Lane-Relative Pose Localization Dataset

#Structured data generation #Lane-relative localization #Spatial relationship calculation #JSON output generation #Lane-level localization #Automated driving navigation #Vehicle trajectory analysis #Map matching

4560
0.4GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-04

AI Analysis & Value Prop

This dataset uses vehicle pose, lane geometry, and coordinate references as inputs to provide structured localization results relative to lane centerlines and lane segments. Outputs include the nearest centerline point index, longitudinal position, lateral offset, heading difference, projected point, lane-segment progress, and lane-membership status, with explicit units and value conventions. It supports structured-output supervised fine-tuning, lane-level localization development, map matching, and spatial relationship evaluation; sample volume and coverage depend on the specific data offering.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
scene_input	object	Raw JSON input containing vehicle pose, lane geometry, and coordinate references.
is_within_lane	boolean	Indicates whether the vehicle is within the valid lane area, based on the absolute lateral offset and lane half-width.
lateral_offset	number	Signed lateral distance in meters from the vehicle reference point to the lane centerline. The sign follows the lane travel direction and the right-hand coordinate convention.
heading_difference	number	Normalized angular difference in radians between the vehicle heading and the tangent direction of the lane centerline, ranging from negative pi to pi.
localization_valid	boolean	Indicates whether localization is valid based on centerline geometry completeness, projection computability, and the spatial relationship between the vehicle and lane.
lane_segment_progress	number	Normalized longitudinal progress of the vehicle along the lane centerline, with 0 at the segment start and 1 at the segment end.
longitudinal_position	number	Cumulative distance in meters from the start of the lane segment to the vehicle reference point along the lane centerline.
distance_to_centerline	number	Absolute distance in meters from the vehicle reference point to the lane centerline.
nearest_centerline_index	integer	Zero-based index of the discrete lane centerline point nearest to the projection of the vehicle reference point.
projected_centerline_point	object	Coordinates in meters of the nearest point on the lane centerline to the vehicle reference point.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What localization information does the data include?: The JSON input contains vehicle pose, lane geometry, and coordinate references. Outputs include the nearest centerline point index, longitudinal position, lateral offset, heading difference, projected centerline point, lane-segment progress, distance to the centerline, and lane-membership and localization-validity flags.

What units are used for distance, angle, and progress?: Longitudinal position, lateral offset, distance to the centerline, and projected point coordinates are expressed in meters. Heading difference is in radians and ranges from negative pi to pi. Lane-segment progress is normalized from 0 to 1. The sign of lateral offset follows the lane travel direction and the right-hand coordinate convention.

Which training and evaluation tasks does it support?: It supports structured-output supervised fine-tuning, lane-relative localization, vehicle-lane spatial relationship calculation, map matching, and JSON generation. It can also help evaluate whether localization fields and lane-validity decisions meet task requirements.

How can I access the data and check its license?: Refer to the product page for the available download or access method, and review the applicable license, usage restrictions, and delivery specifications before use. Confirm the sample volume and coverage in the product listing or delivery documentation.

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Cite this Work

@dataset{Mobiusi2026,
  title={Lane-Relative Pose Localization Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/b805c9a663f3db98b2e73a9d6532c78f},
  urldate={2026-10-27},
  keywords={lane-relative localization data,lane centerline localization,automated driving data,lane-level map data,vehicle lateral offset,heading difference calculation,structured JSON data,localization model training},
  version={1.0}
}

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