NEURAL NETWORK BASED VEHICULAR LOCATION PREDICTION MODEL FOR COOPERATIVE ACTIVE SAFETY SYSTEMS

Dorterler M., BAY Ö. F.

PROMET-TRAFFIC & TRANSPORTATION, cilt.30, sa.2, ss.205-215, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 30 Sayı: 2
  • Basım Tarihi: 2018
  • Doi Numarası: 10.7307/ptt.v30i2.2500
  • Dergi Adı: PROMET-TRAFFIC & TRANSPORTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.205-215
  • Anahtar Kelimeler: cooperative active safety systems, inter-vehicular communication, vehicular location prediction, artificial neural networks
  • Gazi Üniversitesi Adresli: Evet

Özet

Safety systems detect unsafe conditions and provide warnings for travellers to take action and avoid crashes. Estimation of the geographical location of a moving vehicle as to where it will be positioned next with high precision and short computation time is crucial for identifying dangers. To this end, navigational and dynamic data of a vehicle are processed in connection with the data received from neighbouring vehicles and infrastructure in the same vicinity. In this study, a vehicular location prediction model was developed using an artificial neural network for cooperative active safety systems. The model is intended to have a constant, shorter computation time as well as higher accuracy features. The performance of the proposed model was measured with a real-time testbed developed in this study. The results are compared with the performance of similar studies and the proposed model is shown to deliver a better performance than other models.