Abstract
It is important for a driver-assist system to know the phase of the driver, that is, safety or danger. This paper proposes two methods for estimating the driver’s phase by applying machine learning techniques to the sequences of brake signals. One method models the signal set with a mixture of Gaussians, where a Gaussian corresponds to a phase. The other method classifies a segment of the brake sequence to one of the hidden Markov models, each of which represents a phase. These methods are validated with experimental data, and are shown to be consistent with each other for the collected data from an unconstrained drive.
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Mima, H., Ikeda, K., Shibata, T., Fukaya, N., Hitomi, K., Bando, T. (2009). Estimation of Driving Phase by Modeling Brake Pressure Signals. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_53
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DOI: https://doi.org/10.1007/978-3-642-10677-4_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10676-7
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