Motion Planning is a fundamental component of a mobile robot to reach its goal safely
avoiding collision. For a self-driving car on a highway, the presence of non-communicating …

Deep reinforcement learning (DRL) allows a system to interact with its environment and take
actions by training an efficient policy that maximizes self-defined rewards. In autonomous …

The trajectory planning of autonomous vehicles requires making safe sequential decisions
instantaneously. The most significant challenge is the uncertainties brought by complex …

Adversary scenarios in driving, where the other vehicles may make mistakes or have a
competing or malicious intent, have to be studied not only for our safety but also for …

The use of deep reinforcement learning (DRL) for autonomous vehicles is a hot topic in the
autonomous driving industry. Many autonomous vehicular systems rely in part or entirely on …

Highway scenarios are highly dynamic environments where several vehicles interact
following their own goal, leading to different combinations of scenes that also change over …

Reinforcement learning has received high research interest for developing planning
approaches in automated driving. Most prior works consider the end-to-end planning task …

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

Tactical decision making and strategic motion planning for autonomous highway driving are
challenging due to the complication of predicting other road users' behaviors, diversity of …

This study introduces a novel approach to autonomous motion planning, informing an
analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate …