Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system

Shen Cherng, Chiung Yao Fang, Chia Pei Chen, Sei Wang Chen

Research output: Contribution to journalArticle

48 Citations (Scopus)

Abstract

Driving always involves risk. Various means have been proposed to reduce the risk. Critical motion detection of nearby moving vehicles is one of the important means of preventing accidents. In this paper, a computational model, which is referred to as the dynamic visual model (DVM), is proposed to detect critical motions of nearby vehicles while driving on a highway. The DVM is motivated by the human visual system and consists of three analyzers: 1) sensory analyzers, 2) perceptual analyzers, and 3) conceptual analyzers. In addition, a memory, which is called the episodic memory, is incorporated, through which a number of features of the system, including hierarchical processing, configurability, adaptive response, and selective attention, are realized. A series of experimental results with both single and multiple critical motions are demonstrated and show the feasibility of the proposed system.

Original languageEnglish
Article number4773185
Pages (from-to)70-82
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Volume10
Issue number1
DOIs
Publication statusPublished - 2009 Feb 5

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Hierarchical systems
Data storage equipment
Accidents
Processing

Keywords

  • Assembly of adaptive-resonance-theory (ART) neural networks
  • Driver-assistance system (DAS)
  • Dynamic visual model (DVM)
  • Fuzzy integral
  • Spatiotemporal attention (STA) neural network

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Cite this

Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system. / Cherng, Shen; Fang, Chiung Yao; Chen, Chia Pei; Chen, Sei Wang.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 10, No. 1, 4773185, 05.02.2009, p. 70-82.

Research output: Contribution to journalArticle

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