Development of a real-time autonomous driving lateral control algorithm for an articulated bus using a model predictive control algorithm.
Saved in:
| Title: | Development of a real-time autonomous driving lateral control algorithm for an articulated bus using a model predictive control algorithm. |
|---|---|
| Authors: | Pyun, Beomjoon1,2 (AUTHOR), Choi, Hyungjeen2 (AUTHOR), Jung, Dohyun3 (AUTHOR) dhjung@kongju.ac.kr |
| Source: | Journal of Mechanical Science & Technology. Feb2024, Vol. 38 Issue 2, p901-914. 14p. |
| Subjects: | Articulated vehicles, Autonomous vehicles, Bus rapid transit, Prediction models, Algorithms |
| Abstract: | In this paper, an autonomous driving lateral control algorithm is introduced for bus rapid transit (BRT, with an articulated bus). The control algorithm includes an integration of model predictive control (MPC) for lateral control and PID control algorithms for longitudinal control considering the vehicle properties. To verify the algorithm in real-time, a model-in-the-loop system was developed using TruckSim in NI Veristand and Matlab/Simulink in Micro-AutoBox. In TruckSim, a BRT plant model was verified using vehicle tests and then applied. In addition, GPS data from a BRT route were acquired from the vehicle test and applied to a TruckSim scenario. In the Matlab/Simulink, the autonomous driving lateral control algorithm for MPC was developed. To communicate between the plant model and the control algorithm in real-time, a controller area network (CAN) protocol was defined and applied like a real vehicle. Therefore, a real-time verification environment was prepared to test the real-time autonomous driving lateral control algorithm for BRT. By using the verification environment, the control algorithm was verified using the ISO-11270 standard. Although many efforts have been made to develop an autonomous driving control algorithm, autonomous driving of BRT is expected more tangible because the BRT has its own lane and speed limitation. Therefore, this paper will introduce about study of real time feasibility, adaptation of vehicle variables, and precise prediction model for MPC algorithm. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Mechanical Science & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 175528429 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Development of a real-time autonomous driving lateral control algorithm for an articulated bus using a model predictive control algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pyun%2C+Beomjoon%22">Pyun, Beomjoon</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Choi%2C+Hyungjeen%22">Choi, Hyungjeen</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jung%2C+Dohyun%22">Jung, Dohyun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> dhjung@kongju.ac.kr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Mechanical+Science+%26+Technology%22">Journal of Mechanical Science & Technology</searchLink>. Feb2024, Vol. 38 Issue 2, p901-914. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Articulated+vehicles%22">Articulated vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Bus+rapid+transit%22">Bus rapid transit</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, an autonomous driving lateral control algorithm is introduced for bus rapid transit (BRT, with an articulated bus). The control algorithm includes an integration of model predictive control (MPC) for lateral control and PID control algorithms for longitudinal control considering the vehicle properties. To verify the algorithm in real-time, a model-in-the-loop system was developed using TruckSim in NI Veristand and Matlab/Simulink in Micro-AutoBox. In TruckSim, a BRT plant model was verified using vehicle tests and then applied. In addition, GPS data from a BRT route were acquired from the vehicle test and applied to a TruckSim scenario. In the Matlab/Simulink, the autonomous driving lateral control algorithm for MPC was developed. To communicate between the plant model and the control algorithm in real-time, a controller area network (CAN) protocol was defined and applied like a real vehicle. Therefore, a real-time verification environment was prepared to test the real-time autonomous driving lateral control algorithm for BRT. By using the verification environment, the control algorithm was verified using the ISO-11270 standard. Although many efforts have been made to develop an autonomous driving control algorithm, autonomous driving of BRT is expected more tangible because the BRT has its own lane and speed limitation. Therefore, this paper will introduce about study of real time feasibility, adaptation of vehicle variables, and precise prediction model for MPC algorithm. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Mechanical Science & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=175528429 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12206-024-0136-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 901 Subjects: – SubjectFull: Articulated vehicles Type: general – SubjectFull: Autonomous vehicles Type: general – SubjectFull: Bus rapid transit Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Development of a real-time autonomous driving lateral control algorithm for an articulated bus using a model predictive control algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pyun, Beomjoon – PersonEntity: Name: NameFull: Choi, Hyungjeen – PersonEntity: Name: NameFull: Jung, Dohyun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 1738494X Numbering: – Type: volume Value: 38 – Type: issue Value: 2 Titles: – TitleFull: Journal of Mechanical Science & Technology Type: main |
| ResultId | 1 |
