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, New Zealand, 27 - 30 October 2019, pp.1399-1404 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/itsc.2019.8917192
  • City: Auckland
  • Country: New Zealand
  • Page Numbers: pp.1399-1404
  • Istanbul Technical University Affiliated: Yes


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.