Determining vehicle pre-crash speed in frontal barrier crashes using artificial neural network for intermediate car class

被引：11

作者：

Mrowicki, Adam ^{[1
]}

Krukowski, Mateusz ^{[2
]}

Turobos, Filip ^{[2
]}

Kubiak, Przemyslaw ^{[1
,3
]}

机构：

[1] Lodz Univ Technol, Dept Vehicles & Fundamentals Machine Design, Lodz, Poland

[2] Lodz Univ Technol, Inst Math, Lodz, Poland

[3] Warsaw Univ Technol, Inst Vehicles, Warsaw, Poland

来源：

FORENSIC SCIENCE INTERNATIONAL | 2020年 / 308卷

关键词：

Neural networks; Car crash reconstruction; Car accidents; NONLINEAR METHOD;

D O I：

10.1016/j.forsciint.2020.110179

中图分类号：

DF [法律]; D9 [法律]; R [医药、卫生];

学科分类号：

0301 ; 10 ;

摘要：

This paper introduces a new, innovative approach to pre-crash velocity determination, namely the artificial neural networks. A perceptron based on a database obtained from NHTSA (National Highway Traffic Safety Administration) with numerous data concerning frontal vehicle crash tests: i.e. vehicle mass, deformation zone and deformation coefficients C-1-C-6. Part of the database entries were used to train the network to develop consistent accuracy and the remainder was used as validation and training sets. (C) 2020 Published by Elsevier B.V.

引用

页数：5

共 10 条

[1] Determining vehicle pre-crash speed in frontal barrier crashes using genetic algorithm model adjustment techniques for intermediate car class
Mrowicki, Adam
Krukowski, Mateusz
Turobos, Filip
Jaskiewicz, Marek
Radkowski, Stanislaw
Kubiak, Przemyslaw
INTERNATIONAL JOURNAL OF CRASHWORTHINESS, 2022, 27 (04) : 1009 - 1016
[2] Non-linear method of determining vehicle pre-crash speed based on tensor B-spline products with probabilistic weights - Intermediate Car Class
Krukowski, Mateusz
Kubiak, Przemyslaw
Mrowicki, Adam
Siczek, Krzysztof
Gralewski, Jacek
FORENSIC SCIENCE INTERNATIONAL, 2018, 293 : 7 - 16
[3] A deep neural network inverse solution to recover pre-crash impact data of car collisions*
Chen, Qijun
Xie, Yuxi
Ao, Yu
Li, Tiange
Chen, Guorong
Ren, Shaofei
Wang, Chao
Li, Shaofan
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2021, 126
[4] Characterizing future crashes on Indian roads using counterfactual simulations of pre-crash vehicle safety technologies
Puthan, Pradeep
Lubbe, Nils
Davidsson, Johan
IATSS RESEARCH, 2022, 46 (04) : 479 - 491
[5] Predicting Free Flow Speed and Crash Risk of Bicycle Traffic Flow Using Artificial Neural Network Models
Xu, Cheng
Li, Qiangwei
Qu, Zhaowei
Jin, Sheng
MATHEMATICAL PROBLEMS IN ENGINEERING, 2015, 2015
[6] Estimation of Vehicular Speed and Passenger Car Equivalent Under Mixed Traffic Condition Using Artificial Neural Network
Biswas, Subhadip
Chandra, Satish
Ghosh, Indrajit
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2017, 42 (09) : 4099 - 4110
[7] Estimation of Vehicular Speed and Passenger Car Equivalent Under Mixed Traffic Condition Using Artificial Neural Network
Subhadip Biswas
Satish Chandra
Indrajit Ghosh
Arabian Journal for Science and Engineering, 2017, 42 : 4099 - 4110
[8] Analysis of Frontal Face Detection Performance by Using Artificial Neural Network (ANN) and Speed-up Robust Features (SURF)Technique
Ali, Nursabillilah Mohd
Karis, Mohd Safirin
Abd Aziz, Munawwarah
Abidin, Amar Faiz Zainal
INTERNATIONAL CONFERENCE ON ADVANCED SCIENCE, ENGINEERING AND TECHNOLOGY (ICASET 2015), 2016, 1774
[9] Modelling crash severity outcomes for low speed urban roads using back propagation - Artificial neural network (BP - ANN) - A case study in Indian context
Barman, Santanu
Bandyopadhyaya, Ranja
IATSS RESEARCH, 2023, 47 (03) : 382 - 400
[10] Sensorless vector control of induction motors in fuel cell vehicle using a Neuro-Fuzzy speed controller and an on-line Artificial Neural Network speed estimator.
Jalili, K
Farhangi, S
Saievar-Iranizad, E
PROCEEDINGS OF THE 2001 IEEE INTERNATIONAL CONFERENCE ON CONTROL APPLICATIONS (CCA'01), 2001, : 259 - 264

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