Autonomous vehicles have received considerable attention, yet high-speed path following
control remains a critical and challenging issue. At high speeds, achieving perfect control …

With the advent of machine learning, several autonomous driving tasks have become easier
to accomplish. Nonetheless, the proliferation of autonomous vehicles in urban traffic …

Multi-agent reinforcement learning (MARL) methods have emerged as a promising solution
for multi-agent collaborative driving in the intersection and roundabout scenarios. However …

Autonomous driving vehicles play a critical role in intelligent transportation systems and
have garnered considerable attention. Currently, the popular approach in autonomous …

We develop a deep reinforcement learning framework for tactical decision making in an
autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change …

Reinforcement learning has been shown to have great potential applications in autonomous
driving. For collaborative driving scenarios, multi-agent reinforcement learning can be used …

Decision intelligence based on reinforcement learning has gained considerable attention.
Compared to the rule-based approach, the reinforcement learning-based approach shows …

Deep reinforcement learning holds immense promise for applications in autonomous driving
tasks. For autonomous racing that needs to explore the physical limits of the vehicle in …

In this study, an improved reinforcement learning algorithm based on Soft Actor-Critic (SAC)
method is proposed, which is called Priority Multi-factor Weighted Experience Replay-SAC …