Abstract
An expert system has been proposed 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 language | English |
|---|---|
| Title of host publication | New Trends in Mechatronics and Materials Engineering |
| Pages | 165-169 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2012 Feb 13 |
| Event | 2011 International Conference on Mechatronics and Materials Engineering, ICMME 2011 - Qiqihar, China Duration: 2011 Dec 10 → 2011 Dec 12 |
Publication series
| Name | Applied Mechanics and Materials |
|---|---|
| Volume | 151 |
| ISSN (Print) | 1660-9336 |
| ISSN (Electronic) | 1662-7482 |
Other
| Other | 2011 International Conference on Mechatronics and Materials Engineering, ICMME 2011 |
|---|---|
| Country | China |
| City | Qiqihar |
| Period | 11-12-10 → 11-12-12 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
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Estimation of vehicle pre-braking speed. / Tseng, Wen-Kung; Liao, S. X.
New Trends in Mechatronics and Materials Engineering. 2012. p. 165-169 (Applied Mechanics and Materials; Vol. 151).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Estimation of vehicle pre-braking speed
AU - Tseng, Wen-Kung
AU - Liao, S. X.
PY - 2012/2/13
Y1 - 2012/2/13
N2 - An expert system has been proposed 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 - An expert system has been proposed 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=84856765148&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84856765148&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/AMM.151.165
DO - 10.4028/www.scientific.net/AMM.151.165
M3 - Conference contribution
AN - SCOPUS:84856765148
SN - 9783037853504
T3 - Applied Mechanics and Materials
SP - 165
EP - 169
BT - New Trends in Mechatronics and Materials Engineering
ER -