Reinforcement learning (RL) methods have gained popularity in the field of motion planning
for autonomous vehicles due to their success in robotics and computer games. However, no …

In this survey, we systematically summarize the current literature on studies that apply
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …

Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …

For autonomous vehicles integrating onto road-ways with human traffic participants, it
requires understanding and adapting to the participants' intention by responding in …

Deep reinforcement learning (RL) has been widely applied to motion planning problems of
autonomous vehicles in urban traffic. However, traditional deep RL algorithms cannot …

Autonomous vehicles need to handle various traffic conditions and make safe and efficient
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …

Behavioral decision making is an important aspect of autonomous vehicles (AV). In this
work, we propose a behavior planning structure based on hierarchical reinforcement …

Motion planning under uncertainty is one of the main challenges in developing autonomous
driving vehicles. In this work, we focus on the uncertainty in sensing and perception, resulted …

Many Reinforcement Learning (RL) approaches use joint control signals (positions,
velocities, torques) as action space for continuous control tasks. We propose to lift the action …

The uncertainty and complexity of autonomous driving make Deep Reinforcement Learning
(DRL) appealing. DRL can optimize the expected reward by interacting with environments …