Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment. - Ali Alizadeh; Majid Moghadam; Yunus Bicer; Nazim Kemal Ure; Muharrem Ugur Yavas; Can Kurtulus (ITSC, 2019)

Enhancing Situational Awareness and Performance of Adaptive Cruise Control through Model Predictive Control and Deep Reinforcement Learning. - N. Kemal Ure; Muharrem Ugur Yavas; Ali Alizadeh; Can Kurtulus (IV, 2019)

Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents. - Anil Öztürk; Mustafa Burak Gunel; Melih Dal; Muharrem Ugur Yavas; Nazim Kemal Ure (IV, 2020)

A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward. - Muharrem Ugur Yavas; Tufan Kumbasar; Nazim Kemal Ure (IV, 2020)

Model-Based Reinforcement Learning for Advanced Adaptive Cruise Control: A Hybrid Car Following Policy. - Muharrem Ugur Yavas; Tufan Kumbasar; Nazim Kemal Ure (IV, 2022)

Toward Learning Human-Like, Safe and Comfortable Car-Following Policies With a Novel Deep Reinforcement Learning Approach. - Muharrem Ugur Yavas; Tufan Kumbasar; Nazim Kemal Ure (IEEE Access, 2023)

A Real-World Reinforcement Learning Framework for Safe and Human-Like Tactical Decision-Making. - Muharrem Ugur Yavas; Tufan Kumbasar; Nazim Kemal Ure (IEEE Trans. Intell. Transp. Syst., 2023)