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

26 Nov 2020 • Majid Moghadam • Ali Alizadeh • Engin Tekin • Gabriel Hugh Elkaim

Tactical decision making and strategic motion planning for autonomous highway driving are challenging due to the complication of predicting other road users' behaviors, diversity of environments, and complexity of the traffic interactions. This paper presents a novel end-to-end continuous deep reinforcement learning approach towards autonomous cars' decision-making and motion planning... (read more)

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