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In this study, a decision-making and motion planning controller with continuous action space is constructed in the highway driving scenario based on deep reinforcement learning. In the decision-making and planning problem, the goal is to achieve the safety, efficiency, and comfort of automated vehicles. In the driving scenario, the surrounding vehicles are controlled by the intelligent driver model and a general model (minimizing overall braking induced by lane change, MOBIL), which enables them to react to the environment and mimic the vehicle interactions on the highway. Given the uncertainties in the driving conditions, a specific deep reinforcement learning technique, called soft actor-critic, is used to solve the decision-making and planning problem with continuous action space. Simulation results show that the proposed method can solve the decision-making and motion planning problem in the interactive traffic environment to carry out safe lane-change maneuvers and cruise at high speed. In addition, two control policies are developed with different weights on safety, efficiency, and comfort. [ABSTRACT FROM AUTHOR]

Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning (RL) framework to transform the driving tasks in the intersection environments. The driving missions at the unsignalized intersection are cast into a left turn, right turn, and running straight for automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely. This objective promotes the studied vehicle to increase its speed and avoid crashing other vehicles. The decision-making policy learned from one driving task is transferred through three transfer rules in another driving mission and evaluated. Simulation results reveal that the decision-making strategies related to similar tasks are transferable and have a high success rate. It indicates that the presented control framework could reduce time consumption and realize online implementation. Therefore, the transfer RL concept is helpful for establishing the real-time decision-making policy for autonomous vehicles. [ABSTRACT FROM AUTHOR]