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

Ali Alizadeh, Majid Moghadam, Yunus Bicer, Nazim Kemal Ure, Ugur Yavas, Can Kurtulus
2019 arXiv   pre-print
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
more » ... significantly better in noisy environments compared to methods that rely solely on heuristics.
arXiv:1909.11538v1 fatcat:frgmz75pu5ah7ogqcbkt2kwuee