Prediction of Risk Generated by Different Driving Patterns and Their Conflict Redistribution

Gunduz G., Yaman C., Peker A. U., ACARMAN T.

IEEE Transactions on Intelligent Vehicles, cilt.3, sa.1, ss.71-80, 2018 (Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 3 Sayı: 1
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1109/tiv.2017.2788203
  • Dergi Adı: IEEE Transactions on Intelligent Vehicles
  • Derginin Tarandığı İndeksler: Scopus
  • Sayfa Sayıları: ss.71-80
  • Anahtar Kelimeler: Bayes methods, estimation, measurement uncertainty, probability density function, risk analysis, vehicles
  • Galatasaray Üniversitesi Adresli: Evet

Özet

In this paper, risk level correlation and classification using belief functions based on driving activities' data about sharp transient maneuvering tasks, legal speed exceeding, and average speed ensuing with the human being who is controlling the technical system, i.e., the car, is presented. A dataset is constituted by time stamped and geographically referenced driving maneuver information, which is exceptionally reported when an acceleration exceeds the given threshold in both longitudinal and lateral direction. Risk level is labeled in terms of the change in total vehicle collision property damage cost for the analyzed time period, and long term driving activities' magnitude and frequency is divided into two equal time periods, which are used to predict risk levels. Redistribution of conflicts generated by driving activities is used to predict the future risk level of involvement in an accident. Using fuzzy approach a microaveraged F-measure 90.98% is achieved by Dubois-Parade and PCR6 conflict redistribution methods.