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. Index Terms-Assembly of adaptive-resonance-theory (ART) neural networks, driver-assistance system (DAS), dynamic visual model (DVM), fuzzy integral, spatiotemporal attention (STA) neural network.
Shen.et.al has proposed a dynamic visual model which can be used to detect critical motions of nearby vehicles while driving on a highway REF .
Critical Motion Detection of Nearby Moving Vehicles in a Vision-Based Driver-Assistance System
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Computer Science Engineering