Vehicle kinematics ahead prediction for localized impact by the means of autoregressive model with exogenous input

In this paper we present the application of regressive models to simulation of a full-scale vehicle-to-pole impact. The capability of an ARMAX model to reproduce vehicle kinematics was examined. Regressive model parameters were established by minimizing a weighted sum of squares of prediction errors. The prediction horizon was assigned to evaluate model's robustness and verify its time series data forecasting performance. It was found that the ARMAX model reproduces the signal which was used for its establishment (i.e. real vehicle's acceleration). Moreover, such estimation technique preserves all characteristic information relevant for a given collision, since integration of the estimated acceleration pulse yields plots of velocity and displacement which closely follow the reference ones.

[1]  Kai-Yew Lum,et al.  Parameter estimation of ARX/NARX model: a neural network based method , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[2]  Bo-Suk Yang,et al.  A hybrid of nonlinear autoregressive model with exogenous input and autoregressive moving average model for long-term machine state forecasting , 2010, Expert Syst. Appl..

[3]  Hamid Reza Karimi,et al.  Signal Analysis and Performance Evaluation of a Vehicle Crash Test with a Fixed Safety Barrier Based on Haar Wavelets , 2011, Int. J. Wavelets Multiresolution Inf. Process..

[4]  Hamid Reza Karimi,et al.  EFFECTS OF DIFFERENT SPRING-MASS MODEL ELASTO-PLASTIC UNLOADING SCENARIOS ON THE VEHICLE CRASH MODEL FIDELITY , 2011 .

[5]  W. Pawlus,et al.  Further results on mathematical models of vehicle localized impact , 2010, 2010 3rd International Symposium on Systems and Control in Aeronautics and Astronautics.

[6]  S. J. Hu,et al.  Data-based approach in modeling automobile crash , 1995 .

[7]  Nikolaos Kourentzes,et al.  Feature selection for time series prediction - A combined filter and wrapper approach for neural networks , 2010, Neurocomputing.

[8]  Ted Belytschko,et al.  On Computational Methods for Crashworthiness , 1988 .

[9]  Noureddine Zerhouni,et al.  Defining and applying prediction performance metrics on a recurrent NARX time series model , 2010, Neurocomputing.

[10]  K. Jeong,et al.  Non-linear autoregressive modelling by Temporal Recurrent Neural Networks for the prediction of freshwater phytoplankton dynamics , 2008 .

[11]  Eindhoven,et al.  LPV Modeling of Vehicle Occupants , 2008 .

[12]  Hamid Reza Karimi,et al.  Comparative analysis of vehicle to pole collision models established using analytical methods and neural networks , 2010 .

[13]  Mohamed Abdel-Aty,et al.  Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections , 2001 .

[14]  Hamid Reza Karimi,et al.  Mathematical modeling and analysis of a vehicle crash , 2010 .

[15]  H. Zohm,et al.  Autoregressive moving average model for analyzing edge localized mode time series on Axially Symmetric Divertor Experiment (ASDEX) Upgrade tokamak , 2004 .

[16]  Karl-Gustaf Sundin,et al.  Identification of lumped parameter automotive crash models for bumper system development , 2009 .

[17]  Laura Giarré,et al.  NARX models of an industrial power plant gas turbine , 2005, IEEE Transactions on Control Systems Technology.

[18]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[19]  Biao Huang,et al.  System Identification , 2000, Control Theory for Physicists.

[20]  L. Faes,et al.  Linear and nonlinear parametric model identification to assess granger causality in short-term cardiovascular interactions , 2008, 2008 Computers in Cardiology.

[21]  Hamid Reza Karimi,et al.  Development of Mathematical Models for Analysis of a Vehicle Crash , 2010 .

[22]  Norman Jones,et al.  Vehicle Crash Mechanics , 2002 .

[23]  A. Várkonyi-Kóczy,et al.  Intelligent Methods for Car Deformation Modeling and Crash Speed Estimation , 2004 .

[24]  Les E. Atlas,et al.  Recurrent neural networks and robust time series prediction , 1994, IEEE Trans. Neural Networks.

[25]  Hamid Reza Karimi,et al.  Development of lumped-parameter mathematical models for a vehicle localized impact , 2011 .

[26]  Kjell G. Robbersmyr,et al.  Application of viscoelastic hybrid models to vehicle crash simulation , 2011 .

[27]  Péter Várlaki,et al.  Energy distribution modeling of car body deformation using LPV representations and fuzzy reasoning , 2008 .

[28]  Azim Eskandarian,et al.  Vehicle crash modelling using recurrent neural networks , 1998 .