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Prediction of Driving Actions from Driving Signals

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Abstract

A spoken dialogue system for car-navigation systems may be able to provide more natural and smoother communications but it must also cause safety problems. One of these problems is distraction whereby machine operation and voice conversations influence the driver. Even the use of a simple speech interface may affect the driving operation. We consider that a spoken dialogue system which can understand the driver's situation and change its dialogue rhythm according to that situation would be safe as part of a car-navigation system. For this to be possible, the system needs to predict and recognize driver's actions from environmental information such as driving signals. In this chapter, we report the results of an experiment on predicting driver actions. The action prediction system uses HMM-based pattern recognition only on driving signals and does not use position information. Its best driving action prediction accuracy was 0.632.

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Correspondence to Toshihiko Itoh .

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© 2009 Springer Science+Business Media, LLC

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Itoh, T., Yamada, S., Yamamoto, K., Araki, K. (2009). Prediction of Driving Actions from Driving Signals. In: Takeda, K., Erdogan, H., Hansen, J.H.L., Abut, H. (eds) In-Vehicle Corpus and Signal Processing for Driver Behavior. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79582-9_16

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  • DOI: https://doi.org/10.1007/978-0-387-79582-9_16

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-79581-2

  • Online ISBN: 978-0-387-79582-9

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