Dynamic parameters.
<div><p>The target-tracking accuracy of autonomous vehicles is closely related to that of onboard sensors. Methods such as image processing and base station positioning are susceptible to various types of interference in real-world scenarios, resulting in sensor data errors or even losse...
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2025
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| _version_ | 1852020262040502272 |
|---|---|
| author | Xiaosong Liu (66661) |
| author2 | Huanhai Zhu (21387558) Zebiao Shan (21387561) Qingsong Lu (21387564) Liben He (21387567) |
| author2_role | author author author author |
| author_facet | Xiaosong Liu (66661) Huanhai Zhu (21387558) Zebiao Shan (21387561) Qingsong Lu (21387564) Liben He (21387567) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xiaosong Liu (66661) Huanhai Zhu (21387558) Zebiao Shan (21387561) Qingsong Lu (21387564) Liben He (21387567) |
| dc.date.none.fl_str_mv | 2025-05-19T17:45:21Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0322648.t001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Dynamic_parameters_/29102466 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Pharmacology Biological Sciences not elsewhere classified Information Systems not elsewhere classified strategy nonlinearly adjusts extended state observer experimental results demonstrate desired heading angle base station positioning sensor data errors dynamic response capability complex road conditions precision target tracking cumulative errors generated tracking control method sight guidance strategy proposed method lowered autonomous vehicles based target path tracking target tracking accuracy real vehicle experiments cumulative errors sight guidance proposed method autonomous vehicles tracking error tracking accuracy path tests dynamic environments complex environments mpc method method incorporates autonomous vehicle xlink "> world scenarios within 0 wheel odometry various types unknown disturbances ultimately affect time observations tangent line switching mechanism study proposes significantly reducing significant improvement relies solely practical curved perform real onboard sensors odometry mechanism mathematical model image processing even losses |
| dc.title.none.fl_str_mv | Dynamic parameters. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>The target-tracking accuracy of autonomous vehicles is closely related to that of onboard sensors. Methods such as image processing and base station positioning are susceptible to various types of interference in real-world scenarios, resulting in sensor data errors or even losses that ultimately affect the tracking accuracy of autonomous vehicles. This study proposes a target-tracking control method that relies solely on wheel odometry to address this issue. This method incorporates an extended state observer to compensate for the cumulative errors generated by the odometry mechanism, effectively enhancing the robustness and accuracy of the system in complex environments. In addition, a hyperbolic-tangent line-of-sight guidance strategy based on a partition-switching mechanism is designed to improve the dynamic response capability of an autonomous vehicle. This strategy nonlinearly adjusts the tracking error to generate the desired heading angle and velocity, ensuring that the target path tracking is rapid and smooth. First, we establish a mathematical model of an autonomous vehicle and combine the hyperbolic-tangent line-of-sight guidance strategy with a noise-resistant active disturbance rejection controller to achieve high-precision target tracking in dynamic environments. Second, an extended state observer is employed to perform real-time observations and compensate for unknown disturbances during localization, significantly reducing the impact of cumulative errors. Finally, the effectiveness of the proposed method is validated using numerical simulations and real vehicle experiments. The experimental results demonstrate that, compared with the ET-Fuzzy-MPC method, the proposed method lowered the average position tracking error by 45.39% under complex road conditions. In practical curved-path tests, the vehicle's tracking error remained stable to within 0.192 m, representing a significant improvement in the target tracking accuracy and dynamic response performance.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_65392a4f59cd39b375ce631dbcb1b434 |
| identifier_str_mv | 10.1371/journal.pone.0322648.t001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29102466 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Dynamic parameters.Xiaosong Liu (66661)Huanhai Zhu (21387558)Zebiao Shan (21387561)Qingsong Lu (21387564)Liben He (21387567)PharmacologyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedstrategy nonlinearly adjustsextended state observerexperimental results demonstratedesired heading anglebase station positioningsensor data errorsdynamic response capabilitycomplex road conditionsprecision target trackingcumulative errors generatedtracking control methodsight guidance strategyproposed method loweredautonomous vehicles basedtarget path trackingtarget tracking accuracyreal vehicle experimentscumulative errorssight guidanceproposed methodautonomous vehiclestracking errortracking accuracypath testsdynamic environmentscomplex environmentsmpc methodmethod incorporatesautonomous vehiclexlink ">world scenarioswithin 0wheel odometryvarious typesunknown disturbancesultimately affecttime observationstangent lineswitching mechanismstudy proposessignificantly reducingsignificant improvementrelies solelypractical curvedperform realonboard sensorsodometry mechanismmathematical modelimage processingeven losses<div><p>The target-tracking accuracy of autonomous vehicles is closely related to that of onboard sensors. Methods such as image processing and base station positioning are susceptible to various types of interference in real-world scenarios, resulting in sensor data errors or even losses that ultimately affect the tracking accuracy of autonomous vehicles. This study proposes a target-tracking control method that relies solely on wheel odometry to address this issue. This method incorporates an extended state observer to compensate for the cumulative errors generated by the odometry mechanism, effectively enhancing the robustness and accuracy of the system in complex environments. In addition, a hyperbolic-tangent line-of-sight guidance strategy based on a partition-switching mechanism is designed to improve the dynamic response capability of an autonomous vehicle. This strategy nonlinearly adjusts the tracking error to generate the desired heading angle and velocity, ensuring that the target path tracking is rapid and smooth. First, we establish a mathematical model of an autonomous vehicle and combine the hyperbolic-tangent line-of-sight guidance strategy with a noise-resistant active disturbance rejection controller to achieve high-precision target tracking in dynamic environments. Second, an extended state observer is employed to perform real-time observations and compensate for unknown disturbances during localization, significantly reducing the impact of cumulative errors. Finally, the effectiveness of the proposed method is validated using numerical simulations and real vehicle experiments. The experimental results demonstrate that, compared with the ET-Fuzzy-MPC method, the proposed method lowered the average position tracking error by 45.39% under complex road conditions. In practical curved-path tests, the vehicle's tracking error remained stable to within 0.192 m, representing a significant improvement in the target tracking accuracy and dynamic response performance.</p></div>2025-05-19T17:45:21ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0322648.t001https://figshare.com/articles/dataset/Dynamic_parameters_/29102466CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291024662025-05-19T17:45:21Z |
| spellingShingle | Dynamic parameters. Xiaosong Liu (66661) Pharmacology Biological Sciences not elsewhere classified Information Systems not elsewhere classified strategy nonlinearly adjusts extended state observer experimental results demonstrate desired heading angle base station positioning sensor data errors dynamic response capability complex road conditions precision target tracking cumulative errors generated tracking control method sight guidance strategy proposed method lowered autonomous vehicles based target path tracking target tracking accuracy real vehicle experiments cumulative errors sight guidance proposed method autonomous vehicles tracking error tracking accuracy path tests dynamic environments complex environments mpc method method incorporates autonomous vehicle xlink "> world scenarios within 0 wheel odometry various types unknown disturbances ultimately affect time observations tangent line switching mechanism study proposes significantly reducing significant improvement relies solely practical curved perform real onboard sensors odometry mechanism mathematical model image processing even losses |
| status_str | publishedVersion |
| title | Dynamic parameters. |
| title_full | Dynamic parameters. |
| title_fullStr | Dynamic parameters. |
| title_full_unstemmed | Dynamic parameters. |
| title_short | Dynamic parameters. |
| title_sort | Dynamic parameters. |
| topic | Pharmacology Biological Sciences not elsewhere classified Information Systems not elsewhere classified strategy nonlinearly adjusts extended state observer experimental results demonstrate desired heading angle base station positioning sensor data errors dynamic response capability complex road conditions precision target tracking cumulative errors generated tracking control method sight guidance strategy proposed method lowered autonomous vehicles based target path tracking target tracking accuracy real vehicle experiments cumulative errors sight guidance proposed method autonomous vehicles tracking error tracking accuracy path tests dynamic environments complex environments mpc method method incorporates autonomous vehicle xlink "> world scenarios within 0 wheel odometry various types unknown disturbances ultimately affect time observations tangent line switching mechanism study proposes significantly reducing significant improvement relies solely practical curved perform real onboard sensors odometry mechanism mathematical model image processing even losses |