13 citations · 24 across the 3 of their papers we have counts for
3 papers
cs.RO2020★ 6 cited
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space
Majid Moghadam, Ali Alizadeh, Engin Tekin +1
Tactical decision making and strategic motion planning for autonomous highway driving are challenging due to the complication of predicting other road users' behaviors, diversity o…
cs.RO2020★ 13 cited
Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation
Yunus Bicer, Ali Alizadeh, Nazim Kemal Ure +2
The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extrac…
cs.RO2019★ 5 cited
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment
Ali Alizadeh, Majid Moghadam, Yunus Bicer +3
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 tra…