  Combining data-driven applications with control systems plays a key role in
recent Autonomous Car research. This thesis offers a structured review of the
latest literature on Deep Reinforcement Learning (DRL) within the realm of
autonomous vehicle Path Planning and Control. It collects a series of DRL
methodologies and algorithms and their applications in the field, focusing
notably on their roles in trajectory planning and dynamic control. In this
review, we delve into the application outcomes of DRL technologies in this
domain. By summarizing these literatures, we highlight potential challenges,
aiming to offer insights that might aid researchers engaged in related fields.
