Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey

Fuente: arXiv
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Auteurs principaux: Chen, Yiyang, Ji, Chao, Cai, Yunrui, Yan, Tong, Su, Bo
Format: Preprint
Publié: 2024
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author Chen, Yiyang
Ji, Chao
Cai, Yunrui
Yan, Tong
Su, Bo
author_facet Chen, Yiyang
Ji, Chao
Cai, Yunrui
Yan, Tong
Su, Bo
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2404_00340
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
Chen, Yiyang
Ji, Chao
Cai, Yunrui
Yan, Tong
Su, Bo
Robotics
Systems and Control
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.
title Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
topic Robotics
Systems and Control
url https://arxiv.org/abs/2404.00340