Results of the curved-path driving experiment.

<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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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Xiaosong Liu (66661) (author)
مؤلفون آخرون:	Huanhai Zhu (21387558) (author), Zebiao Shan (21387561) (author), Qingsong Lu (21387564) (author), Liben He (21387567) (author)
منشور في:	2025
الموضوعات:	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
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Results of the curved-path driving experiment.

مواد مشابهة