A unified longitudinal trajectory dataset for automated vehicle

Abstract Automated Vehicles (AVs) promise significant advances in transportation. Critical to these improvements is understanding AVs’ longitudinal behavior, relying heavily on real-world trajectory data. Existing open-source trajectory datasets of AV, however, often fall short in refinement, reliab...

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Bibliographic Details
Main Authors: Hang Zhou, Ke Ma, Shixiao Liang, Xiaopeng Li, Xiaobo Qu
Format: Article
Language:English
Published: Nature Portfolio 2024-10-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-024-03795-y
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