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.