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Different kinds of warning algorithms were used in the Frontal Collision Warning Systems (FCWS) for threat assessment, which were based on different kinds of kinematics measures of vehicles. In this paper, we discuss the required deceleration warning algorithm in two different situations, and consider a more complicated scenario. Based on field experiment data, we use an experienced driver's action...
It is well known that traffic systems are highly nonlinear. Previous research has shown the existence of the chaotic behavior in traffic flow data. However, the corresponding result in individual vehicle behavior has not been investigated. In this paper, we use tools of nonlinear time series analysis to explore the chaotic characteristic from data of individual vehicle behavior in car following. The...
Vehicle occlusion in congested ground traffic situations causes performance degradation in visual traffic surveillance systems. In this paper, we present a hidden Markov model (HMM) -based vehicle detection algorithm that is capable of handling vehicle occlusion and detecting vehicles from image sequences. In our algorithm, we first use principal component analysis (PCA) and multiple discriminant...
Vehicle detection and classification system is an important part of the intelligent transportation systems (ITS). Its function is to measure traffic parameters such as flow-rate, speed, and vehicle types, which are valuable information for applications of road surveillance, traffic signal control, road planning, and so on. This paper presents a novel low-cost vehicle detection and classification system...
Currently, the prevalent frontal collision warning systems (FCWS) are mainly based on quantitative ways. Their warning algorithms usually do prediction and assessment in the quantitative level which can not supply a universal quality under different traffic scenarios. The lack of cognition to surroundings may probably mislead the threat assessment. Besides, it is not the quantitative method but qualitative...
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