat belt webbing on the chest generated most of the AIS + injuries. 5. Injury Scenario Results The derived injury scenarios are shown in diagrammatic form in Figure. Scenarios to 9 refer to the driver, whilst scenario 0 refers to the front seat passenger. Small Driver Small drivers who naturally sit close to the steering wheel are at risk of serious chest head and neck injuries from a range of sources airbag cover contact, airbag punch-out, underchin loading and lack of distance for ride-down before steering wheel contact. Large Driver Large drivers often adopt a very reclined position to prevent roof contact. This leads to poor positioning of the diagonal belt, allowing extensive forward motion and severe submarining under the lap belt. December 005 PRISM

24 Very Late Deployment Similar in some respects to the small driver case, but this is a dynamic version that affects all statures. Poor crash pulse discrimination in soft impacts (pole, angled offset, shallow overlap and under-ride) allows excessive driver forward motion before airbag deployment, leading to a similar injury set to that suffered by the small driver. 4 Driver Misses Airbag Angled offset and shallow-overlap crashes cause vehicle rotation and displacement of the dashboard / steering wheel inboard (by up to 500mm). The driver then misses the airbag and has heavy head contact with either the lower A-pillar, the outboard dashboard, the driver face vent or the top of the door casing. 5 Airbag Bottoms Out Driver strikes the steering wheel indirectly through the airbag. These are of types: ) Head through the top edge of the airbag,) Chest simply overloads the airbag and penetrates through, deforming the rim and loading the hub. 6 Steering Wheel Edge Strike Driver impacts the steering wheel directly with minimal protection from the airbag. These are of types: ) Head over the top of the airbag, ) Steering wheel upward rotation followed by chest impact. Radial loading with the wheel at the on-spoke position is particularly aggressive. December PRISM

25 7 Header Rail Strike Some instances of moderate under-runs with some header rail intrusion, but not a total loss of head space. Severe head injuries against deformed header rail or truck rear through the windscreen. 8 Chest Injury General Large numbers of chest injuries in crashes for no readily apparent reasons. Few cases with crash severity exceeding 56 km/h, no major steering wheel intrusion, no pattern of driver age or proximity issues. Needs more investigation to understand real loading levels, sequence, timing and effects of rib, sternum and clavicle fractures. 9 Femur Fracture General Large numbers of femur fractures in crashes for no readily apparent reasons. Few cases with crash severity exceeding 56 km/h, no major facia intrusion, no pattern of driver age or proximity issues. Off axis loading? Needs more investigation to understand direction and magnitude of real loading. 0 High Chest Injury Risk to Front Seat Passengers Lower quantities of passenger data made classification of different categories more difficult. It was clear however that most serious injuries were to the chest. Mainly sustained by older women in low severity crashes. 5. Injury Scenario Weighting The incidence of scenarios in the case population is not easy to quantify exactly as all cases are different. There are some cases that are clearly and definitely representative of a scenario but there are other cases in which the injury mechanism described by the scenario may have had some influence on the case outcome, to a greater or lesser extent. In order to assess the incidence of scenarios to allow potential benefits of smart restraints to be determined, a twin approach was adopted: The definite cases and the possible cases were identified by the methods described in section 6.. December PRISM

26 Each of the possible cases was then subjectively assessed against the scenario definition and was given a rating between 0 and that attempted to quantify the degree of relevance of the scenario to that case. Since the assessment was very subjective, the possible increments were kept relatively coarse: 0.0 Not at all relevant 0. Slightly or possibly relevant, but marginal 0.4 Likely to have been relevant to a lesser degree 0.6 May have been relevant to a significant degree, or a likely low severity incidence of the scenario 0.8 Was clearly relevant, but not certain to have been the only mechanism of injury in the appropriate body region, or was not of sufficient severity to justify a score of.0 Definite case The subjective nature of these assessments implies that closer investigation of any one case may suggest some revision, but overall, it is assumed that the general levels are likely to be a reasonable indication of the incidence of the scenarios in the real world accident population. Each of the scores was then used as a weighting factor for the relevance of its aspects to the scenario and to produce a weighted population for each injury mechanism scenario. The femur and chest general scenarios are not included since it has already been established that the injury mechanisms themselves are unclear but there are some trends worthy of further investigation. The results of the weighting procedure are shown in table below. Table Incidence of Injury Scenarios Scenario Number Description Definite Cases Partial (weighted) cases Total Weighted Total Small Driver Very large driver Very late deployment Driver misses airbag 4. 5 Bag bottoming out a Steering wheel edge strike (chest) 6 7 6b Steering wheel edge strike (head).6 7 Header rail strike Chest - General Femur Fractures Passenger (chest) 5??? Examining the frequency of scenarios shows that the small driver, late deployment, bag bottoming out, steering wheel edge strike, chest general, femur fractures and passenger chest injuries are the most frequent situations. December PRISM

27 6 Linking Injury Outcome Between Crash Scenarios and Simulation It was necessary to create a baseline simulation representing each crash scenario into which SMART restraints could be introduced and assessed. The methodology for simulation development is described in detail in report R6/R7. To be representative of real crashes, it was necessary to achieve a similar injury outcome between the baseline simulations and the real crashes. Real world injury outcome was assessed by the Abbreviated Injury Scale (AAAM, 990) whereas simulations produce measured physical parameters on the dummy. In order to read across between dummy output and real injury, it was necessary to use a common measuring system. To this end, PRISM employed a method of converting dummy outputs into a probable AIS value using injury risk curves (Kuchar et al, 00). The whole process is reported in detail in section 7. of the R6 & R7 report. This method could then be used to detect changes in injury severity after the addition of SMART restraint systems into the scenario simulations. New injury outputs could then be compared to those from current vehicles to gain insights into potential benefits. The process of evaluating benefits is reported in PRISM report R8. 6. Use of the Injury Severity Score Since changing the response of a restraint system in one area (e.g. chest deflection) is likely to have an effect on another (e.g. head loading), the ISS or Injury Severity Score (Baker et al, 974) was chosen as the general measure of system performance because it accounts for changes in injury outcome to several body regions together. Use of the ISS had the additional value of allowing a benefit assessment based on risk of fatality. The ISS is a good measure of fatality risk because it considers injuries to multiple body regions by summing the squared maximum AIS to the three most severely injured body regions out of five (Fig ). Figure ISS Body Regions The calculation of ISS from the scenarios was limited to using injury at the head, neck, chest and thigh because risk curves were only available for those body regions in the simulations. Injury reference values for the ISS body regions of face, abdomen or pelvic contents, extremities (other than thigh) and external were not available. It was hypothesised that this PRISM ISS (ISS P ) would nevertheless still give a sufficiently close value to the real ISS value whose calculation involves additional body regions. December PRISM

28 The ISS P for the injury impact scenarios was calculated as follows: ISS P =max(ais HEAD, AIS NECK ) + max(ais CHEST ) +max(ais THIGH ) The simulated ISS (ISS M ) was calculated by relating the dummy output to probable AIS values from injury risk curves (Section 7. of the R6 & R7 report) for head, neck, chest and thigh injury, then using the following formula. ISS M =max(ais HIC, AIS NIJ ) + max(ais CTI ) +max(ais FFC ) Where HIC=head injury criteria, NIJ=neck injury criteria, CTI=combined thoracic index and FFC=femur force criteria. In order to ascertain how well the ISS P represented the real ISS, comparisons were made of the two, as seen in table below, for the 4 drivers in the scenarios where AIS + head, chest or thigh injuries occurred. Table Comparison of Real ISS to PRISM ISS ISS Real PRISM % % 7% 8% 8% 7% 5% 9% 6% 4% % 0% Total (N) 4 (00%) 4 (00%) Chi-square = 5.085, df=5, p=0.406 The ISS P calculation put more drivers into the low ISS (0-0) band and less into the -40 band. In all other bands the distribution of ISS was similar between the two calculation methods. A chi-squared test showed no statistical difference in ISS distributions at the p<0.05 level using the two methods. Therefore the ISS P body regions gave a reasonable assessment of real injury severity for these cases. In order to relate ISS values to a risk of mortality in the benefits analysis, it was necessary to build predictive models of survival rate by ISS. One factor to consider was the question of increasing fragility of occupants with age (Morris et al, 00), especially in women during or after the menopause. Additionally, Baker et al (974) showed that, for the same ISS value, the mortality risk for older casualties was higher than that for younger people. The literature describing at what age tolerance to blunt impact starts to deteriorate does not give an exact cut-off point but relates degradation of bone strength to injury tolerance levels. The research summarised in Figure, shows how skeletal mass deteriorates for males and females. Based on this, it was decided to build two predictive ISS mortality models: one to represent the population of casualties with normal bone strength and one for those where bone strength was likely to be compromised. December PRISM

29 Figure Skeletal Mass Versus Age for Males and Females Using,88 occupants in the CCIS data set, linear regression was used to build predictive models of survival rate by ISS for firstly all of the males, plus females under 55 (normal bone strength, N=,4) and secondly for all females 55 and older (compromised bone strength, N=990). Of course, these are coarse groupings but do allow at least a consideration of the difference in injury tolerance in the casualty population. Linear regression is a statistical technique which gives the predicted response of a dependent variable based on the responses to an independent (predictor) variable. In this case the dependent variable was the survival rate and the predictor variable was the real ISS. The underlying assumptions associated with the use of linear regression were validated for the data used. It should be noted that the predicted rate is only valid for the range of the predictor variable. For the model, R is a measure of the correlation between the observed value and the predicted value of the dependent variable. R is the square of the R value and indicates the proportion of the variance in the dependent variable which is accounted for by the model. Essentially, R is a measure of how good a prediction of the dependent variable can be made by knowing the value of the independent variable. If for example the regression produces an R of 0.75 then the model has accounted for 75% of the variance in the dependent variable. Ideally an R of is sought, indicating an exact prediction. The p value provides a test of whether there is a significant relationship between the independent and dependent variables. Regression is however most generally assessed on the basis of the R square value. December PRISM

30 Predicted Survival Rate all males/females <55 females >= ISS Figure Predicted Survival Rate for ) all Males, plus Females <55 and ) all Females >=55 Figure shows that generally, survival rate declines as ISS rises and that for the same ISS values, the survival rate is much lower for women over 54 years of age. The R value for all males plus females <55 is and for females >=55 it is For both groups the p value is <0.00. Therefore, the validity of the predictive models is high and highly significant. 6. Chest Injury Risk Predictor Of all the human body regions, the chest is the one most sensitive to occupant bone condition. Numerous studies have shown that the chest tolerance to blunt impact is highly dependent on overall bone strength, which is why older females are particularly at risk from serious chest injury (Morris et al, 00, Frampton and Mackay, 99). For the PRISM simulations, it was necessary to choose a chest injury risk predictor which accurately measured real world chest injury risk and could also predict that risk for high and low bone strength conditions. Particularly because, in the scenarios many of the serious chest injuries were sustained by older women. The Combined Thoracic Index (CTI) reported by (Kleinberger et al, 998) was chosen as the measure of chest injury severity because it was shown to be a better predictor of real-world chest injuries than chest deflection or acceleration alone (Hardy et al, 005). Two versions of the CTI were used for the PRISM simulations dependent on whether we wanted to predict chest injury for normal bone strength or for the older, more at-risk casualties in the accident population. For the normal condition the CTI risk curves developed for the 50 th percentile HYBRID III male dummy were used. For the older, more vulnerable casualties, the CTI risk curves representing the original NHTSA cadaveric test data were used (Eppinger et al, 999). Those tests used cadavers of mean age 60. There was nearly a 0 year difference in average age between the cadavers and the U.S. driver as represented by Hybrid III. The MADYMO and Human Body Models used for the PRISM simulations already contained the required CTI curves for the Hybrid III but it was necessary to derive the CTI cadaveric curve equations using information from NHTSA about how the injury risk for cadavers related to the risk predicted by the Hybrid III. In the development of CTI, the 50% probability of AIS + injury for the cadavers was adjusted to represent a 5% probability of injury for the driving December PRISM