Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature
It provides a comprehensive overview for researchers and practitioners in autonomous vehicles, but is incremental as it synthesizes existing literature without introducing novel methods.
This review paper surveys state-of-the-art algorithms and approaches for path-planning in autonomous vehicles, covering planning, scheduling, learning, neural networks, graph neural networks, reinforcement learning, and temporal planning with uncertainty, without presenting new experimental results or specific numerical outcomes.
This short review aims to make the reader familiar with state-of-the-art works relating to planning, scheduling and learning. First, we study state-of-the-art planning algorithms. We give a brief introduction of neural networks. Then we explore in more detail graph neural networks, a recent variant of neural networks suited for processing graph-structured inputs. We describe briefly the concept of reinforcement learning algorithms and some approaches designed to date. Next, we study some successful approaches combining neural networks for path-planning. Lastly, we focus on temporal planning problems with uncertainty.