Guodong Ma commited on
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README.md
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Dataset, Expressway weaving segments, Vehicle trajectory, UAV, Semantic
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Dataset Card for U-EASWS
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Dataset Description
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Dataset Summary
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Accurate modeling of microscopic driving behaviors under complex road geometries relies heavily on high-fidelity trajectory datasets. As typical traffic bottlenecks, Type-A short weaving segments feature intensive vehicle conflicts and coexisting discretionary/mandatory lane-changing behaviors, and have long attracted broad research attention. However, no open-source dataset specifically designed for such road segments is currently available, which greatly restricts the validation of microscopic traffic models in these high-conflict extreme environments.
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To address this gap, we present U-EASWS: a UAV-based vehicle trajectory dataset for urban expressway Type-A short weaving segments. The dataset covers 7 typical weaving scenarios in Changchun, China, and is enriched with comprehensive interaction semantics and traffic risk annotations. Processed via a standardized automated pipeline, U-EASWS provides a multi-layer semantic benchmark with empirical support for various microscopic driving behavior models targeting Type-A short weaving segments.
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Supported Tasks
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Dataset, Expressway weaving segments, Vehicle trajectory, UAV, Semantic
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annotation
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Accurate modeling of microscopic driving behaviors under complex road geometries relies heavily on high-fidelity trajectory datasets. As typical traffic bottlenecks, Type-A short weaving segments feature intensive vehicle conflicts and coexisting discretionary/mandatory lane-changing behaviors, and have long attracted broad research attention. However, no open-source dataset specifically designed for such road segments is currently available, which greatly restricts the validation of microscopic traffic models in these high-conflict extreme environments.
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To address this gap, we present U-EASWS: a UAV-based vehicle trajectory dataset for urban expressway Type-A short weaving segments. The dataset covers 7 typical weaving scenarios in Changchun, China, and is enriched with comprehensive interaction semantics and traffic risk annotations. Processed via a standardized automated pipeline, U-EASWS provides a multi-layer semantic benchmark with empirical support for various microscopic driving behavior models targeting Type-A short weaving segments.
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Supported Tasks
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