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http://dx.doi.org/10.5302/J.ICROS.2011.17.1.6

Collision Risk Assessment for Pedestrians' Safety Using Neural Network  

Kim, Beom-Seong (Yonsei University)
Park, Seong-Keun (Yonsei University)
Choi, Bae-Hoon (Yonsei University)
Kim, Eun-Tai (Yonsei University)
Lee, Hee-Jin (Hankyong National University)
Kang, Hyung-Jin (MANDO CORP.)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.17, no.1, 2011 , pp. 6-11 More about this Journal
Abstract
This paper proposes a new collision risk assessment system for pedestrians's safety. Monte Carlo Simulation (MCS) method is a one of the most popular method that rely on repeated random sampling to compute their result, and this method is also proper to get the results when it is unfeasible or impossible to compute an exact result. Nevertheless its advantages, it spends much time to calculate the result of some situation, we apply not only MCS but also Neural Networks in this problem. By Monte carlo method, we make some sample data for input of neural networks and by using this data, neural networks can be trained for computing collision probability of whole area where can be measured by sensors. By using this trained networks, we can estimate the collision probability at each positions and velocities with high speed and low error rate. Computer simulations will be shown the validity of our proposed method.
Keywords
intelligent vehicle; monte carlo; neural networks; collision risk; monte carlo simulation;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
Times Cited By SCOPUS : 2
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