Systems Modeling of Frontal Crash Compatibility

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Ford Taurus. 1711. 1400. Midsize Car. Chevrolet C1500. 2072. 1820. Large Pickup. Ford Explorer. 2206. 1950. SUV. Ford Expedition. 2673. 2400. Super SUV.
2000-01-0878

Systems Modeling of Frontal Crash Compatibility Hampton C. Gabler Rowan University

William T. Hollowell and Stephen Summers National Highway Traffic Safety Administration

ABSTRACT This paper presents a Systems Model for evaluating frontal crash compatibility based upon computer simulation of frontal-frontal crashes. An analysis was conducted using the Systems Model to evaluate the frontal crash compatibility of a midsize production car. Results of this analysis show that the Systems Model is capable of capturing the aggressivity trends observed in accident data. The paper demonstrates that the Systems Model approach is a promising tool for forecasting the aggressivity of vehicle makes and models which are newly introduced into the market. INTRODUCTION The crash compatibility of a vehicle is a combination of its crashworthiness and its aggressivity when involved in crashes with other members of the vehicle fleet. While crashworthiness focuses on the capability of a vehicle to protect its occupants in a collision, aggressivity is measured in terms of the casualties to occupants of the other vehicle in the collision. Crashworthiness is sometimes referred to as self-protection while reduction in aggresivity is sometimes referred to as partner-protection. In the United States, one of the largest sources of crash incompatibility has been observed in collisions between light trucks and vans (LTVs) and cars. Of the 5,373 fatalities in LTV-car crashes in 1997, 81 percent of the fatally injured were occupants of the car [1]. As a group, LTVs are heavier, of more rugged construction, and have higher ground clearance than the passenger cars with which they share the road. The concern is that these design features, introduced to allow specialized functions, e.g., off-road driving, may make LTVs fundamentally incompatible with cars in highway crashes, and in some cases dangerous to the occupants of the cars struck by LTVs. The crash compatibility of a particular vehicle is typically evaluated either by crash testing [2] or analysis of accident data [1,3,4,5]. Although single crash tests provide an invaluable indicator of crash safety in a

particular crash mode, the results of one test cannot be readily extrapolated to forecast the fleet wide safety performance of a car. Accident data, e.g. FARS or NASS, provide an indisputable record of the safety performance of a vehicle in the field. Accident data, however, is an historical record and, by its nature, is not available for new vehicle models until several years after the car is first introduced into the fleet. What is needed is a methodology that will allow predictive estimates of crash safety prior to the introduction of the vehicle into the fleet. OBJECTIVE The objective of this paper is to describe a Systems Model for forecasting the frontal crash compatibility of passenger vehicles exposed to the traffic accident environments of the future. APPROACH When on the highway, cars are subjected to not one, but a myriad of different types of accidents. Systems Modeling attempts to capture this fleet wide crash safety performance by evaluating car design across the full range of potential impact speeds, angles, collision partners, occupant seating locations, and occupant restraints. The outcome of each of these collision modes is computed in units of fatalities, injuries, or social cost, weighted by its probability of occurrence, and summed. The sum includes casualties in both the design vehicle and its collision partners. The result is a system-wide measure of safety performance of the car design in terms such as the annual number of fatalities which will likely be incurred by future occupants of this car and its collision partners. In the general case, the Systems Model is described by the following relationship:

SocialCost= n∑ pi c(si ) i

where n is the number of crashes per year to which the design vehicle is subjected, pi is probability of crash event

i given that a crash has occurred, s i is the severity of crash event i in terms of mechanical loads on the occupant, and the c(s i) is the cost or outcome of the a crash of type i. The severity of a crash is measured in terms of mechanical loads on the occupant using standard injury criteria such as HIC and peak chest deceleration. The cost or outcome of a crash is a function of the severity of the crash. Crash outcome is measured in units such as fatalities, injuries, or social cost measures such as harm. By summing the weighted outcome of each possible crash event i, pic(s i), the result is a system wide measure of the social cost incurred by occupants of the design vehicle and its collision partners. FRONTAL-FRONTAL COLLISIONS: A SPECIAL CASE The goal of this study was to investigate crash compatibility in frontal-frontal collisions. The strategy was to evaluate the crash response of a reference car when subjected to a frontal-frontal collision with each of the potential collision partners of the future fleet. The injuries in both the design car and its collision partners were evaluated. The design car crashworthiness and aggressivity were evaluated in collisions with each of the potential collision partners of the future fleet, across the distribution of expected impact speeds. This study uses the ‘Severe Injury Ratio’ as the crash compatibility metric. Earlier studies [1,3,4,5] have reported the crash compatibility of passenger vehicles in terms of a fatality ratio. The fatality ratio was defined to be the number of driver deaths in the collision partner divided by the number of driver deaths in the subject vehicle. To generalize these results to include non-fatal as well as fatal injuries, the Severe Injury Ratio was defined to be the number of AIS 4+ injured drivers in the Design Vehicle divided by the number of AIS 4+ injured drivers in the collision partner as shown below:

SevereInjuryRatio =

InjDesignVehicle InjCollisionPartner

where

Inj DesignVehicle = n∑ pi p( AIS ≥ 4 | si ,DesignVehicle )

i. By summing the weighted outcome of each possible crash event i, pi p(AIS>=4 | s i), the result is the total number of severely injured drivers in each vehicle. A Severe Injury Ratio is computed for each of the collision partners by dividing the injury count in the reference car by the injury count in each of the collision partners. Higher injury ratios correspond to vehicles that are more aggressive. Lower injury ratios correspond to vehicles that are less aggressive. REFERENCE VEHICLE AND COLLISION PARTNERS The reference car chosen for this study was the Ford Taurus, a midsize passenger car. This Taurus was selected as the reference car for several reasons. First, the Taurus has a large sales volume which makes this vehicle a suitable representative of the midsize passenger car component of the fleet. Second, large numbers of Taurus crash tests were available in the NHTSA crash test database for validation. Finally, a Ford Taurus finite element model, developed by NHTSA, was available for further refinement of this study. The frontal crash compatibility of the Reference Car will be investigated in collisions with each of four collision partners presented in Table 1 below. The Ford Taurus is included in Table 1 for purposes of comparison. Table 1. Reference Vehicle and Collision Partners Vehicle

Dodge Neon

NCAP Curb Test Weight Weight (kg) (kg) 1354 1120

Vehicle Category Subcompact Car

Ford Taurus

1711

1400

Midsize Car

Chevrolet C1500

2072

1820

Large Pickup

Ford Explorer

2206

1950

SUV

Ford Expedition

2673

2400

Super SUV

i

InjCollisionP artner = n∑ pi p ( AIS ≥ 4 | s i ,CollisionP artner ) i

As before, n is the number of frontal-frontal crashes per year to which the design vehicle is subjected. For this special case of Systems Modeling, six crash events i are considered – one for each impact speed. pi is the probability of crash event i given that a crash has occurred. s i is the severity of crash event i in terms of mechanical loads on the occupant as measured by HIC and chest deceleration. p(AIS>=4 | s i) is the probability of injuries of severity AIS = 4 or greater from a crash of type

The collision partners were chosen to represent distinct segments of the passenger car fleet. The Dodge Neon was chosen to represent the subcompact car fleet. The Chevrolet C1500 was chosen to represent the pickup truck segment. The Ford Explorer was chosen to represent the Sport Utility Vehicle (SUV) fleet. The Ford Expedition was selected to represent the emerging super SUV fleet segment.

VEHICLE IMPACT RESPONSE

5

Occupant Compartment Accel. (G)

To evaluate structural impact response, lumped mass models of each vehicle were developed using the SISAME code [6]. A frontal-barrier crash model was developed for each vehicle. As shown in Figure 1, each vehicle was modeled as a system of three masses connected by nonlinear, rate sensitive, energy absorbing springs. Using the recently released SISAME multi-event extraction methodology, the non-linear springs were simultaneously extracted from frontal-barrier crash tests run at 48 km/hr and 57 km/hr.

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Figure 3. Validation of Taurus Frontal-Barrier Lumped Mass Model: Crash Test vs. Simulation at

Wheel / Suspension Occupant Compartment

0.02

EA-5

EA -6

Barrier

Engine EA-2

EA-3

Figure 1. Vehicle-Barrier Model Validation of each resulting vehicle model was conducted by simulating frontal-barrier crashes at two different impact speeds and comparing results with physical crash tests. Figures 2 and 3 show the close agreement between the simulation and actual crash test results for the Reference Vehicle occupant compartment deceleration time history. It is especially important to accurately model the impact response of the occupant compartment as this deceleration time history is used to drive the occupant impact response model described below.

56.3 km/hr A frontal-frontal crash response model was developed for each reference car/collision pairing by combining the Reference Vehicle barrier model with the frontal-barrier model of each collision partner. The two models were interconnected by an interface mass of negligible mass (0.1 kg) as shown in Figure 4. The resulting four frontalfrontal vehicle models were executed with each vehicle moving at impact speeds of 20, 30, 40, 50, 60, 70, and 80 km/hr. The resulting occupant compartment crash pulses were used as input to the occupant impact response model described below.

Vehicle 1

Wheel/ Susp.

Occ. Comp .

Engine

Wheels/ Susp.

Int. Engine

Vehicle 2 Occ. Comp.

Occupant Compartment Accel. (G)

5

Figure 4. Frontal-Frontal Collision Model

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Sim (g)

-15 -20 -25 -30 0

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Figure 2. Validation of Taurus Frontal-Barrier Lumped Mass Model: Crash Test vs. Simulation at 47.2 km/hr

The frontal-frontal collisions were modeled assuming full frontal structural engagement. Offset frontal collisions were not modeled. Although offset frontal crashes are a common frontal crash mode, full frontal engagement is believed to represent a more serious threat to life in terms of its potential for head and thoracic injury [7]. Full frontal-frontal vehicle collisions produce a more severe occupant compartment deceleration pulse, and from this perspective can be regarded as a worst case scenario for frontal-frontal collisions. It should be noted that occupant compartment deformation, sometimes called intrusion, was not modeled in this study. This constraint focuses the study exclusively on injuries resulting from sudden deceleration of the occupant compartment. Intrusion however is an important contributor to occupant injury – especially in higher severity impacts. The lumped mass models used in this study did not however permit the evaluation of intrusion. The lumped mass models were extracted from crash tests at 48 km/hr and 57 km/hr where intrusion is

generally insignificant. Extrapolation of these test results to predict intrusion at higher speeds would be unwarranted. Future studies will use detailed finite element models to investigate intrusion and occupant compartment collapse at these higher speeds. OCCUPANT IMPACT RESPONSE To evaluate the occupant impact response, the Systems Model uses the MADYMO computer code and a modified version of the standard TNO Sedan model. The TNO Standard Model, shown in Figure 5, simulates a driver restrained by a 3-point belt and an unfolding airbag [8]. Both the seat belts and airbag are modeled using finite elements. For this study, the TNO Sedan Model has been modified by eliminating intrusion. In all cases, the airbag is triggered at 15 milliseconds after impact. The driver in all cases is modeled as a 50th percentile male and is assumed to be wearing a seat belt. These restrictions will be revisited in follow-on studies to capture the important effects of unbelted occupants and the interaction of the airbag with both shorter and taller occupants.

thorax was measured in terms of the 3 millisecond clip of chest acceleration. Both injury criteria were computed from the results of the MADYMO simulations. To obtain the potential for occupant injury, HIC and Chest Acceleration are converted to a probability of injury of severity AIS 4 or greater – referred to hereafter as AIS 4+. The Abbreviated Injury Scale (AIS) is a measure of threat to life which varies from 0 to 6 where AIS=0 corresponds to no injury, and AIS = 6 corresponds to fatal injury. AIS = 4 corresponds to severe injury while AIS = 4 includes all severe injuries up to and including fatal injuries. Prasad and Mertz [9] developed a head injury risk function which relates HIC to the probability of injury. This risk function was further adapted by Viano and Arepally [10] who also developed a risk function which relates chest deceleration to the probability of thoracic injury. The injury risk function for the head is shown in Figure 6 while the injury risk function for the chest is shown in Figure 7.

1

Probability of AIS 4+ Injury

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

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HIC

Both the Reference Vehicle and its collision partners use this common occupant interior model and common airbag model. The advantage of this approach is that this strategy focuses the study exclusively on the effect of vehicle design parameters – e.g. vehicle mass and structural stiffness. The disadvantage, of course, is that the occupant response measured in field data are a function of both vehicle and occupant restraint design parameters. The result is that the results of this study concentrate more on aggressivity, which is governed by the structural design, and less on crashworthiness which is influenced by both vehicle and restraint design. OCCUPANT INJURY RESPONSE In this study, occupant injury response was measured in terms of the potential for severe injury to the head and chest. Following the practice of FMVSS No. 208, the crash loading to the head was measured in terms of the Head Injury Criterion (HIC). The crash loading to the

Figure 6. HIC vs. Probability of AIS 4+ Head Injury 1 0.9 Probability of AIS 4+ Injury

Figure 5. MADYMO Belted/Airbag-Restrained Driver Model

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

50

100

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Chest G (3 millisecond clip)

Figure 7. Chest G vs. Probability of AIS 4+ Chest Injury One case that requires careful attention for correct calculation of injury probability is the occupant subjected

to multiple injuries. It is well documented that an individual who suffers multiple injuries has a higher risk of permanent disability or death. To compute the combined effect of injury to the head and chest, this study uses the NHTSA recommended formula shown below [11] for computing the combined probability of AIS 4+ injury: Pcombined = Phead + Pchest – Phead*Pchest

As shown in Figures 9 and 10, driver HIC increased with higher impact speed for both the Taurus and its collision partners. For a given impact speed, HIC for the Taurus driver tended to be more severe in crashes with heavier partners. HIC for the driver of the collision partner tended to be lower for drivers of heavier vehicles. Note that in simulated collisions between the Taurus (1700-kg) and the Neon (1300-kg) the occupant restraint system of the Neon appeared to be overwhelmed by collisions above 40 km/hr.

EXPOSURE To study frontal crash compatibility, the severity of each crash event must be weighted by the relative likelihood of occurrence of that event. The goal of this study was to determine the frontal crash compatibility with each of four separate types of passenger vehicles. Given a collision partner, the primary indicator of crash severity is impact speed. The distribution of delta-velocity for AIS 2+ restrained drivers, as developed by Hackney et al [12], is shown in Figure 8. This distribution was used to obtain the probability of a delta-velocity given that a crash has occurred. Note that although this study caps impact speed at 80 km/hr, Figure 8 indicates that fewer than 1% occur of impacts involve delta-v over 80 km/hr.

As shown in Figures 11 and 12, driver chest deceleration, as measured using the 3 millisecond clip, increased with higher impact speed for both the Taurus and its collision partners. At a given impact speed, chest G’s for the Taurus driver tended to be more severe in collisions with heavier partners. Chest G’s for the driver of the collision partner tended to be lower for heavier vehicles. The chest G’s were substantially lower for the driver of the Ford Expedition (2600-kg) than for the driver of the Dodge Neon (1300-kg).

1600 Taurus-Neon

1400

Taurus-C1500 Taurus-Explorer

1200

Taurus-Expedition

1.0 1000 HIC

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Cumulative Probability

0.8 600

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Figure 9. Taurus-to-Vehicle Collision: Driver Head Injury Level in the Taurus

0.1 0.0 0

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Delta-V (kph)

Taurus-Neon Taurus-C1500 Taurus-Explorer

3000

Taurus-Expedition

Figure 8. Distribution of Frontal Impact Change in Velocity

2500

HIC

2000

1500

RESULTS 1000

As described above, frontal-to-frontal collisions were simulated between a 1993 Ford Taurus and a 1996 Dodge Neon, a 1995 Chevrolet C-1500 pickup, a 1998 Ford Explorer, and a 1999 Ford Expedition. Each pairing involved six SISAME vehicle simulations for each of the six impact speeds under consideration and twelve MADYMO occupant simulations (Taurus driver at each of 6 speeds and the collision partner at the same 6 speeds). OCCUPANT RESPONSES

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Figure 10. Taurus-to-Vehicle Collision: Driver Head Injury Level in the Collision Partner

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Taurus-C1500 Taurus-Explorer Prob. Of AIS 4+ Injury

Taurus-Expedition 3 Msec Clip (G's)

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Figure 13. Taurus-to-Vehicle Collision: Probability of Severe Injury (AIS 4+) in the Taurus

Figure 11. Taurus-to-Vehicle Collision: Driver Chest Injury Level in the Taurus 140

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Figure 12. Taurus-to-Vehicle Collision: Driver Chest Injury Level in the Collision Partner

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Figure 14. Taurus-to-Vehicle Collision: Probability of Severe Injury (AIS 4+) in the Collision Partner

PROBABILITY OF INJURY

FRONTAL CRASH COMPATIBILITY

For each vehicle-to-vehicle pairing and each impact velocity, the combined probability of injury from head and chest impact response was computed. The results are presented in Figure 13 and 14. Under 60 km/hr, the probability of AIS 4+ injury for the Taurus driver was below 20% and was relatively independent of collision partner. However, for impact speeds in excess of 60 km/hr, the probability of AIS 4+ was substantially higher – reaching 70% for impact speeds of 80 km/hr (49 mph) for TaurusExplorer collisions.

The Severe Injury Ratio was used to as a metric of crash compatibility in collisions between the reference car and each of its collision partners. The first step in computing SIR was to calculate the total number of AIS 4+ injuries in both the driver and the collision partners. The total probability of injury was computed by weighting the probability of injury at each impact by the likelihood of that impact speed occurring. These weighted probabilities were summed for each vehicle to obtain a total probability of injury given that a particular crash pairing had occurred. Injury ratios were then computed by dividing the two total probability of Taurus driver injury by the total probability of the collision partner injury.

The probability of injury for the driver of the collision partner on the other hand was a strong function of collision partner. The probability of severe injury for the driver of the Neon in a Taurus-Neon frontal-frontal crash exceed 30% for all collisions above 45 km/hr and exceeded 90% for collisions above 60%. On the other hand, the driver of the Ford Expedition was relatively immune to severe injury. The probability of injury for this driver was under 15% for all collision speeds considered in the study.

As shown in Figure 15, the injury ratios increase with increasing weight of the collision partner. The Expedition subjected the Taurus to the highest injury ratio with a SIR = 2.5. The Neon subjected the Taurus to the lowest injury ratio with a SIR = 0.3. For every severely injured driver in the Expedition there are 2.5 severely injured drivers in the Taurus. In Taurus-Neon collisions, for every severely injured Taurus driver, there are over 3 severely injured Neon drivers.

3.5

Expedition

3.1

3.0

2.5

Explorer Injury Ratio

Collision Partner

Fatality Ratio: Taurus vs. Partner (FARS 1992-96)

C1500

2.4 2.2

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Neon

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AIS 4+ Injury Ratio

For purposes of validation, the Severe Injury Ratios, calculated from simulation, were compared with driver fatality ratios, developed from FARS 1992-96 accident data [1]. The driver fatality ratios were determined by analyzing frontal-to-frontal collisions between midsize passenger cars and subcompact cars (represented by the Neon), midsize cars (represented by the Taurus), SUVs (represented by the Explorer), and pickup trucks (represented by the C-1500). Only two vehicle collisions were considered. To focus the analysis on airbagequipped vehicles, the analysis was restricted to crashes where both vehicles were of model year 1990 or later. Note that as the Ford Expedition was first introduced in 1998, no fatality data are available for this vehicle. The fatality data for this analysis are given in Table 2, and the fatality ratios are presented in Figure 16. Table 2. Driver Fatality Ratios: Midsize Cars vs. Other Collision Partners from FARS 1992-1996. Vehicle B

Driver Deaths in Vehicle -A

C1500

Explorer

Expedition

Collision Partner

Figure 15. Frontal Crash Compatibility: Ratio of AIS 4+ Injuries of Taurus Driver to Collision Partner Driver

Vehicle A

Taurus

Driver DeathsDeaths in A/ Vehicle -B DeathsB 54 0.37

MidSize SubCompact Car Car

20

MidSize MidSize Car Car

15

14

1.0

MidSize Pickup Truck Car

59

27

2.2

MidSize SUV Car

31

Figure 16. Frontal Crash Compatibility: Ratio of AIS 4+ Injuries of Taurus Driver to Collision Partner Driver A comparison of the Severe Injury Ratios and the fatality ratios are presented in Figure 16. Although the fatality ratios are higher, than the injury ratios, the Systems Model has captured both the trend and magnitudes of this compatibility metric. Agreement this close is highly encouraging as the Injury Ratios are based exclusively on computer simulation while the fatality ratios are based upon accident data. Note that the true promise of this tool is not in its validation with the past, but rather for its predictive value. The Ford Expedition, one of the newly introduced superSUVs, does not yet have sufficient accident data for evaluation of compatibility by evaluating historical data. The Systems Model however predicts that the Ford Expedition will be approximately 2.5 times more aggressive than the Ford Taurus. The validation study highlights the areas of needed future enhancement to the Systems Model. One reason that the fatality ratios are higher is because the Systems Model assumes that all drivers are belted. Unbelted drivers have a substantially higher probability of injury than do belted drivers. Likewise, intrusion, an important factor in severe-to-fatal crashes, was not modeled in this study. Follow-on studies with the Systems Model will include both unbelted drivers and structural deformation of the occupant compartment to capture these important aspects of crashworthiness. When included these factors are expected to increase the Severe Injury Ratio for each vehicle.

CONCLUSIONS 10

3.1

This paper has presented the methodology for evaluating frontal crash compatibility using a System Model based upon computer simulation of the frontal-frontal crashes. An analysis was conducted using the Systems Model to evaluate the frontal crash compatibility of a midsize production car. Results of this analysis show that the

Systems Model is capable of capturing the aggressivity trends observed in accident data. But more importantly, the Systems Model has been demonstrated to have great promise to serve as a predictive tool for forecasting the aggressivity of vehicle makes and models which are newly introduced into the market. REFERENCES 1.

2.

3.

4.

5.

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