In the control of the self-driving vehicles, PID controllers are widely used due to their simple
structure and good stability. However, in complex self-driving scenarios such as curvature …

Unpredictable situations frequently occur in real driving environments, and it is often difficult
to recognize road signs. In this case, autonomous vehicles (AVs) have a limited ability to …

Purpose This paper aims to develop a robust navigation enhancement framework to handle
one of the most urgent needs for real applications of autonomous vehicles nowadays, as …

The contradiction between increasing traffic and the relatively poor roundabout infrastructure
is getting stronger. The control and optimization of the macroscopic traffic flow needs to be …

In recent years, deep reinforcement learning has attracted significant attention from both
industry and academia and is used in solving various complex problems. In this paper, an …

Deep reinforcement learning (DRL) has become a powerful method for autonomous driving
while often lacking safety guarantees. In this paper, we propose a Risk-fused Constraint …

Reinforcement Learning (RL) algorithms help in training an autonomous agent to learn the
optimal actions in an unknown environment. RL, in conjunction with neural networks, known …

Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient
driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts …

In autonomous driving, the hybrid strategy of deep reinforcement learning and cooperative
adaptive cruise control (CACC) can fully utilize the advantages of the two algorithms and …

Self-reflecting about our performance (eg, how confident we are) before doing a task is
essential for decision making, such as selecting the most suitable tool or choosing the best …