2007 Fiscal Year Final Research Report Summary
A research on vehicle accident reconstruction using inverse problem analysis
| Project/Area Number |
18560229
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Dynamics/Control
|
|---|
| Research Institution | Toyohashi University of Technology |
|---|
Principal Investigator |
MINAMOTO Hirofumi Toyohashi University of Technology, Department of mechanical Engineering, Associate Professor (20273328)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
KAWAMURA Shozo Toyohashi University of Technology, Research renter for future vehicle, Professor (00204777)
|
|---|
| Project Period (FY) |
2006 – 2007
|
| Keywords | Automobile / Inverse problem / vehicle collision / Accident reconstruction / Neural network |
|---|
| Research Abstract |
Accidents of vehicle collision are serious problem in our society today. Accident reconstruction contains the process that seeks the vehicle pm-impact speeds from the post-impact vehicle rest position. This is understood as one of the inverse problems which reconstruct the cause of the incident from the post-incident results. The purpose of this research is to construct the method of inverse analysis for the vehicle collision accidents, lb solve the vehicle collision accident inversely the artificial neural network technique was used in this research. The results of this research are as followings
1. The vehicle collision accident was divided into two stages: vehicle collision and post-impact motion. The inverse analyses were carried out by using artificial neural network for each stage. By using the both inverse analyses sequentially, the pre-impact vehicle speeds were reconstructed from the final vehicle rest positions. In this research, the vehicle pre-impact speeds were reconstructe
… More
d in the reasonable accuracy for right angled side impact accident. This time, only the right angled collision accidents were examined. But the validity of this method has to be examined for other impact configurations in near fixture.
2. In the learning process of the artificial neural network, supervised signals for the post-impact vehicle motion were made by detailed vehicle dynamics model using CarSim simulation software. On the other hand, the rigid body collision theory, which may give physically rational but practically rough estimations of pre- and poet- impact speeds, was used to make the supervised signal for the vehicle impact speeds. Therefore, the results of this time of research do not have enough accuracy for practical reconstruction. However, they may be used as a first estimation for the detailed reconstruction at the present.
3. When more accurate and systematic supervised signals axe available, the accuracy of this method will increase naturally. Of course, the frame work which constructed in this research will be still valid for such caws and have a possibility to carry out more accurate inverse analyses Less
|
