Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment

Alizadeh A., Moghadam M., Bicer Y., Üre N. K., Yavas U., Kurtulus C.

IEEE Intelligent Transportation Systems Conference (IEEE-ITSC), Auckland, Yeni Zelanda, 27 - 30 Ekim 2019, ss.1399-1404 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/itsc.2019.8917192
  • Basıldığı Şehir: Auckland
  • Basıldığı Ülke: Yeni Zelanda
  • Sayfa Sayıları: ss.1399-1404
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.