An expert system using RBF neural network for estimating vehicle speed based on length of skid mark

Wen-Kung Tseng, Shih Syong Liao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents an expert system to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. Since the length of the skid mark varies with many factors, there is no a single formula or equation which can represent the relationship between the vehicle pre-braking speed and the length of the skid mark. Therefore in this paper an expert system is built to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. The radial basis function (RBF) neural network is used for the expert system due to its shorter training time and higher accuracy. There are many factors affecting the skid mark. In this paper we choose 7 factors, i.e. brand of vehicle, vehicle displacement, year of manufacture, vehicle weight, vehicles with and without ABS, roadway surface, and vehicle speed for the training in the RBF neural network. The total number of the training data for the RBF neural network is 2619. The results showed that high accuracy is obtained for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark. Thus the expert system proposed in this paper is demonstrated to be a suitable system for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark.

Original languageEnglish
Title of host publicationProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
Pages631-635
Number of pages5
DOIs
Publication statusPublished - 2011 Oct 6
Event2011 7th International Conference on Natural Computation, ICNC 2011 - Shanghai, China
Duration: 2011 Jul 262011 Jul 28

Publication series

NameProceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
Volume2

Other

Other2011 7th International Conference on Natural Computation, ICNC 2011
CountryChina
CityShanghai
Period11-07-2611-07-28

Fingerprint

Expert Systems
Expert systems
Neural networks
Braking
Weights and Measures

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Neuroscience(all)

Cite this

Tseng, W-K., & Liao, S. S. (2011). An expert system using RBF neural network for estimating vehicle speed based on length of skid mark. In Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011 (pp. 631-635). [6022211] (Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011; Vol. 2). https://doi.org/10.1109/ICNC.2011.6022211
Tseng, Wen-Kung ; Liao, Shih Syong. / An expert system using RBF neural network for estimating vehicle speed based on length of skid mark. Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011. 2011. pp. 631-635 (Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011).
@inproceedings{c176310b087d42589ba15c238e7a6f69,
title = "An expert system using RBF neural network for estimating vehicle speed based on length of skid mark",
abstract = "This paper presents an expert system to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. Since the length of the skid mark varies with many factors, there is no a single formula or equation which can represent the relationship between the vehicle pre-braking speed and the length of the skid mark. Therefore in this paper an expert system is built to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. The radial basis function (RBF) neural network is used for the expert system due to its shorter training time and higher accuracy. There are many factors affecting the skid mark. In this paper we choose 7 factors, i.e. brand of vehicle, vehicle displacement, year of manufacture, vehicle weight, vehicles with and without ABS, roadway surface, and vehicle speed for the training in the RBF neural network. The total number of the training data for the RBF neural network is 2619. The results showed that high accuracy is obtained for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark. Thus the expert system proposed in this paper is demonstrated to be a suitable system for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark.",
author = "Wen-Kung Tseng and Liao, {Shih Syong}",
year = "2011",
month = "10",
day = "6",
doi = "10.1109/ICNC.2011.6022211",
language = "English",
isbn = "9781424499533",
series = "Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011",
pages = "631--635",
booktitle = "Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011",

}

Tseng, W-K & Liao, SS 2011, An expert system using RBF neural network for estimating vehicle speed based on length of skid mark. in Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011., 6022211, Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011, vol. 2, pp. 631-635, 2011 7th International Conference on Natural Computation, ICNC 2011, Shanghai, China, 11-07-26. https://doi.org/10.1109/ICNC.2011.6022211

An expert system using RBF neural network for estimating vehicle speed based on length of skid mark. / Tseng, Wen-Kung; Liao, Shih Syong.

Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011. 2011. p. 631-635 6022211 (Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011; Vol. 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - An expert system using RBF neural network for estimating vehicle speed based on length of skid mark

AU - Tseng, Wen-Kung

AU - Liao, Shih Syong

PY - 2011/10/6

Y1 - 2011/10/6

N2 - This paper presents an expert system to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. Since the length of the skid mark varies with many factors, there is no a single formula or equation which can represent the relationship between the vehicle pre-braking speed and the length of the skid mark. Therefore in this paper an expert system is built to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. The radial basis function (RBF) neural network is used for the expert system due to its shorter training time and higher accuracy. There are many factors affecting the skid mark. In this paper we choose 7 factors, i.e. brand of vehicle, vehicle displacement, year of manufacture, vehicle weight, vehicles with and without ABS, roadway surface, and vehicle speed for the training in the RBF neural network. The total number of the training data for the RBF neural network is 2619. The results showed that high accuracy is obtained for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark. Thus the expert system proposed in this paper is demonstrated to be a suitable system for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark.

AB - This paper presents an expert system to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. Since the length of the skid mark varies with many factors, there is no a single formula or equation which can represent the relationship between the vehicle pre-braking speed and the length of the skid mark. Therefore in this paper an expert system is built to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. The radial basis function (RBF) neural network is used for the expert system due to its shorter training time and higher accuracy. There are many factors affecting the skid mark. In this paper we choose 7 factors, i.e. brand of vehicle, vehicle displacement, year of manufacture, vehicle weight, vehicles with and without ABS, roadway surface, and vehicle speed for the training in the RBF neural network. The total number of the training data for the RBF neural network is 2619. The results showed that high accuracy is obtained for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark. Thus the expert system proposed in this paper is demonstrated to be a suitable system for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark.

UR - http://www.scopus.com/inward/record.url?scp=80053419692&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053419692&partnerID=8YFLogxK

U2 - 10.1109/ICNC.2011.6022211

DO - 10.1109/ICNC.2011.6022211

M3 - Conference contribution

AN - SCOPUS:80053419692

SN - 9781424499533

T3 - Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011

SP - 631

EP - 635

BT - Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011

ER -

Tseng W-K, Liao SS. An expert system using RBF neural network for estimating vehicle speed based on length of skid mark. In Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011. 2011. p. 631-635. 6022211. (Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011). https://doi.org/10.1109/ICNC.2011.6022211