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Occupant Injury Response Prediction Prior to Crash Based on Pre-Crash Systems
Technical Paper
2017-01-1471
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Occupant restraint systems are developed based on some baseline experiments. While these experiments can only represent small part of various accident modes, the current procedure for utilizing the restraint systems may not provide the optimum protection in the majority of accident modes. This study presents an approach to predict occupant injury responses before the collision happens, so that the occupant restraint system, equipped with a motorized pretensioner, can be adjusted to the optimal parameters aiming at the imminent vehicle-to-vehicle frontal crash. The approach in this study takes advantage of the information from pre-crash systems, such as the time to collision, the relative velocity, the frontal overlap, the size of the vehicle in the front and so on. In this paper, the vehicle containing these pre-crash features will be referred to as ego vehicle. The information acquired and the basic crash test results can be integrated to predict a simplified crash pulse. The injury of the occupant in the ego vehicle can thus be predicted using this crash pulse. The approach is verified by some vehicle-to-vehicle crash tests.
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Luo, X., Du, W., Li, H., LI, P. et al., "Occupant Injury Response Prediction Prior to Crash Based on Pre-Crash Systems," SAE Technical Paper 2017-01-1471, 2017, https://doi.org/10.4271/2017-01-1471.Also In
References
- Carlin, J., Birdsong, C., Schuster, P., Thompson, W. et al., "Evaluation of Cost Effective Sensor Combinations for a Vehicle Precrash Detection System," SAE Technical Paper 2005-01-3578, 2005, doi:10.4271/2005-01-3578.
- Mori, H., Charkari, N. M., “Shadow and Rhythm as Sign Patterns of Obstacle Detection,” Industrial Electronics, 1993, Conference Proceedings, ISIE’ 93, Budapest, Hungary, June 1-3, 1993, doi: 10.1109/ISIE.1993.268796.
- Zielke, T., Braukmann, M., Seelen, W., “Intersity and Edge-based Symmetry Detection with an Application to Car-Following,” CVGIP: Image Understanding, 58: 177-190, 1993, doi: 10.1006/ciun.1993.1037.
- Broggi, A., Cerri, P., Antonello, P. C., “Multi-Resolution Vehicle Detection Using Artificial Vision,” Intelligent Vehicles Symposium, 2004 IEEE, Parma, Italy, June 14-17, 2004, doi: 10.1109/IVS.2004.1336400.
- Nishigaki, M., Rebhan, S., Einecke, N., “Vision-Based Lateral Position Improvement of RADAR Detections,” 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, Alaska, USA, September 16-19, 2012, doi: 10.1109/ITSC.2012.6338679.
- Haselhoff, A., Kummert, A., Schneider, G., “Radar-Vision Fusion with an Application to Car-Following Using an Improved AdaBoost Detection Algorithm,” Proceedings of the 2007 IEEE Intelligent Transportation Systems Conference, Seattle, WA, USA, September 30-October 3, 2007, doi: 10.1109/ITSC.2007.4357647.
- DuBois, P., Chou, C. C., Fileta, B. B. et al., “Vehicle Crashworthiness and Occupant Protection,” American Iron and Steel Institute, Southfield, Michigan, 2004.
- Wagstrom, L., Thompson, R., Pipkorn B., “Structual Adaptivity for Acceleration Level Reduction in Passenger Car Frontal Collisions,” International Journal of Crashworthiness, 9(2): 121-127, 2004, doi: 10.1533/ijcr.2004.0285.
- Woolley, R., "Crash Pulse Scaling Applied to Accident Reconstruction," SAE Technical Paper 2008-01-0183, 2008, doi:10.4271/2008-01-0183.
- Varat, M. and Husher, S., "Vehicle Impact Response Analysis Through the Use of Accelerometer Data," SAE Technical Paper 2000-01-0850, 2000, doi:10.4271/2000-01-0850.
- Woolley, R., "Non-Linear Damage Analysis in Accident Reconstruction," SAE Technical Paper 2001-01-0504, 2001, doi:10.4271/2001-01-0504.