Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
Robotics
2024-04-02 v1 Systems and Control
Systems and Control
Abstract
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.
Cite
@article{arxiv.2404.00340,
title = {Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey},
author = {Yiyang Chen and Chao Ji and Yunrui Cai and Tong Yan and Bo Su},
journal= {arXiv preprint arXiv:2404.00340},
year = {2024}
}