Abstract
Smart cars are promising application domain for ubiquitous computing. Context recognition is important support for a smart car to avoid accidents proactively. Despite many techniques have been developed, we find a lack of complex situation recognition in the smart car environment. This paper presents a novel context recognition approach that is composed of two parts: offline statistic-based situation pattern training and online situation recognition. The training phase is done to learn the statistical relationship between simple context atoms and complex context situations and hence generate the pattern of every single situation. The online recognition phase will recognize the current situation according to its pattern in the running time of a smart car. The implementation of the software and prototype is given to provide the running environment for the approach. Performance evaluation shows that our approach is effective and applicable in a smart car.
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Sun, J., He, K. (2009). Statistic-Based Context Recognition in Smart Car. In: Barnaghi, P., Moessner, K., Presser, M., Meissner, S. (eds) Smart Sensing and Context. EuroSSC 2009. Lecture Notes in Computer Science, vol 5741. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04471-7_16
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DOI: https://doi.org/10.1007/978-3-642-04471-7_16
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