Autonomous driving (AD) endows vehicles with the capability to drive partly or entirely
without human intervention. AD agents generate driving policies based on online perception …

In multi-agent reinforcement learning (MARL), agents must learn to cooperate by observing
the environment and selecting actions that maximize their rewards. However, this learning …

One of the key factors determining whether autonomous vehicles (AVs) can be seamlessly
integrated into existing traffic systems is their ability to interact smoothly and efficiently with …

Abstract Vehicle‐to‐Everything (V2X) communication has been proposed as a potential
solution to improve the robustness and safety of autonomous vehicles by improving …

Investigating Vehicle-to-everything (V2X) communication, we dive into the concept of vehicle
platoons, a key innovation in transport systems, introducing a new era of cooperative driving …

In recent years, many researchers have paid great attention to the transportation
convenience and advantages brought by the future extensive use of connected and …

Human-level driving is the ultimate goal of autonomous driving. As the top-level decision-
making aspect of autonomous driving, behavior decision establishes short-term driving …

This paper presents an approach that improves the efficiency and generalization capabilities
of Reinforcement Learning-based autonomous vehicles operating in urban driving …

Coordination among connected and autonomous vehicles (CAVs) is advancing due to
developments in control and communication technologies. However, much of the current …

Autonomous vehicles with a self-evolving ability are expected to cope with unknown
scenarios in the real-world environment. Take advantage of trial and error mechanism …