Evaluation of the ARAS HD ICATS System in Relation to the RICSAC Staged Crash Events.

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1 AWP #2013-3 Evaluation of the ARAS HD ICATS System in Relation to the RICSAC Staged Crash Events. Mike Kennedy, ACTAR, and Paul Hetherington, BTACS ABSTRACT ARAS HD is a software application designed to allow users to draw crash scene diagrams from electronic measurements or hand measurements, and to create threedimensional animations and scene models. It also contains a 2.5D (2D with terrain following) simulation tool called ICATS (Interactive Collision and Trajectory Simulation). ICATS is based on the original published SMAC (Simulation Model of Automobile Collisions) algorithms produced for NHTSA (National Highway Traffic Safety Administration) in the 1970s. The results generated by this tool are often taken to both criminal and civil trials at which the accuracy of the tool will be questioned. This means that the user of the system must be able to describe how the tool works in terms of foundational mathematical algorithms. Users must also give information on the accuracy of the tool in relation to staged crashes. The validation procedure must be described and documented for review by nonscientific parties. This paper describes the way the ICATS system in ARAS HD works and the method and results of the validation study. INTRODUCTION In the 1970s, NHTSA commissioned Calspan to create a computer program to model twovehicle collisions. This led to the creation of the SMAC program. SMAC is a planar, 2D only simulation system that models the vehicles as rectangular boxes with three degrees of freedom (x, y, and yaw). ICATS implements the same system, but in 2.5D. All simulation calculations are done using the three degrees of freedom model, but with terrain following added in for the generated simulation paths. That is, the elevation, pitch, and roll along the simulation trajectories are adjusted to follow the terrain while keeping the x, y, and yaw results from the simulation calculations. Simulation is a multivariable system in which different combinations of parameters can be adjusted to achieve the same result. VALIDATION STUDY Validation of the ICATS system was done by comparing the rest positions, orientations, postimpact trajectories and Delta V from the impact determined by the ICATS run with the data provided by the RICSAC (Research Input for Computer Simulation of Automobile Collisions) tests. These twelve tests were commissioned by NHTSA in the late 1970s to provide a standardized series of tests and data to validate accident reconstruction techniques, such as SMAC and CRASH. The authors of this paper have relied upon the diagrams and data provided by the RICSAC reports, particularly from volumes II and III (Shoemaker, 1978). The diagrams from the reports were scanned and imported into ARAS HD as images and then scaled using the scale indicated on the diagram. No more than ten minutes was spent on each test, modifying input data to get as close a match as possible to the test data while also attempting to stay true to the data provided by the RICSAC tests. In the case of Delta V comparison, as mentioned in several other studies done on the RICSAC tests, the data for Delta V s cannot be used directly as it was taken from sensors that were placed at a distance from the center of gravity

2 of the vehicles. Instead, the Delta V s used for comparison here come from the Engineering Dynamics Corp. (EDC) study of the RICSAC tests (Day & Hargens, 1990). Several tests had data that indicated rear wheel damage causing some rear wheel steerage. Since this cannot be simulated with the current system, these tests will have a higher degree of error in post-impact results. Some of the diagrams did not match the specified inputs; for example, one test had the vehicles listed as having a 10 degree angle of impact, but the diagram was drawn with about a 25 degree angle. Other diagrams showed an inconsistency in the size of the vehicles drawn, with some vehicles being off by as much as three feet. So, as with any test, the results are only as good as the data provided. One of the inputs that is a major influence in the tire-road model is the braking (or roll-resistance) values of each tire. While the RICSAC tests provided a road way friction coefficient (0.87), they provided no data on the roll-resistance of the vehicles, other than to say that they were 100% braked or not. No values for partial braking/damaged tires were provided. In all cases, except for the 100% locked, the actual value for braking/roll-resistance combined with roadway friction had to be estimated. This resulted in values from 0.04 to 0.06 for freerolling for most of the tests. The data from several of the tests indicated that there had to be either partial braking on some/all tires or other forces in effect simply due to the fact that the length of post-impact trajectories did not support either a 100% braking scenario or the low end free-roll scenario. Several tests also had an obvious snag situation. That is, where the two vehicles stuck together for some amount of time after the initial impact, and in some cases remained in contact at their rest positions; the current iteration of ICATS cannot account for these differences. The error analysis done on each test follows the same approach employed by EDC (Day & Hargens, 1990). Path length used for each test was approximated using a spline curve drawn on top of the scaled diagram, rather than the straightline approach used in other studies for path length. Delta Rest Position (R.X rest x component, R.Y is rest y component) ΔR = (R. X pred R. X acc, R. Y pred R. Y acc ) Delta ΔL = Path pred Path acc Path Error ΔR ΔL Yaw Error ΔYaw pred ΔYaw acc 360 Delta V Error ΔV pred ΔV meas ΔV meas See Appendix 1 for full tables of results. See Appendix 2 comparing just Delta V s. LIMITATIONS OF SIMULATION The simulation is simplified to a twodimensional, homogenous plane with only three degrees of freedom. The current ICATS vehicle model is limited to steering inputs on the front

3 tires only. The simulation assumes that during impact the collision forces are much greater than the tire-road forces and, therefore, tireroad forces are neglected during the collision impulse. In the case of large mass differences between vehicles this would not necessarily hold true. The simulation also assumes both vehicles are of the two-axle, four-tire variety. While this is so, a single vehicle and vehiclebarrier impact can still be set up, keeping in mind that the primary configuration was intended to be two-vehicle collisions. SENSITIVITY TO DATA Simulation is highly sensitive to the data provided by the user. While small changes in incoming position/angle will only cause small changes in the separation speeds and Delta V s, they can still cause huge variations in the postimpact trajectories, rest positions and orientations of the vehicles. One of the pitfalls of using simulation is that there are so many variables for which the user may have to estimate the values. Changing different combinations of these values can still end up with the same apparent result. This makes simulation a tool that is, potentially, very easy to misuse. SUMMARY AND CONCLUSION Important observations for users have come from this study. As with any model, the results can never be better than the input. As mentioned in other studies done on the RICSAC tests, the test data itself is subject to some rather large errors in some cases. This is due to limitations in resources and technology at the time these tests were done. Even the Impact Speeds given for these tests are an approximation. Other data, such as Delta V s, have to be calculated based on different sensor data, and again is subject to its own errors. The post-impact trajectory and resulting rest positions/orientation are very sensitive to input data. This author found that even small changes in location and orientation at time of impact could cause a large change in the resulting post-impact response. Other changes, such as Load Deflection, seemed to have less effect on the overall results. Looking at the Delta V comparisons, the range of error is -43% to + 14%, indicating that the simulation is predominantly underestimating the Delta V s. The worst case, Test 9, is out by -43% on Vehicle 2, but the actual difference is only -3.8 mph for the Delta V. The absolute differences range from -5.4 mph to +3.1 mph. ACKNOWLEDGMENTS The authors gratefully acknowledge the assistance of the staff of ARAS 360 Technologies as well as the many contributing authors over the years discussing the RICSAC tests and results from those tests. Additionally, we acknowledge the training classes at both Northwestern University Traffic Institute and IPTM in crash reconstruction techniques. We would specifically like to thank Jerry Ogden (MS, PE, NAFE, ACTAR, ATSSA Certified) of Ogden Engineering and Consulting for his assistance and input. REFERENCES 1. Chan, C., Studies of Vehicle Collisions A Documentation of the Simulation Codes: SMAC (Simulation Model of Automobile Collisions), California PATH Working Paper, UCB-ITS-PWP-98-16, August 1998.

2. Day, T.D., Hargens, R.L., Further Validation of EDSMAC Using the RICSAC Staged Collisions, Engineering Dynamics Corp, SAE Technical Paper Series, 900102, International Congress and Exposition, Detroit, Michigan, February 26-March 2, 1990. 3. McHenry, R.R., Development of a Computer Program to Aid the Investigation of Highway Accidents, Calspan Report No. VJ-2979-V-1, Cornell Aeronautical Lab Inc., Buffalo, NY, 1971. 4. Shoemaker, N.E., Research Input for Computer Simulation of Automobile Collisions Volume II, DOT HS-805 038, Calspan Corp. Advanced Technology Center, Buffalo, NY, 1978. 5. Shoemaker, N.E., Research Input for Computer Simulation of Automobile Collisions Volume III, DOT HS-805 039, Calspan Corp. Advanced Technology Center, Buffalo, NY, 1978. 4

5 APPENDIX 1 Ricsac Test Vehicle No Vehicles Impact Speed Crash Orientation DeltaV DeltaV pred Rest Rest Delta Rest 1 1 Chevy Chevelle 19.8 12.2 13.9-1.94-2.54 1.29 8.52 11.72 21.35 14 19 3.23 11.06 11.522 9.63 5 2 Ford Pinto 19.8 15.6 10.2 4.73 4.79 9.12 8.63 11.53 17.81 27 49 4.39 3.84 5.832 6.28 22 Delta Rest Position Delta Path Length V1 Delta V Error 0.13934 V1 Path Error V2 Path Error V2 Delta V Error -0.34615 0.9831 0.5059 0.0139 0.0611 2 1 Chevy Chevelle 31.5 19.6 20.08 4 2.65-3.8 1.5 28.3 18 86 47-7.8-1.15 7.884 10.3 39 2 Ford Pinto 31.5 n/a 27.26 13.5 12.8 25 15.25 30.7 41.1 45 89 11.5 2.45 11.758 10.4 44 V1 Delta V Error 0.02449 V1 Path Error V2 Path Error V2 Delta V Error n/a 0.2786 0.3830 0.1083 0.1222 3 1 Ford Torino 21.2 9.5 9.6-16.1 20-3.4 21.8 114 99.7 0 0 12.7 1.8 12.827 14.3 0 2 Ford Pinto 0 15.8 15.2-86 9.6-87 11.3 170 171.7 18 23-1 1.7 1.972 1.7 5 V1 Delta V Error 0.01053 V1 Path Error V2 Path Error V2 Delta V Error -0.03797 0.1125 0.0116 0.0000 0.0139 4 1 Ford Torino 38.7 18.7 15.9-83.4 23.5-86.1-18.4 97.6 56.4 140 48-2.7-41.9 41.987 41.2 92 2 Ford Pinto 0 22.2 22.9-105 27.4-79 -8.3 85.8 33.2 80 73 26-35.7 44.164 52.6 7 V1 Delta V Error -0.14973 V1 Path Error V2 Path Error V2 Delta V Error 0.03153 0.4302 0.5147 0.2556 0.0194

6 Ricsac Test Vehicle No Vehicles Impact Speed Crash Orientation DeltaV DeltaV pred Rest Rest Delta Rest Position Delta Rest Delta Path Length 5 1 Ford Torino 39.7 16.3 16-327.6 0.5-362.1-0.2 250 284.6 1-15.5-34.5 37.822 250.2 283.6 0 2 Honda Civic 0 25.1 28.2-137 34.1-139.4 18.8 61.6 51.6 296 5.9-2.4 6.369 42.8 244.4 16.5 V1 Delta V Error -0.01840 V1 Path Error V2 Path Error V2 Delta V Error 0.12351 1.0008 0.6948 0.0458 0.8058 6 1 Chevy Malibu 21.5 9.2 7.2-43.5 4.7-115.9 4.5 63.1 133.5 17 6-72.4-0.2 72.400 70.4 11 2 VW Rabbit 21.5 11.9 10.7-2 13.9 14.7 30.7 22.1 33.9 122 181 16.7 16.8 23.688 11.8 59 V1 Delta V Error -0.21739 V1 Path Error V2 Path Error V2 Delta V Error -0.10084 1.1474 1.0719 0.0306 0.1639 7 1 Chevy Malibu 29.1 12 10-81 -6.3-187.7 5.1 91.1 190 16 10-107 11.4 107.307 98.9 6 2 VW Rabbit 29.1 16.5 15.4-19.25 22.8 2.5 21.2 44.1 44.8 144 214 21.75-1.6 21.809 0.7 70 V1 Delta V Error -0.16667 V1 Path Error V2 Path Error V2 Delta V Error -0.06667 1.1779 0.4945 0.0167 0.1944 8 1 Chevy Chevelle 20.7 15.3 15-33.6-21.4-53.6-5.6 14.8 39 43 21-20 15.8 25.488 24.2 22 2 Chevy Chevelle 20.7 10.7 12.2-36.8-10.4-28.7-11 22.7 28.2 50 78 8.1-0.6 8.122 5.5 28 V1 Delta V Error -0.01961 V1 Path Error V2 Path Error V2 Delta V Error 0.14019 1.7222 0.3578 0.0611 0.0778

7 Ricsac Test Vehicle No Vehicles Impact Speed Crash Orientation DeltaV DeltaV pred Rest Rest Delta Rest Position Delta Rest Delta Path Length 9 1 Honda Civic 21.2 21.4 16.7 13.8-9.9 14.8-7.7 42.8 38.4 104 100 1 2.2 2.417 4.4 4 2 Ford Torino 21.2 8.9 5.1 29-16.3 19.5 9.4 62.1 47 60 14-9.5 25.7 27.400 15.1 46 V1 Delta V Error -0.21963 V1 Path Error V2 Path Error V2 Delta V Error -0.42697 0.0565 0.4412 0.0111 0.1278 10 1 Honda Civic 33.3 35.1 30.9-46.5-30.8-46 -50.6 45 25.3 90 146 0.5-19.8 19.806 19.7 56 2 Ford Torino 33.3 14.1 13.5 0 0 0 0 0 0 0 0 0 0 0.000 0 0 V1 Delta V Error -0.11966 V1 Path Error V2 Path Error V2 Delta V Error -0.04255 0.4401 n/a 0.1556 n/a can't do actual comparison for vehicle 2 due to post-impact collision with a foreign object plus moving onto a gravel/grass area that was not recorded 11 1 Chevy Vega 20.4 24 26.5-78 -34.3-73.9-33.3 10.6 9.2 2 18 4.1 1 4.220 1.4 16 2 Ford Torino 20.4 15.7 17.8-61.8-27.8-57.3-27.2 7.8 3.2 0 0 4.5 0.6 4.540 4.6 0 V1 Delta V Error 0.10417 V1 Path Error V2 Path Error V2 Delta V Error 0.13376 0.3981 0.5820 0.0444 0.0000 12 1 Chevy Vega 31.5 40.1 39.5-81.5-32 -83.3-34.4 8 15.5 53 24-1.8-2.4 3.000 7.5 29 2 Ford Torino 31.5 26.4 27.3-66.9-22.8-66.9-22.8 7.5 6.36 13 13 0 0 0.000 1.14 0 V1 Delta V Error -0.01496 V1 Path Error V2 Path Error V2 Delta V Error 0.03409 0.3750 0.0000 0.0806 0.0000

8 APPENDIX 2 Comparison of Delta V s RICSAC Delta V Comparison RICSAC Test # Vehicle 1 Vehicle 2 Vehicle 1 icted Vehicle 2 icted Vehicle 1 Error % Vehicle 2 Error % Vehicle 1 Error Abs Vehicle 2 Error Abs 1 12.2 15.6 13.9 10.2 14% -35% 1.7-5.4 2 19.6 n/a 20.1 27.3 3% n/a 0.5 n/a 3 9.5 15.8 9.6 15.2 1% -4% 0.1-0.6 4 18.7 22.2 15.9 22.9-15% 3% -2.8 0.7 5 16.3 25.1 16 28.2-2% 12% -0.3 3.1 6 9.2 11.9 7.2 10.7-22% 10% -2-1.2 7 12 16.5 10 15.4-17% -7% -2-1.1 8 15.3 10.7 15 12.2-2% 14% -0.3 1.5 9 21.4 8.9 16.7 5.1-22% -43% -4.7-3.8 10 35.1 14.1 30.9 13.5-12% -4% -4.2-0.6 11 24 15.7 26.5 17.8 10% 13% 2.5 2.1 12 40.1 26.4 39.5 27.3-2% 3% -0.6 0.9