Cargando…

Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System

Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers’ imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the developmen...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Jing, Zhao, Cong, Jiang, Shengchuan, Zhang, Xinyuan, Li, Zhongxin, Du, Yuchuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819844/
https://www.ncbi.nlm.nih.gov/pubmed/36613215
http://dx.doi.org/10.3390/ijerph20010893
_version_ 1784865327634448384
author Chen, Jing
Zhao, Cong
Jiang, Shengchuan
Zhang, Xinyuan
Li, Zhongxin
Du, Yuchuan
author_facet Chen, Jing
Zhao, Cong
Jiang, Shengchuan
Zhang, Xinyuan
Li, Zhongxin
Du, Yuchuan
author_sort Chen, Jing
collection PubMed
description Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers’ imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the development of the cooperative vehicle infrastructure system (CVIS), multi-source road and traffic information can be collected by onboard or roadside sensors and integrated into a cloud. The information is updated and used for decision-making in real-time. This study proposes an intelligent speed control approach for AVs in CVISs using deep reinforcement learning (DRL) to improve safety, efficiency, and ride comfort. First, the irregular and fluctuating road profiles of rough pavements are represented by maximum comfortable speeds on segments via vertical comfort evaluation. A DRL-based speed control model is then designed to learn safe, efficient, and comfortable car-following behavior based on road and traffic information. Specifically, the model is trained and tested in a stochastic environment using data sampled from 1341 car-following events collected in California and 110 rough pavements detected in Shanghai. The experimental results show that the DRL-based speed control model can improve computational efficiency, driving efficiency, longitudinal comfort, and vertical comfort in cars by 93.47%, 26.99%, 58.33%, and 6.05%, respectively, compared to a model predictive control-based adaptive cruise control. The results indicate that the proposed intelligent speed control approach for AVs is effective on rough pavements and has excellent potential for practical application.
format Online
Article
Text
id pubmed-9819844
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-98198442023-01-07 Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System Chen, Jing Zhao, Cong Jiang, Shengchuan Zhang, Xinyuan Li, Zhongxin Du, Yuchuan Int J Environ Res Public Health Article Traffic crashes, heavy congestion, and discomfort often occur on rough pavements due to human drivers’ imperfect decision-making for vehicle control. Autonomous vehicles (AVs) will flood onto urban roads to replace human drivers and improve driving performance in the near future. With the development of the cooperative vehicle infrastructure system (CVIS), multi-source road and traffic information can be collected by onboard or roadside sensors and integrated into a cloud. The information is updated and used for decision-making in real-time. This study proposes an intelligent speed control approach for AVs in CVISs using deep reinforcement learning (DRL) to improve safety, efficiency, and ride comfort. First, the irregular and fluctuating road profiles of rough pavements are represented by maximum comfortable speeds on segments via vertical comfort evaluation. A DRL-based speed control model is then designed to learn safe, efficient, and comfortable car-following behavior based on road and traffic information. Specifically, the model is trained and tested in a stochastic environment using data sampled from 1341 car-following events collected in California and 110 rough pavements detected in Shanghai. The experimental results show that the DRL-based speed control model can improve computational efficiency, driving efficiency, longitudinal comfort, and vertical comfort in cars by 93.47%, 26.99%, 58.33%, and 6.05%, respectively, compared to a model predictive control-based adaptive cruise control. The results indicate that the proposed intelligent speed control approach for AVs is effective on rough pavements and has excellent potential for practical application. MDPI 2023-01-03 /pmc/articles/PMC9819844/ /pubmed/36613215 http://dx.doi.org/10.3390/ijerph20010893 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Jing
Zhao, Cong
Jiang, Shengchuan
Zhang, Xinyuan
Li, Zhongxin
Du, Yuchuan
Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System
title Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System
title_full Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System
title_fullStr Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System
title_full_unstemmed Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System
title_short Safe, Efficient, and Comfortable Autonomous Driving Based on Cooperative Vehicle Infrastructure System
title_sort safe, efficient, and comfortable autonomous driving based on cooperative vehicle infrastructure system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819844/
https://www.ncbi.nlm.nih.gov/pubmed/36613215
http://dx.doi.org/10.3390/ijerph20010893
work_keys_str_mv AT chenjing safeefficientandcomfortableautonomousdrivingbasedoncooperativevehicleinfrastructuresystem
AT zhaocong safeefficientandcomfortableautonomousdrivingbasedoncooperativevehicleinfrastructuresystem
AT jiangshengchuan safeefficientandcomfortableautonomousdrivingbasedoncooperativevehicleinfrastructuresystem
AT zhangxinyuan safeefficientandcomfortableautonomousdrivingbasedoncooperativevehicleinfrastructuresystem
AT lizhongxin safeefficientandcomfortableautonomousdrivingbasedoncooperativevehicleinfrastructuresystem
AT duyuchuan safeefficientandcomfortableautonomousdrivingbasedoncooperativevehicleinfrastructuresystem