신경 회로망을 이용한 보행자와의 충돌 위험 판단 방법Collision Risk Assessment for Pedestrians' Safety Using Neural Network
- Other Titles
- Collision Risk Assessment for Pedestrians' Safety Using Neural Network
- Authors
- 박성근
- Issue Date
- Jan-2011
- Publisher
- 제어·로봇·시스템학회
- Keywords
- intelligent vehicle; monte carlo; neural networks; collision risk; monte carlo simulation
- Citation
- 제어.로봇.시스템학회 논문지, v.17, no.1, pp.6 - 11
- Journal Title
- 제어.로봇.시스템학회 논문지
- Volume
- 17
- Number
- 1
- Start Page
- 6
- End Page
- 11
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/16801
- ISSN
- 1976-5622
- 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.
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Collections - SCH Media Labs > Department of Smart Automobile > 1. Journal Articles
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