most citedAn End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space

6 citations · 7 across the 2 of their papers we have counts for

collaborators

3 papers

cs.RO2020★ 1 cited

An Autonomous Driving Framework for Long-term Decision-making and Short-term Trajectory Planning on Frenet Space

Majid Moghadam, Gabriel Hugh Elkaim

In this paper, we present a hierarchical framework for decision-making and planning on highway driving tasks. We utilized intelligent driving models (IDM and MOBIL) to generate lon…

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.RO2019

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…