A novel rear-end collision warning system using neural network ensemble

An Jhonghyun, Choi Baehoon, Hwang Taehun, Euntai Kim

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

10 Citations (Scopus)


Negligence of a driver or a sudden stop of a forward vehicle can cause rear-end collision. In this paper, we propose a new situation assessment algorithm to determine collision probability to prevent the rear-end collision. The proposed algorithm consists of two phases: coarse assessment and fine assessment. In the coarse assessment, the algorithm selects a target vehicle with the highest possibility of collision by using fuzzy logic. In fine assessment, it determines collision probability based on a statistical approach considering driving maneuvers; it models the driving maneuvers to enable the driver to operate the vehicle in conditions toward the collision and calculates the collision probability as the ratio between the total driving maneuvers and the driving maneuvers in possible collisions. To reduce the simulation time complexity, we adapt a neural network. Since there exist variance of widths for different vehicles, we also apply neural network ensemble to cope with the variance. Numerical evaluation of the proposed method is provided through simulations and practical tests.

Original languageEnglish
Title of host publication2016 IEEE Intelligent Vehicles Symposium, IV 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509018215
Publication statusPublished - 2016 Aug 5
Event2016 IEEE Intelligent Vehicles Symposium, IV 2016 - Gotenburg, Sweden
Duration: 2016 Jun 192016 Jun 22

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings


Other2016 IEEE Intelligent Vehicles Symposium, IV 2016

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Automotive Engineering
  • Modelling and Simulation


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