Lane-free traffic is a novel research domain, in which vehicles no longer adhere to the
notion of lanes, and consider the whole lateral space within the road boundaries. This …

This paper proposes a new reinforcement learning (RL) algorithm, called encoding
distributional soft actor-critic (E-DSAC), for decision-making in autonomous driving. Unlike …

Safe and efficient autonomous driving maneuvers in an interactive and complex
environment can be considerably challenging due to the unpredictable actions of other …

With the advent of universal function approximators in the domain of reinforcement learning,
the number of practical applications leveraging deep reinforcement learning (DRL) has …

The push towards sustainable transportation emphasizes vehicular energy efficiency in
mixed traffic scenarios. A research hotspot is the cooperative control of connected and …

Reinforcement Learning (RL) has made promising progress in planning and decision-
making for Autonomous Vehicles (AVs) in simple driving scenarios. However, existing RL …

Sensing and communication technologies have enhanced learning-based decision making
methodologies for multi-agent systems such as connected autonomous vehicles (CAV) …

The core technique of unmanned vehicle systems is the autonomous maneuvering decision,
which not only determines the applications of unmanned vehicles but also is the critical …

Compared to the modularized rule-based framework, end-to-end deep reinforcement
learning (DRL) algorithms have demonstrated greater adaptability in autonomous driving …

Despite significant progress in autonomous vehicles (AVs), the development of driving
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …