In this paper, we propose a method for training a supervisor that selects proper DAS by deep reinforcement learning. The driving policy operates based on camera�...

Abstract—With the commercialization of various Driver. Assistance Systems (DAS), those vehicles have some autonomous functions like Smart Cruise Control�...

In this paper, we propose a supervisor agent, which can enhance the driver assistant systems by using deep distributional reinforcement learning. The supervisor�...

Using Deep Q- Networks to predict target points for trajectory planning can serve as a new method to deal with the challenges in decision making and planning�...

These agents, trained with deep reinforcement learning (DRL), decide their motion by taking high-level decisions, such as “keep lane”, “overtake” and “go to�...

Nov 7, 2019 � This work proposes the development of a driving policy based on reinforcement learning. In this way, the proposed driving policy makes minimal or no�...

Mar 15, 2024 � This study proposes a deep reinforcement learning-based lane-changing model to train autonomous vehicles to complete lane-changing in the interaction with�...

This thesis offers a structured review of the latest literature on Deep Reinforcement Learning (DRL) within the realm of autonomous vehicle Path Planning and�...

Human-level Control Through Deep Reinforcement Learning � Deep Reinforcement Learning with Double Q-Learning � Prioritized Experience Replay � Dueling Network�...

This study formulates a lane-changing model where the traffic lanes are discretized into cells, considers both mandatory lane-changing and discretionary lane-�...