Multi-agent reinforcement learning (MARL) provides an efficient way for
...
This paper proposes a discretionary lane selection algorithm. In particu...
Maneuvering in dense traffic is a challenging task for autonomous vehicl...
In a multi-agent setting, the optimal policy of a single agent is largel...
There have been numerous advances in reinforcement learning, but the
typ...
Traditional planning and control methods could fail to find a feasible
t...
This paper presents a real-time lane change control framework of autonom...
Decision making in dense traffic can be challenging for autonomous vehic...
Estimating statistical uncertainties allows autonomous agents to communi...
Navigating urban environments represents a complex task for automated
ve...
Designing reliable decision strategies for autonomous urban driving is
c...
In order to drive safely and efficiently under merging scenarios, autono...
We propose CM3, a new deep reinforcement learning method for cooperative...
Driving is a social activity: drivers often indicate their intent to cha...
This paper presents a learning from demonstration approach to programmin...
Decomposition methods have been proposed in the past to approximate solu...
We analyze how the knowledge to autonomously handle one type of intersec...
Providing an efficient strategy to navigate safely through unsignaled
in...