Browsing by Author "Li, Rih-Jin"

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A passing probability model for intelligent transportation system
(2007)
Cho, Hsun-Jung
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Li, Rih-Jin
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
Intelligent transportation system (ITS) uses electronic technologies such as information and communication technologies to improve the efficiency of transportation management. In the theoretical framework of gas-kinetic traffic flow model, the probability of capable passing is essential for ITS. The probability of capable passing is assumed be a linear function of the traffic concentration, indicating that the probability of capable passing decreases proportionally with an increasing concentration. This work improves the model by introducing the speed desired by the driver into the original two-lane cellular automata (CA) model. This study also examines the accuracy of the linearity assumption. A capable passing principle is established to simulate the capable passing probability for different concentrations and can be embedded in a system on chip of ITS.
Method for identification of traffic lane boundary
(2010-06-24)
Jou, Yow-Jen
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Cho, Hsun-Jung
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Chen, Yu-Kuang
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Huang, Heng
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Hsu, Chia-Chun
The invention provides a method for identification of traffic lane boundary. Firstly the microwave signal is received, and the noise reduction is treated for the microwave signal. Then the frequency domain information is employed to calculate the legal set of closed interval, in order to form the frequency span information. Finally, the probability density function model is employed to calculate the frequency span information in order to identify the traffic lane boundary.
Vehicle Classification using Support Vector Machines and k-means Clustering
(2009)
Cho, Hsun-Jung
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Li, Rih-Jin
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Lee, Hsia
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Wu, Jennifer Yuh-Jen
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運輸與物流管理系註：原交通所+運管所
This investigation involves the combination of support vector machines (SVM) and k-means clustering implemented on radar signal. SVM classifier based on k-means algorithm has an advance for classifying unlabeled data. To classify the vehicle types automatically, the combined method is implemented with radar signals. This paper proposed a classifier training algorithm based on SVM and k-means clustering as follows (i) using the k-means algorithm to label the input feature data extracted from radar signal into two subsets, (ii) train SVM with labeled data, and (iii) classify unidentified radar signal into large vehicle or small vehicle with the trained classifier. Training features of radar signal includes (i) signal volume and (ii) sum of signal variations, both in frequency domain. These features are taken as system input, while vehicle types as system output. The proposed algorithm is implemented and demonstrated with real FMCW radar signals. With the numerical experiment, satisfying result is obtained.