Reinforcement learning methods have shown the ability to solve challenging scenarios in
unmanned systems. However, solving long-time decision-making sequences in a highly …

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 …

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 …

Autonomous vehicles (AVs) have the potential to prevent accidents caused by drivers' error
and reduce road traffic risks. Due to the nature of heavy vehicles, whose collisions cause …

Reinforcement learning (RL) can be used to create a tactical decision-making agent for
autonomous driving. However, previous approaches only output decisions and do not …

Autonomous driving systems (ADSs) have the potential to revolutionize transportation by
improving traffic safety and efficiency. As the core component of ADSs, maneuver decision …

To improve the safety and efficiency of autonomous vehicles on the highway, a hierarchical
framework combining deep reinforcement learning and risk assessment is proposed in this …

Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore,
it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most …

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

Uncertain environment on multi-lane highway, eg, the stochastic lane-change maneuver of
surrounding vehicles, is a big challenge for achieving safe automated highway driving. To …