Trajectory planning and control of autonomous vehicle based on reinforcement learning algorithm

In order to explore the trajectory planning and control method of autonomous vehicle based on reinforcement learning algorithm, this paper designs an advanced control system by combining the basic characteristics of autonomous vehicle with the advantages of reinforcement learning. The system can int...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Procedia computer science Ročník 262; s. 1166 - 1172
Hlavný autor: Wang, Jia
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 2025
Predmet:
ISSN:1877-0509, 1877-0509
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Abstract In order to explore the trajectory planning and control method of autonomous vehicle based on reinforcement learning algorithm, this paper designs an advanced control system by combining the basic characteristics of autonomous vehicle with the advantages of reinforcement learning. The system can intelligently plan the optimal driving trajectory according to the real-time road information, traffic conditions and the vehicle’s own state, and realize the smooth and safe driving of the vehicle through the precise control algorithm. Experiments have proved that the system can perform well in a variety of complex road conditions, effectively improving the driving efficiency and safety of autonomous vehicles. Compared with traditional trajectory planning methods, the system has stronger adaptive ability and can cope with unexpected situations and uncertainties better.
AbstractList In order to explore the trajectory planning and control method of autonomous vehicle based on reinforcement learning algorithm, this paper designs an advanced control system by combining the basic characteristics of autonomous vehicle with the advantages of reinforcement learning. The system can intelligently plan the optimal driving trajectory according to the real-time road information, traffic conditions and the vehicle’s own state, and realize the smooth and safe driving of the vehicle through the precise control algorithm. Experiments have proved that the system can perform well in a variety of complex road conditions, effectively improving the driving efficiency and safety of autonomous vehicles. Compared with traditional trajectory planning methods, the system has stronger adaptive ability and can cope with unexpected situations and uncertainties better.
Author Wang, Jia
Author_xml – sequence: 1
  givenname: Jia
  surname: Wang
  fullname: Wang, Jia
  email: 15294120786@163.com
  organization: Lanzhou Institute of Technology, Lanzhou 730050, China
BookMark eNp9kM1uwjAQhK2KSqWUJ-jFL0DqH9lODj1UqD9ISL3Qs7U4G3CU2MgOSLx9Q-mhp-5hdy4zO_ruySTEgIQ8clZwxvVTWxxSdLkQTKiCqYIrfUOmvDRmwRSrJn_0HZnn3LJxZFlW3EyJ3SRo0Q0xnemhgxB82FEINXUxDCl2NDYUjkMMsY_HTE-4965DuoWMNY2BJvShiclhj2GgHUK6JnS7mPyw7x_IbQNdxvnvnZGvt9fN8mOx_nxfLV_WCyfGvuNGB65ixoAWNTDgJW8YirrRSoPeqgqU2SrkKIRhWjgpuXYSoZICtCvljMhrrksx54SNPSTfQzpbzuwFk23tDyZ7wWSZsuPb0fV8deFY7eQx2ew8Boe1TyMUW0f_r_8bACB12A
ContentType Journal Article
Copyright 2025
Copyright_xml – notice: 2025
DBID 6I.
AAFTH
AAYXX
CITATION
DOI 10.1016/j.procs.2025.05.156
DatabaseName ScienceDirect Open Access Titles
Elsevier:ScienceDirect:Open Access
CrossRef
DatabaseTitle CrossRef
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISSN 1877-0509
EndPage 1172
ExternalDocumentID 10_1016_j_procs_2025_05_156
S1877050925020034
GroupedDBID --K
0R~
1B1
457
5VS
6I.
71M
AAEDT
AAEDW
AAFTH
AAIKJ
AALRI
AAQFI
AAXUO
AAYWO
ABMAC
ABWVN
ACGFS
ACRPL
ACVFH
ADBBV
ADCNI
ADEZE
ADNMO
ADVLN
AEUPX
AEXQZ
AFPUW
AFTJW
AGHFR
AIGII
AITUG
AKBMS
AKRWK
AKYEP
ALMA_UNASSIGNED_HOLDINGS
AMRAJ
E3Z
EBS
EJD
EP3
FDB
FNPLU
HZ~
IXB
KQ8
M41
M~E
O-L
O9-
OK1
P2P
ROL
SES
SSZ
~HD
9DU
AAYXX
CITATION
ID FETCH-LOGICAL-c2156-c2ecac9077a62da0a181f0e2df656a6b59a57b5e1e227062c3316c3ea932a6c83
ISSN 1877-0509
IngestDate Sat Nov 29 07:30:19 EST 2025
Sun Oct 19 01:39:02 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Keywords DWA algorithm
Vehicle trajectory planning
Autonomous driving
Reinforcement learning algorithm
Language English
License This is an open access article under the CC BY-NC-ND license.
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-c2156-c2ecac9077a62da0a181f0e2df656a6b59a57b5e1e227062c3316c3ea932a6c83
OpenAccessLink https://dx.doi.org/10.1016/j.procs.2025.05.156
PageCount 7
ParticipantIDs crossref_primary_10_1016_j_procs_2025_05_156
elsevier_sciencedirect_doi_10_1016_j_procs_2025_05_156
PublicationCentury 2000
PublicationDate 2025
2025-00-00
PublicationDateYYYYMMDD 2025-01-01
PublicationDate_xml – year: 2025
  text: 2025
PublicationDecade 2020
PublicationTitle Procedia computer science
PublicationYear 2025
Publisher Elsevier B.V
Publisher_xml – name: Elsevier B.V
References Bo, Lei, Jie (bib9) 2023; 37
Ban-qiang, Chagen, Zhou-Ping (bib2) 2023; 11
Yue, Pengpeng, Ruyu (bib8) 2023; 41
Jianming, Fengsheng, Di (bib1) 2023; 44
Jingda, Zhiyu, Zhongxu (bib11) 2023; 2
Jianjun, Xiaoyi, Xiaoqiang (bib3) 2024; 68
Xinyang, Zhenbo, Weiqing (bib10) 2024; 41
Ning Qiang, Liu Yuansheng, Xie Longyang. Automatic vehicle control method based on SAC application. Computer engineering and application, 2023, 59 (8): 9.
Shi Gaosong, Zhao Qinghai, Dong Xin, et al. Autopilot based on PPO algorithm man-machine interactive reinforcement learning method. Computer application research, 2024 (9):25-29.
Bingli, Yafei (bib7) 2023; 46
Yanliang, Baorong, Yuan (bib6) 2024; 37
Ban-qiang (10.1016/j.procs.2025.05.156_bib2) 2023; 11
Yanliang (10.1016/j.procs.2025.05.156_bib6) 2024; 37
Jianjun (10.1016/j.procs.2025.05.156_bib3) 2024; 68
Jingda (10.1016/j.procs.2025.05.156_bib11) 2023; 2
Xinyang (10.1016/j.procs.2025.05.156_bib10) 2024; 41
10.1016/j.procs.2025.05.156_bib5
Jianming (10.1016/j.procs.2025.05.156_bib1) 2023; 44
10.1016/j.procs.2025.05.156_bib4
Bingli (10.1016/j.procs.2025.05.156_bib7) 2023; 46
Yue (10.1016/j.procs.2025.05.156_bib8) 2023; 41
Bo (10.1016/j.procs.2025.05.156_bib9) 2023; 37
References_xml – volume: 44
  start-page: 53
  year: 2023
  end-page: 61
  ident: bib1
  article-title: Automatic driving trajectory prediction based on reinforcement learning based on directed graph
  publication-title: Journal of Zhengzhou University: Engineering Edition
– volume: 11
  start-page: 1
  year: 2023
  end-page: 4
  ident: bib2
  article-title: Adaptive planning and Control Method of Autonomous Vehicle based on Reinforcement Learning
  publication-title: Volkswagen
– volume: 37
  start-page: 1
  year: 2023
  end-page: 10
  ident: bib9
  article-title: Research on autonomous driving motion planning based on reinforcement learning under uncertain environment
  publication-title: Journal of Chongqing University of Technology (Natural Science)
– volume: 41
  start-page: 172
  year: 2024
  end-page: 178
  ident: bib10
  article-title: End-to-end automatic driving based on LSTM deep reinforcement Learning
  publication-title: Computer Simulation
– volume: 2
  start-page: 75
  year: 2023
  end-page: 91
  ident: bib11
  article-title: Human-in-loop deep reinforcement Learning algorithm and its Application to intelligent Decision making in autonomous driving
  publication-title: Engineering (English)
– reference: Shi Gaosong, Zhao Qinghai, Dong Xin, et al. Autopilot based on PPO algorithm man-machine interactive reinforcement learning method. Computer application research, 2024 (9):25-29.
– volume: 46
  start-page: 865
  year: 2023
  end-page: 872
  ident: bib7
  article-title: Horizontal control for trajectory tracking based on deep reinforcement learning
  publication-title: Journal of Hefei University of Technology: Natural Science Edition
– volume: 41
  start-page: 67
  year: 2023
  end-page: 75
  ident: bib8
  article-title: Following behavior modeling of autonomous vehicle based on deep reinforcement learning
  publication-title: Traffic Information and Safety
– reference: Ning Qiang, Liu Yuansheng, Xie Longyang. Automatic vehicle control method based on SAC application. Computer engineering and application, 2023, 59 (8): 9.
– volume: 37
  start-page: 24
  year: 2024
  end-page: 26
  ident: bib6
  article-title: RMB based on reinforcement learning algorithm of automatic driving study
  publication-title: Industrial control computer
– volume: 68
  start-page: 8
  year: 2024
  end-page: 14
  ident: bib3
  article-title: Research on energy saving control strategy of urban rail trains based on Sarsa algorithm
  publication-title: Railway Standard Design
– volume: 68
  start-page: 8
  issue: 8
  year: 2024
  ident: 10.1016/j.procs.2025.05.156_bib3
  article-title: Research on energy saving control strategy of urban rail trains based on Sarsa algorithm
  publication-title: Railway Standard Design
– volume: 46
  start-page: 865
  issue: 7
  year: 2023
  ident: 10.1016/j.procs.2025.05.156_bib7
  article-title: Horizontal control for trajectory tracking based on deep reinforcement learning
  publication-title: Journal of Hefei University of Technology: Natural Science Edition
– volume: 44
  start-page: 53
  issue: 5
  year: 2023
  ident: 10.1016/j.procs.2025.05.156_bib1
  article-title: Automatic driving trajectory prediction based on reinforcement learning based on directed graph
  publication-title: Journal of Zhengzhou University: Engineering Edition
– volume: 11
  start-page: 1
  year: 2023
  ident: 10.1016/j.procs.2025.05.156_bib2
  article-title: Adaptive planning and Control Method of Autonomous Vehicle based on Reinforcement Learning
  publication-title: Volkswagen
– volume: 37
  start-page: 24
  issue: 3
  year: 2024
  ident: 10.1016/j.procs.2025.05.156_bib6
  article-title: RMB based on reinforcement learning algorithm of automatic driving study
  publication-title: Industrial control computer
– volume: 41
  start-page: 172
  issue: 2
  year: 2024
  ident: 10.1016/j.procs.2025.05.156_bib10
  article-title: End-to-end automatic driving based on LSTM deep reinforcement Learning
  publication-title: Computer Simulation
– volume: 41
  start-page: 67
  issue: 2
  year: 2023
  ident: 10.1016/j.procs.2025.05.156_bib8
  article-title: Following behavior modeling of autonomous vehicle based on deep reinforcement learning
  publication-title: Traffic Information and Safety
– ident: 10.1016/j.procs.2025.05.156_bib4
– volume: 37
  start-page: 1
  issue: 11
  year: 2023
  ident: 10.1016/j.procs.2025.05.156_bib9
  article-title: Research on autonomous driving motion planning based on reinforcement learning under uncertain environment
  publication-title: Journal of Chongqing University of Technology (Natural Science)
– ident: 10.1016/j.procs.2025.05.156_bib5
– volume: 2
  start-page: 75
  year: 2023
  ident: 10.1016/j.procs.2025.05.156_bib11
  article-title: Human-in-loop deep reinforcement Learning algorithm and its Application to intelligent Decision making in autonomous driving
  publication-title: Engineering (English)
SSID ssj0000388917
Score 2.3425698
Snippet In order to explore the trajectory planning and control method of autonomous vehicle based on reinforcement learning algorithm, this paper designs an advanced...
SourceID crossref
elsevier
SourceType Index Database
Publisher
StartPage 1166
SubjectTerms Autonomous driving
DWA algorithm
Reinforcement learning algorithm
Vehicle trajectory planning
Title Trajectory planning and control of autonomous vehicle based on reinforcement learning algorithm
URI https://dx.doi.org/10.1016/j.procs.2025.05.156
Volume 262
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 1877-0509
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000388917
  issn: 1877-0509
  databaseCode: M~E
  dateStart: 20100101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1NT9wwELUK5cAF2kLFVysfuEEk29nYyREhEIcWVSqVuFmO48CuaHYVFgQXfjsztrO7dBGiSFysXUuxo3nWzHgyM4-QXcnKuoarVWKEzZOeLOsk57JMSi5kZdNKOuObuP5Qp6f5-XnxK7JtXns6AdU0-d1dMXpXqGEOwMbS2f-Ae7IoTMBvAB1GgB3G1wHfmoEPxd8jRbRnJIrFayEpHT_834yxlgGzX2_dJS6wh9aswi8HrfO9VK0PG3akErDC1cWw7Y8v_856s77KAA6YT0xHboi9aFCnYfqY8Ns3s-GFUIQcol1zFS9eQeZKJdgzJtiPZ-aiVhVRyQa9yLmUMzaW88DXM6e_QyhhgNbDYjN1kWFfVZ790y3b29_fuC9uC14cptj1FshHobICE_t-PkwjbdjvpvDUy5MX7dpP-US_ub2ed1Fm3I6zT2Ql3hfoQcD5M_ngmi9ktePioFE1rxE9hZ12sFOAnUbY6bCmU9hphJ162OmwoU9gpx3sdAL7OvlzfHR2eJJE8ozEghcnYXTW2IIpZaSoDDPgytXMiaoGD97IMitMpsrMcSeEYlLYNOXSps6AQ2-kzdOvZLEZNm6DUFZyEGHmhDVwHYcrbi83psfqNBXMpVxskv1OXnoUeqToLnlwoL14NYpXs0zDq20S2clUx1MZ3DcNp-ClB7fe-uA2WcZ_IXK2QxbH7Y37Rpbs7bh_3X73p-URML57DA
linkProvider ISSN International Centre
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Trajectory+planning+and+control+of+autonomous+vehicle+based+on+reinforcement+learning+algorithm&rft.jtitle=Procedia+computer+science&rft.au=Wang%2C+Jia&rft.date=2025&rft.pub=Elsevier+B.V&rft.issn=1877-0509&rft.eissn=1877-0509&rft.volume=262&rft.spage=1166&rft.epage=1172&rft_id=info:doi/10.1016%2Fj.procs.2025.05.156&rft.externalDocID=S1877050925020034
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1877-0509&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1877-0509&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1877-0509&client=summon