Decision-making is critical for lane change in autonomous driving. Reinforcement learning
(RL) algorithms aim to identify the values of behaviors in various situations and thus they …

Mandatory lane-change scenarios are often challenging for autonomous vehicles in
complex environments. In this paper, a human-knowledge-enhanced reinforcement learning …

Lane-change decision-making for vehicles is a challenging task for many reasons, including
traffic rules, safety, and the stochastic nature of driving. Because of its success in solving …

The field of autonomous driving has seen increasing proposed use of machine learning
methodologies. However, there are still challenges in applying such methods since …

Decision making for lane change is an important challenge for automated vehicles,
especially in complex traffic environments. In recent years, there have been studies that …

Machine learning approaches often lack safety guarantees, which are often a key
requirement in real-world tasks. This paper addresses the lack of safety guarantees by …

Performing safe and efficient lane changes is a crucial feature for creating fully autonomous
vehicles. Recent advances have demonstrated successful lane following behavior using …

Deep reinforcement learning (DRL), combining the perception capability of deep learning
(DL) and the decision-making capability of reinforcement learning (RL)[1], has been widely …

Robust lane-changing behavior planning is critical to ensuring the safety and comfort of
autonomous vehicles. In this paper, we proposed an efficient and robust vehicle lane …

Although deep reinforcement learning (DRL) methods are promising for making behavioral
decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to …