Intelligent collision risk assessment based on Neural Network Ensemble

Bumsung Kim, Baehoon Choi, Seongkeun Park, Euntai Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


In this paper, we propose the collision risk assessment system. When pedestrian is detected by radar or another sensor, system could know the pedestrian's position and velocity. Using this information, system can compute the collision risk. If system does not concerned about the simulation time, Monte Carlo Simulation is simple and powerful method. But in dynamic circumstance, the position and velocity of pedestrian is changed rapidly. So I propose to apply Neural Network Ensemble in this problem. Neural Network train the network using training data, this process take a long time. But by using trained network, system can compute the collision risk quickly. However, wide range of input data can cause huge memory use, and lengthy simulation time. So we propose apply Neural Network Ensemble to this problem. Neural Network Ensemble separate the input data and training each network with different data set. This method will reduce the computation load with small error.

Original languageEnglish
Title of host publicationProceedings of SICE Annual Conference 2010, SICE 2010 - Final Program and Papers
PublisherSociety of Instrument and Control Engineers (SICE)
Number of pages4
ISBN (Print)9784907764364
Publication statusPublished - 2010

Publication series

NameProceedings of the SICE Annual Conference

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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