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Fuzzy Theory and Bayesian Update-Based Traffic Prediction and Optimal Path Planning for Car Navigation System using Historical Driving Information

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2009, 14(11), pp.159-167
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

정상준 1 허용관 1 조한무 1 Jong-jin Kim 1 최슬기 1

1LIG넥스원

Accredited

ABSTRACT

The vehicles play a significant role in modern people's life as economy grows. The development of car navigation system(CNS) provides various convenience because it shows the driver where they are and how to get to the destination from the point of source. However, the existing map-based CNS does not consider any environments such as traffic congestion. Given the same starting point and destination, the system always provides the same route and the required time. This paper proposes a path planning method with traffic prediction by applying historical driving information to the Fuzzy theory and Bayesian update. Fuzzy theory classifies the historical driving information into groups of leaving time and speed rate, and the traffic condition of each time zone is calculated by Bayesian update. An ellipse area including starting and destination points is restricted in order to reduce the calculation time. The accuracy and practicality of the proposed scheme are verified by several experiments and comparisons with real navigation.

Citation status

* References for papers published after 2022 are currently being built.