TY - JOUR
T1 - Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system
AU - Cherng, Shen
AU - Fang, Chiung Yao
AU - Chen, Chia Pei
AU - Chen, Sei-Wang
N1 - Funding Information:
Manuscript received September 29, 2007; revised February 5, 2008, May 29, 2008, and September 8, 2008. First published February 3, 2009; current version published February 27, 2009. This work was supported by the National Science Council, Republic of China, under Contract NSC-96-2221-E-003-010-MY3. The Associate Editor for this paper was S. Nedevschi.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Assembly of adaptive-resonance-theory (ART) neural networks
KW - Driver-assistance system (DAS)
KW - Dynamic visual model (DVM)
KW - Fuzzy integral
KW - Spatiotemporal attention (STA) neural network
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U2 - 10.1109/TITS.2008.2011694
DO - 10.1109/TITS.2008.2011694
M3 - Article
AN - SCOPUS:61849163364
VL - 10
SP - 70
EP - 82
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
SN - 1524-9050
IS - 1
M1 - 4773185
ER -