Collision risk assessment for pedestrians' safety using neural network

Beomseong Kim, Seongkeun Park, Baehoon Choi, Euntai Kim, Heejin Lee, Hyung Jin Kang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)6-11
Number of pages6
JournalJournal of Institute of Control, Robotics and Systems
Issue number1
Publication statusPublished - 2011 Jan

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Applied Mathematics


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