To address the shortcomings of previous autonomous decision models, which often
overlook the personalized features of users, this paper proposes a personalized decision …

Due to the complexity of the driving environment and the dynamics of the behavior of traffic
participants, self-driving in dense traffic flow is very challenging. Traditional methods usually …

Autonomous vehicle (AV) uses the artificial intelligence (AI) technologies to control the
vehicle without human intervention. The implementation of AV has the advantages over the …

Most studies developing car-following controllers for AVs in mixed traffic primarily focus on
maximizing the utility of the AVs. However, the utility of the entire mixed traffic flow is largely …

This paper proposes an end-to-end method based on deep reinforcement learning (DRL) for
autonomous driving (AD) in diverse road conditions. The proposed method is developed …

Reinforcement learning (RL) shows promise for autonomous driving decision-making.
However, designing appropriate reward functions to guide RL agents towards complex …

Reinforcement learning (RL) is an effective approach to motion planning in autonomous
driving, where an optimal driving policy can be automatically learned using the interaction …

With the fast development of driving automation technologies, user psychological
acceptance of driving automation has become one of the major obstacles to the adoption of …

Autonomous driving with artificial intelligence technology has been viewed as promising for
autonomous vehicles hitting the road in the near future. In recent years, considerable …

Deep reinforcement learning (DRL) is a machine learning method based on rewards, which
can be extended to solve some complex and realistic decision-making problems …