Machine Learning for Car Navigation

Mitrovic, Dejan

doi:10.1007/3-540-45517-5_74

Dejan Mitrovic³

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2070))

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International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

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Abstract

In this paper we present the usage of neural networks and hidden markov models for learning driving patterns. We used neural networks for short-term prediction of lateral and longitudinal vehicle acceleration. For long- time prediction, hidden markov models provide recognition of individual driving events. The experiments performed showed that both techniques are very reliable. Recognition rate for driving events is above 98% and prediction error for events in the near future is very low. Predicted events will be used to support drivers in solving guidance navigation tasks.

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Authors and Affiliations

University of Canterbury, New Zealand
Dejan Mitrovic

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Dejan Mitrovic
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Hungarian Academy of Sciences, Intelligent Manufacturing and Business Processes Computer and Automation Research Institute, Kende utca 13-17, 1111, Budapest, Hungary
László Monostori & József Váncza &
Department of Computer Science 601 University Drive, Southwest Texas State University, San Marcos, TX, 78666-4616, USA
Moonis Ali

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Mitrovic, D. (2001). Machine Learning for Car Navigation. In: Monostori, L., Váncza, J., Ali, M. (eds) Engineering of Intelligent Systems. IEA/AIE 2001. Lecture Notes in Computer Science(), vol 2070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45517-5_74

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DOI: https://doi.org/10.1007/3-540-45517-5_74
Published: 18 June 2001
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42219-8
Online ISBN: 978-3-540-45517-2
eBook Packages: Springer Book Archive

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