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

3 Citations (Scopus)

Abstract

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.
Pages1265-1270
Number of pages6
ISBN (Electronic)9781509018215
DOIs
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
Volume2016-August

Other

Other2016 IEEE Intelligent Vehicles Symposium, IV 2016
CountrySweden
CityGotenburg
Period16/6/1916/6/22

Fingerprint

Neural Network Ensemble
Alarm systems
Collision
Neural networks
Fuzzy logic
Driver
Situation Assessment
Fuzzy Logic
Time Complexity
Simulation
Neural Networks
Calculate
Target

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Automotive Engineering
  • Modelling and Simulation

Cite this

Jhonghyun, A., Baehoon, C., Taehun, H., & Kim, E. (2016). A novel rear-end collision warning system using neural network ensemble. In 2016 IEEE Intelligent Vehicles Symposium, IV 2016 (pp. 1265-1270). [7535553] (IEEE Intelligent Vehicles Symposium, Proceedings; Vol. 2016-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IVS.2016.7535553
Jhonghyun, An ; Baehoon, Choi ; Taehun, Hwang ; Kim, Euntai. / A novel rear-end collision warning system using neural network ensemble. 2016 IEEE Intelligent Vehicles Symposium, IV 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1265-1270 (IEEE Intelligent Vehicles Symposium, Proceedings).
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Jhonghyun, A, Baehoon, C, Taehun, H & Kim, E 2016, A novel rear-end collision warning system using neural network ensemble. in 2016 IEEE Intelligent Vehicles Symposium, IV 2016., 7535553, IEEE Intelligent Vehicles Symposium, Proceedings, vol. 2016-August, Institute of Electrical and Electronics Engineers Inc., pp. 1265-1270, 2016 IEEE Intelligent Vehicles Symposium, IV 2016, Gotenburg, Sweden, 16/6/19. https://doi.org/10.1109/IVS.2016.7535553

A novel rear-end collision warning system using neural network ensemble. / Jhonghyun, An; Baehoon, Choi; Taehun, Hwang; Kim, Euntai.

2016 IEEE Intelligent Vehicles Symposium, IV 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1265-1270 7535553 (IEEE Intelligent Vehicles Symposium, Proceedings; Vol. 2016-August).

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

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Jhonghyun A, Baehoon C, Taehun H, Kim E. A novel rear-end collision warning system using neural network ensemble. In 2016 IEEE Intelligent Vehicles Symposium, IV 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1265-1270. 7535553. (IEEE Intelligent Vehicles Symposium, Proceedings). https://doi.org/10.1109/IVS.2016.7535553