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README.md

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+---
+pretty_name: >-
+  U-EASWS: An Urban Expressway Type-A Short Weaving Segment UAV Vehicle
+  Trajectory Dataset Integrated with Interaction and Risk Semantic Annotations
+language:
+- en
+license: cc-by-4.0
+source_datasets:
+- original
+tags:
+- vehicle-trajectory
+- uav
+- urban-expressway
+- weaving-segment
+- type-a-weaving
+- short-weaving-segment
+- microscopic-traffic
+- lane-changing
+- traffic-safety
+- surrogate-safety-measures
+- vehicle-interaction
+- interaction-semantics
+- risk-semantics
+- tabular
+- timeseries
+- image
+size_categories:
+- 1B<n<10B
+---
 U-EASWS Dataset Card
 ---
+---
 pretty_name: >-
   U-EASWS: An Urban Expressway Type-A Short Weaving Segment UAV Vehicle
   Trajectory Dataset Integrated with Interaction and Risk Semantic Annotations
 language:
   - en
 license: cc-by-4.0
-task_categories:
-  - uav-vehicle-trajectory-dataset
-size_categories:
-  - 10B<n<100B
+source_datasets:
+  - original
 tags:
-  - >-
-    Dataset, Expressway weaving segments, Vehicle trajectory, UAV, Semantic
-    annotation
+  - vehicle-trajectory
+  - uav
+  - urban-expressway
+  - weaving-segment
+  - type-a-weaving
+  - short-weaving-segment
+  - microscopic-traffic
+  - lane-changing
+  - traffic-safety
+  - surrogate-safety-measures
+  - vehicle-interaction
+  - interaction-semantics
+  - risk-semantics
+  - tabular
+  - timeseries
+  - image
 ---
-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.
-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.
-Supported Tasks
-U-EASWS is built to support research on microscopic traffic flow and driving behavior, including but not limited to:
-- Microscopic driving behavior modeling and model validation
-- Discretionary and mandatory lane-changing behavior analysis
-- Traffic surrogate safety assessment and conflict identification
-- Multi-vehicle interaction mechanism and game dynamics research
-- Traffic simulation model calibration for weaving segments
-Dataset Structure
-Core Data Components
-U-EASWS provides three tiers of standardized data content:
-1. High-fidelity continuous vehicle trajectories
-Vehicle trajectories are extracted using a fine-tuned YOLOv8 detection and tracking model, then reconstructed via PCHIP interpolation and sEMA filtering. All kinematic parameters have passed physical consistency verification, effectively eliminating pixel jitter and tracking noise.
-2. Synchronized surrogate safety measures
-Multi-dimensional traffic risk indicators are calculated and aligned with trajectory timestamps, including:
-- Time-to-Collision (TTC)
-- Post-Encroachment Time (PET)
-- Deceleration Rate to Avoid a Crash (DRAC)
-3. Spatial interaction and intensity semantics
-Based on proximity resistance theory, the dataset annotates inter-vehicle spatial interaction relationships and interaction intensity, to capture complex multi-vehicle game dynamics under high spatial pressure in weaving areas.
-Dataset Creation
-Data Collection
-- Collection platform: Unmanned Aerial Vehicle (UAV) aerial photography
-- Coverage: 7 typical Type-A short weaving segments on urban expressways in Changchun, China
-- Scenarios: Naturalistic traffic flow covering diverse traffic states and geometric conditions
-Processing Pipeline
-1. Vehicle detection and multi-object tracking via fine-tuned YOLOv8 model
-2. Trajectory reconstruction and denoising via PCHIP interpolation + sEMA filtering
-3. Kinematic parameter calculation and physical consistency verification
-4. Surrogate safety measure calculation and timestamp alignment
-5. Spatial interaction semantics annotation based on proximity resistance theory
-Technical Validation
-The fidelity and reliability of U-EASWS have been systematically verified:
-1. Exceptional vehicle detection accuracy and identity tracking stability under dense traffic conditions
-2. Effective elimination of pixel jitter and tracking noise, yielding smooth, physically consistent vehicle kinematic data
-3. Successful capture of volatile driving behaviors under high spatial pressure, revealing complex multi-vehicle game dynamics that cannot be fully characterized by conventional metrics
-Additional Information
-Licensing Information
-This dataset is licensed under CC BY 4.0.
+
+# U-EASWS
+
+## Dataset Description
+
+U-EASWS (Urban Expressway Type-A Short Weaving Segment UAV Vehicle Trajectory Dataset Integrated with Interaction and Risk Semantic Annotations) is a UAV-based microscopic vehicle trajectory dataset specifically developed for urban expressway Type-A short weaving segments.
+
+The dataset contains 19,122 high-quality vehicle trajectories collected from seven real-world Type-A short weaving scenarios in Changchun, China, with approximately 3.6 h of valid aerial video observations. The observed weaving segments cover different roadway geometries and channelization configurations, including unrestricted weaving, restricted merging, and restricted diverging layouts.
+
+Type-A weaving is characterized by merging and diverging traffic streams that are each required to perform a lane change within the same weaving area. In short weaving segments, the limited longitudinal distance available for completing these maneuvers produces frequent mandatory lane-changing interactions among merging, diverging, and through vehicles. U-EASWS was therefore designed specifically to provide continuous microscopic observations of vehicle movements under these geometrically constrained and interaction-intensive conditions.
+
+In addition to reconstructed vehicle trajectories and kinematic states, U-EASWS provides road-geometry information, lane information, surrogate safety measures, and vehicle-interaction semantic annotations. Frame-level detection and tracking outputs are also released to allow users to perform independent coordinate transformation, trajectory reconstruction, smoothing, vehicle-dimension estimation, or other customized post-processing procedures.
+
+The dataset is licensed under CC BY 4.0.
+
+## Supported Research Tasks
+
+U-EASWS is intended to support research on microscopic traffic flow and driving behavior in complex weaving environments, including:
+
+- microscopic driving behavior modeling and model validation;
+- car-following, lane-changing, and weaving behavior analysis;
+- discretionary and mandatory lane-changing analysis;
+- traffic surrogate safety assessment and conflict identification;
+- multi-vehicle interaction analysis;
+- spatial interaction and driving-behavior research;
+- vehicle trajectory prediction;
+- traffic simulation model calibration and validation;
+- autonomous-driving simulation;
+- tactical decision-making and trajectory-planning research;
+- comparative analysis of traffic behavior under different weaving geometries and channelization configurations.
+
+## Dataset Repository Structure
+
+The repository is organized according to the processing level and semantic content of the data. The current release contains five main data components.
+
+### 1. Weaving Segment Geometry Images
+
+[Weaving_Segment_Geometry_Images](https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main/Weaving_Segment_Geometry_Images)
+
+This folder contains seven top-down aerial channelization images corresponding to Weaving Segments WS1–WS7.
+
+The images provide the roadway geometry and channelization context required to interpret the trajectory data. They show the actual road layout, lane arrangement, lane boundaries, solid and dashed lane markings, and the geometric organization of the observed Type-A short weaving segments.
+
+The seven scenarios contain multiple channelization configurations, including unrestricted weaving, restricted merging, and restricted diverging layouts. Dashed lane markings indicate roadway sections where lateral crossing is permitted, whereas solid lane markings represent sections with restricted lane-changing opportunities.
+
+These geometry images should be used together with the trajectory fields `Direction`, `Lane number`, `Dist_to_Merge_m`, and `Dist_to_Diverge_m` when reconstructing the spatial relationship between vehicle movements and the underlying road geometry.
+
+### 2. Raw Data
+
+[Raw_Data](https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main/Raw_Data)
+
+The `Raw_Data` folder contains frame-level vehicle detection and tracking outputs before coordinate transformation, trajectory reconstruction, kinematic calculation, and semantic annotation.
+
+The raw data are divided into 16 CSV files:
+
+- `WS1_2_raw_detection_data_1.csv`
+- `WS1_2_raw_detection_data_2.csv`
+- `WS1_2_raw_detection_data_3.csv`
+- `WS1_2_raw_detection_data_4.csv`
+- `WS1_2_raw_detection_data_5.csv`
+- `WS1_2_raw_detection_data_6.csv`
+- `WS3_raw_detection_data_1.csv`
+- `WS3_raw_detection_data_2.csv`
+- `WS3_raw_detection_data_3.csv`
+- `WS4_5_raw_detection_data_1.csv`
+- `WS4_5_raw_detection_data_2.csv`
+- `WS4_5_raw_detection_data_3.csv`
+- `WS4_5_raw_detection_data_4.csv`
+- `WS6_7_raw_detection_data_1.csv`
+- `WS6_7_raw_detection_data_2.csv`
+- `WS6_7_raw_detection_data_3.csv`
+
+Files beginning with `WS1_2`, `WS4_5`, and `WS6_7` correspond to the three bidirectional observation sites, whereas files beginning with `WS3` correspond to the one-way observation scenario.
+
+Each row represents one tracked vehicle observation in a particular video frame.
+
+The released raw data contain the following fields:
+
+- `Frame`: video frame index. Unit: dimensionless frame index.
+- `Vehicle_ID`: vehicle tracking ID assigned during the detection and multi-object tracking stage. Unit: dimensionless identifier.
+- `x_center_px`: horizontal pixel coordinate of the center of the detected vehicle bounding box. Unit: pixel.
+- `y_center_px`: vertical pixel coordinate of the center of the detected vehicle bounding box. Unit: pixel.
+- `width_px`: width of the axis-aligned vehicle detection bounding box. Unit: pixel.
+- `height_px`: height of the axis-aligned vehicle detection bounding box. Unit: pixel.
+
+The raw files preserve image-space vehicle positions and detection-box dimensions and can therefore be used for independent coordinate transformation, vehicle-dimension estimation, trajectory interpolation, smoothing, or other user-defined post-processing procedures.
+
+The `Vehicle_ID` in the raw data is the tracking identifier associated with the corresponding source sequence and should be distinguished from the globally unique `Ego_Vehicle_ID` provided in the processed trajectory files.
+
+The term "raw data" in this repository specifically refers to frame-level vehicle detection and tracking outputs. It does not refer to the original UAV video files.
+
+### 3. Basic Trajectory Data
+
+[Basic_Trajectory_Data](https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main/Basic_Trajectory_Data)
+
+This folder contains four CSV files corresponding to the four observation groups:
+
+- Weaving Segments 1–2;
+- Weaving Segment 3;
+- Weaving Segments 4–5;
+- Weaving Segments 6–7.
+
+The files provide reconstructed vehicle trajectories expressed in a road-aligned physical coordinate system together with vehicle kinematic states, lane information, traffic direction, geometry-related distances, and estimated vehicle length.
+
+The main fields are defined as follows:
+
+- `Ego_Vehicle_ID`: globally unique vehicle identifier. Unit: dimensionless identifier.
+- `Frame`: source video frame index. Unit: dimensionless frame index.
+- `Delta_t_s`: physical time interval associated with the current trajectory record. Unit: s.
+- `x_center`: vehicle-center coordinate along the longitudinal road axis in the road-aligned local physical coordinate system. Unit: m.
+- `y_center`: vehicle-center coordinate along the lateral road axis in the road-aligned local physical coordinate system. Unit: m.
+- `v_x`: longitudinal vehicle velocity. Unit: m/s.
+- `v_y`: lateral vehicle velocity. Unit: m/s.
+- `a_x`: longitudinal vehicle acceleration. Unit: m/s².
+- `a_y`: lateral vehicle acceleration. Unit: m/s².
+- `Direction`: vehicle travel direction. The values are `Left` or `Right`. Unit: dimensionless categorical variable.
+- `Lane number`: current lane occupied by the vehicle. Unit: dimensionless lane index.
+- `Dist_to_Merge_m`: absolute longitudinal distance from the current vehicle center to the predefined merge reference cross-section. Unit: m.
+- `Dist_to_Diverge_m`: absolute longitudinal distance from the current vehicle center to the predefined diverge reference cross-section. Unit: m.
+- `Vehicle_Length_m`: estimated physical length of the vehicle. Unit: m.
+
+`Vehicle_Length_m` is estimated as a vehicle-level physical attribute from multiple valid frame-level bounding-box observations and remains constant for the complete trajectory of the same uniquely identified vehicle.
+
+`Dist_to_Merge_m` and `Dist_to_Diverge_m` explicitly connect individual vehicle states to the principal geometric locations of the weaving segment and can be used to analyze how microscopic behavior changes with the remaining distance to the merge or diverge reference cross-section.
+
+### 4. Risk Semantic Data
+
+[Risk_Semantic_Data](https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main/Risk_Semantic_Data)
+
+This folder contains four CSV files with risk semantic annotations corresponding to:
+
+- Weaving Segments 1–2;
+- Weaving Segment 3;
+- Weaving Segments 4–5;
+- Weaving Segments 6–7.
+
+The risk semantic files retain the basic trajectory and kinematic fields and additionally provide surrogate safety measures and explicit risk-related annotations.
+
+The additional fields are:
+
+- `TTC`: Time-to-Collision. Unit: s.
+- `PET`: Post-Encroachment Time. Unit: s.
+- `DRAC`: Deceleration Rate to Avoid a Crash. Unit: m/s².
+- `Max_Risk_Target_ID`: ID of the target vehicle producing the highest conflict risk for the ego vehicle in the current frame. The value is set to `-1` when no corresponding risk target exists. Unit: dimensionless identifier.
+- `Risk_Level`: explicit risk classification, with values `High Risk` or `Low Risk`.
+
+TTC and DRAC are frame-wise state-based surrogate safety measures. PET is treated as an event-level measure associated with identified cut-in events. For each identified cut-in event, the calculated PET value is assigned at the event-detection frame to both the cut-in vehicle and its corresponding following vehicle.
+
+A record is labeled `High Risk` when at least one of the following conditions is satisfied:
+
+- `TTC < 3.0 s`;
+- `DRAC > 3.0 m/s²`;
+- the record corresponds to an identified cut-in event with `PET < 2.5 s`.
+
+Records satisfying none of these conditions are labeled `Low Risk`.
+
+### 5. Interaction Semantic Data
+
+[Interaction_Semantic_Data](https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main/Interaction_Semantic_Data)
+
+This folder contains four CSV files with vehicle-interaction semantic annotations corresponding to:
+
+- Weaving Segments 1–2;
+- Weaving Segment 3;
+- Weaving Segments 4–5;
+- Weaving Segments 6–7.
+
+The interaction semantic files retain the basic trajectory information and add vehicle-pair interaction information derived from a two-dimensional Proximity Resistance (PR) framework.
+
+For each ego vehicle, surrounding vehicles traveling in the same direction within a local perception radius of 50 m are first treated as candidate interaction vehicles. Candidate vehicle pairs are then represented in an ego-vehicle-centered local coordinate system and assigned interaction-related variables.
+
+The additional fields are:
+
+- `Interaction_Target_ID`: ID of the target vehicle paired with the ego vehicle. Unit: dimensionless identifier.
+- `x_local`: relative-position component of the target vehicle along the local X-axis of the vehicle-pair coordinate system. Unit: m.
+- `y_local`: relative-position component of the target vehicle along the local Y-axis of the vehicle-pair coordinate system. Unit: m.
+- `v_rel`: scalar relative speed between the vehicle pair. Unit: m/s.
+- `Interaction Strength`: dimensionless PR-based measure of proximity-induced vehicle interaction, ranging from 0 to 1. Larger values represent stronger proximity-induced interaction.
+- `Is Interacting`: binary indicator of whether the candidate vehicle pair satisfies the effective-interaction criterion. `1` denotes an effective interaction and `0` otherwise.
+- `Interaction Type`: behavioral interaction category, including `Car Following`, `Parallel Driving`, and `Lane Changing/Weaving`.
+
+`Interaction Strength` represents proximity-induced interaction rather than collision probability or conflict severity. A large PR value indicates that the target vehicle lies deeper within the empirically inferred driver space of the ego vehicle and therefore represents stronger spatial interaction.
+
+Consequently, `Is Interacting = 1` should not automatically be interpreted as a high-risk traffic conflict. Risk semantics and interaction semantics describe different and complementary dimensions of microscopic traffic behavior.
+
+## Road Geometry and Scenario Information
+
+The dataset covers seven Type-A short weaving segments located on urban expressways in Changchun, China.
+
+All seven sites exhibit the defining Type-A weaving configuration, in which merging and diverging traffic streams must each perform a lane-changing maneuver within the same weaving area.
+
+Three UAV observation sequences contain bidirectional traffic flows and each corresponds to two weaving scenarios:
+
+- WS1 and WS2;
+- WS4 and WS5;
+- WS6 and WS7.
+
+The remaining observation sequence contains one-way traffic and corresponds to WS3.
+
+WS1, WS3, WS4, and WS6 retain left-to-right vehicle movement in the road-geometry representation, whereas WS2, WS5, and WS7 represent the opposite right-to-left traffic direction.
+
+The seven scenarios have the following collection characteristics:
+
+- **WS1:** collected on January 7, 2025; 1547 s of valid video; approximately 290 m observation length; approximately 220 m UAV altitude; four lanes; 80 km/h speed limit; 60 fps source video; 2056 trajectories.
+- **WS2:** collected on January 7, 2025; 1547 s of valid video; approximately 290 m observation length; approximately 220 m UAV altitude; four lanes; 80 km/h speed limit; 60 fps source video; 1935 trajectories.
+- **WS3:** collected on September 17, 2025; 2394 s of valid video; approximately 315 m observation length; approximately 270 m UAV altitude; three lanes; 80 km/h speed limit; source observations include 60 fps and 30 fps video; 2985 trajectories.
+- **WS4:** collected on September 16, 2025; 1437 s of valid video; approximately 375 m observation length; approximately 360 m UAV altitude; four lanes; 80 km/h speed limit; 30 fps source video; 2017 trajectories.
+- **WS5:** collected on September 16, 2025; 1437 s of valid video; approximately 375 m observation length; approximately 360 m UAV altitude; five lanes; 80 km/h speed limit; 30 fps source video; 2126 trajectories.
+- **WS6:** collected on September 17, 2025; 2306 s of valid video; approximately 370 m observation length; approximately 350 m UAV altitude; five lanes; 80 km/h speed limit; 30 fps source video; 4038 trajectories.
+- **WS7:** collected on September 17, 2025; 2306 s of valid video; approximately 370 m observation length; approximately 350 m UAV altitude; five lanes; 80 km/h speed limit; 30 fps source video; 3965 trajectories.
+
+The reported observation length represents the retained roadway coverage used for trajectory extraction, including the core weaving area and adjacent upstream and downstream buffer regions where applicable.
+
+The geometry images in `Weaving_Segment_Geometry_Images` should be consulted when performing lane-level or geometry-specific analyses because lane numbers, solid/dashed markings, and merge/diverge locations vary among the seven scenarios.
+
+## Coordinate Systems
+
+U-EASWS contains three distinct coordinate systems corresponding to different processing levels. These coordinate systems should not be interpreted interchangeably.
+
+### Source-Image Pixel Coordinate System
+
+The frame-level files in `Raw_Data` use the source-video image coordinate system.
+
+The coordinate origin is located at the upper-left corner of the video image. The horizontal pixel coordinate increases from left to right, and the vertical pixel coordinate increases from top to bottom.
+
+The relevant variables are:
+
+- `x_center_px`: horizontal bounding-box-center coordinate, in pixels;
+- `y_center_px`: vertical bounding-box-center coordinate, in pixels;
+- `width_px`: bounding-box width, in pixels;
+- `height_px`: bounding-box height, in pixels.
+
+These variables describe image-space measurements and should not be directly interpreted as physical distances.
+
+### Road-Aligned Local Physical Coordinate System
+
+The processed trajectory data use a scene-level road-aligned metric Cartesian coordinate system.
+
+For scenes recorded by multiple UAV video sequences, a stabilized reference frame is selected as the master frame. Static road features visible in both the source frames and the master frame are used as control points, and fixed planar homography transformations are applied to project vehicle locations from different UAV views into a common reference coordinate system.
+
+Static road reference points with known physical distances are then used to determine the metric scale. A local Cartesian coordinate origin is defined in the master frame.
+
+In this coordinate system:
+
+- the `x` axis represents the longitudinal road direction;
+- the `y` axis represents the lateral road direction;
+- positions are measured in meters;
+- velocities are measured in meters per second;
+- accelerations are measured in meters per second squared.
+
+The principal variables expressed in this system are `x_center`, `y_center`, `v_x`, `v_y`, `a_x`, and `a_y`.
+
+For files containing bidirectional traffic, the road-aligned coordinate system is fixed to the corresponding scene rather than independently aligned with the travel direction of each vehicle. Users should therefore interpret the coordinate values together with the `Direction` field.
+
+### Vehicle-Pair Local Interaction Coordinate System
+
+The interaction semantic data additionally use an ego-vehicle-centered local coordinate system for each candidate vehicle pair.
+
+The centroid of the ego vehicle (EV) is defined as the origin.
+
+The direction of the relative-velocity vector between the ego vehicle and the principal other vehicle (POV) is defined as the positive local Y-axis. The direction perpendicular to the relative-velocity vector and pointing to the right is defined as the positive local X-axis.
+
+The relative position of the target vehicle is projected into this local coordinate system to obtain `x_local` and `y_local`.
+
+Vehicle pairs with zero relative velocity are excluded from this coordinate transformation because the direction of the relative-velocity vector is undefined.
+
+This vehicle-pair local coordinate system is different from the scene-level road-aligned physical coordinate system used for `x_center` and `y_center`.
+
+## Temporal Resolution
+
+The UAV videos were recorded using two source recording modes:
+
+- 3840 × 2160 pixels at 30 frames per second;
+- 2688 × 1512 pixels at 60 frames per second.
+
+Therefore, the nominal physical interval between two consecutive source frames is:
+
+- `1/30 s`, approximately `0.03333 s`, for 30 fps video;
+- `1/60 s`, approximately `0.01667 s`, for 60 fps video.
+
+Because U-EASWS contains observations originating from both frame rates, users should not assume a single universal sampling interval for the entire dataset.
+
+The processed trajectory files therefore include `Delta_t_s`, which explicitly provides the physical time interval associated with the corresponding trajectory record.
+
+`Frame` and `Delta_t_s` should be used together when reconstructing physical time sequences or calculating time-dependent variables.
+
+## Data Collection
+
+The original vehicle observations were extracted from UAV aerial videos recorded using two DJI Mavic 2 Zoom UAVs.
+
+The recording modes were 3840 × 2160 at 30 fps and 2688 × 1512 at 60 fps. The UAVs were operated from a near-vertical top-down perspective to provide continuous coverage of the short weaving areas and their adjacent roadway sections.
+
+The zoom function was not used during data collection in order to avoid changes in image scale and additional geometric distortion caused by zoom adjustment.
+
+The UAV recording altitude ranged approximately from 220 m to 360 m, while the retained observation lengths ranged approximately from 290 m to 400 m.
+
+Data collection was performed during daytime periods under clear and low-wind conditions to reduce severe vehicle-shadow effects and UAV-view instability.
+
+## Data Processing
+
+The processed trajectory and semantic data were generated through a standardized workflow.
+
+The major stages include UAV video stabilization and region-of-interest extraction, vehicle detection and multi-object tracking, lane-region annotation, multi-video coordinate registration, transformation from image coordinates to road-aligned metric coordinates, trajectory quality screening, missing-observation reconstruction, trajectory smoothing, vehicle kinematic calculation, vehicle-length estimation, surrogate-safety-measure calculation, and interaction semantic annotation.
+
+Vehicle trajectories are reconstructed using PCHIP interpolation where sufficient observations are available, with linear interpolation used for sparse cases that cannot satisfy the PCHIP requirements. Symmetric exponential moving average (sEMA) filtering is subsequently applied to suppress high-frequency trajectory noise while reducing temporal phase shift.
+
+The processed data should therefore be distinguished from the frame-level records provided in `Raw_Data`.
+
+## Relationship Between Data Components
+
+The repository provides multiple data levels so that users can select the level most appropriate for their research objective.
+
+`Raw_Data` provides low-level frame-wise image-coordinate and bounding-box information and is intended for users who wish to independently perform trajectory post-processing.
+
+`Basic_Trajectory_Data` provides reconstructed physical vehicle trajectories and is suitable for vehicle kinematics, lane-changing behavior, traffic-flow modeling, trajectory prediction, and simulation calibration.
+
+`Risk_Semantic_Data` retains the basic trajectory information and supplements it with TTC, PET, DRAC, maximum-risk-target information, and explicit risk labels.
+
+`Interaction_Semantic_Data` retains the basic trajectory information and supplements it with vehicle-pair relative states, PR-based interaction strength, effective-interaction labels, and interaction-type annotations.
+
+`Weaving_Segment_Geometry_Images` provides the roadway and channelization context required to interpret lane, direction, and merge/diverge-related variables.
+
+The processed risk and interaction files already retain the corresponding basic trajectory variables. Users therefore do not need to combine all processed layers for every analysis and may directly select the data component matching their research objective.
+
+## Technical Validation
+
+The quality of U-EASWS was evaluated from multiple complementary perspectives.
+
+Vehicle detection and multi-object tracking performance were examined under dense UAV-based traffic observations.
+
+Trajectory continuity and spatial consistency were evaluated after coordinate registration, reconstruction, and smoothing.
+
+Kinematic consistency was assessed using reconstructed position, velocity, and acceleration profiles to verify that the processed trajectories remain compatible with realistic microscopic vehicle motion.
+
+The spatial distributions of lane-changing events were further examined across all seven Type-A short weaving segments. The results show that lane-changing behavior is systematically related to roadway geometry, lane position, travel direction, and channelization constraints rather than being randomly distributed within the observation area.
+
+The risk semantic layer was evaluated through the spatial and temporal characteristics of TTC, PET, and DRAC.
+
+The interaction semantic layer was evaluated by examining the relationship between PR-based interaction strength and microscopic vehicle responses, as well as the spatial distribution of effective interactions across the weaving areas.
+
+## Usage Notes
+
+Users interested primarily in vehicle trajectories and kinematic analysis can directly use `Basic_Trajectory_Data`.
+
+Users studying traffic conflicts and surrogate safety should use `Risk_Semantic_Data`.
+
+Users studying vehicle-pair interaction, spatial interaction strength, or interaction type should use `Interaction_Semantic_Data`.
+
+Users who wish to perform independent trajectory reconstruction or alternative processing pipelines should begin with `Raw_Data`.
+
+For files containing bidirectional traffic, users should first distinguish traffic streams using `Direction` before conducting lane-level, vehicle-level, or weaving-segment-specific analyses.
+
+The fields `Lane number`, `Dist_to_Merge_m`, and `Dist_to_Diverge_m` should be interpreted together with the corresponding channelization image in `Weaving_Segment_Geometry_Images`.
+
+TTC and DRAC represent frame-wise conflict-related states, whereas PET is an event-level measure associated with identified cut-in events.
+
+PR-based `Interaction Strength` characterizes proximity-induced interaction rather than collision probability. An effective interaction does not necessarily indicate a high-risk conflict, and risk and interaction annotations should therefore not be interpreted as interchangeable measures.
+
+When comparing WS1–WS7, users should account for differences in roadway geometry, channelization configuration, lane number, traffic direction, observation duration, weaving length, and traffic conditions. Individual scenarios should preferably be examined separately before cross-scenario aggregation or normalization.
+
+## Limitations and Important Considerations
+
+The released `Raw_Data` represents frame-level outputs from the vehicle detection and multi-object tracking stage rather than the original UAV video streams.
+
+Consequently, these records preserve information at an earlier processing level than the reconstructed trajectory data but still depend on the performance of the upstream vehicle detection and tracking procedures.
+
+The road-aligned physical coordinates are scene-specific local coordinates rather than geographic latitude/longitude coordinates.
+
+`Vehicle_Length_m` is an estimated physical vehicle attribute derived from detection bounding boxes transformed into the metric coordinate system and is not a direct manual measurement of the physical vehicle.
+
+The interaction-strength annotation represents proximity-induced interaction and should not be interpreted as a direct estimate of crash probability.
+
+The dataset covers seven Type-A short weaving scenarios in Changchun, China. Researchers should consider the geographic, geometric, traffic-composition, and traffic-operation characteristics of these sites when generalizing findings to other roadway environments.
+
+## Licensing Information
+
+U-EASWS is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
+
+Under this license, users may share and adapt the material for any purpose, provided that appropriate credit is given to the original dataset creators and any changes are indicated.
+
+## Dataset Access
+
+The complete dataset is available at:
+
+https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main
+
+The repository currently contains:
+
+- [Weaving_Segment_Geometry_Images](https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main/Weaving_Segment_Geometry_Images)
+- [Raw_Data](https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main/Raw_Data)
+- [Basic_Trajectory_Data](https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main/Basic_Trajectory_Data)
+- [Risk_Semantic_Data](https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main/Risk_Semantic_Data)
+- [Interaction_Semantic_Data](https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main/Interaction_Semantic_Data)