Reference Line Coordinate Conversion for Precise Trajectory Localization
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Solution Overview
Problem
Raw path data from sensors on moving objects is insufficient for high-precision absolute localization, especially when using Cartesian coordinates, leading to errors and drifts in trajectory tracking, and conversion from Cartesian to reference line-based coordinates is inaccurate and prone to discontinuity and precision loss.
Innovation Solution
A system and method for transforming Cartesian coordinates to reference line-based coordinates using a reference line generator, ground truth preparer, coordinate conversion verifier, kinematic value generator, and delta yaw generator, which includes deduplicating, smoothing, and generating a curve to ensure accurate conversion and kinematic value calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If Cartesian coordinates are used to describe object position, then the coordinate system is simple and easy to work with, but localization precision deteriorates and objects drift outside the reference line
Solution Approach 1:
The patent transforms the coordinate system from Cartesian (x, y) parameters to reference line-based parameters (s, d) where s represents longitudinal distance along the reference line and d represents lateral distance. This parameter transformation resolves the contradiction by maintaining ease of operation through systematic conversion while achieving superior localization precision through the constrained reference line framework that prevents drift.
Solution Approach 2:
The patent introduces an intermediary conversion mechanism that transforms Cartesian coordinates to reference line-based coordinates through a systematic process involving reference line generation, point projection, and coordinate calculation. This intermediary transformation layer allows the system to leverage the simplicity of Cartesian coordinates while achieving the precision benefits of reference line-based coordinates.
2Reliability
If complex reference line geometry with sharp turns is used, then the model better represents real-world trajectories, but computational intensity increases significantly
Solution Approach 1:
The patent changes the mathematical representation by transforming complex Cartesian coordinate transformations into simpler reference line-based parameter calculations. The reference line-based system represents complex trajectories through longitudinal (s) and lateral (d) parameters relative to the reference line, which simplifies the computational operations required while maintaining accurate trajectory representation even with sharp turns.
3Productivity
If direct transformation from Cartesian to reference line coordinates is performed, then conversion speed is fast, but accuracy deteriorates due to incomplete conversion steps
Solution Approach 1:
The patent performs preliminary actions by pre-generating the reference line from trajectory data and pre-calculating the necessary transformation parameters and intermediate values. This allows for fast and accurate conversion during actual processing, as the complex reference line geometry is prepared in advance and stored for quick lookup during coordinate transformations.
Solution Approach 2:
The patent introduces intermediary calculation steps that bridge Cartesian and reference line coordinates through a systematic conversion process. This intermediary layer ensures accuracy by including all necessary conversion steps (reference line projection, closest point calculation, parameter computation) while maintaining efficiency through optimized algorithms and pre-computed reference data.
4Adaptability or versatility
If multiple coordinate transformations are performed to obtain absolute yaw angle, then the system can handle various input formats, but precision is lost due to error accumulation
Solution Approach 1:
The patent introduces an intermediary yaw angle calculation mechanism that computes absolute yaw angles directly from reference line-based coordinates rather than through multiple chained transformations. This intermediary approach maintains flexibility in handling various input formats while preventing error accumulation by reducing the number of transformation steps and using more direct calculation methods.
Data Source
AI summary
Techniques for preparing data for high-precision absolute localization of a moving object along a trajectory are provided. In one technique, a sliding window of a set of adjacent points along a trajectory of a moving object is identified, along with a midpoint in the sliding window. Based on the set of adjacent points, a first polynomial equation is generated for a first dimension and a second polynomial equation is generated for a second dimension. A first derivative at a particular timestamp associated with the midpoint is a first velocity along the first dimension, while a particular first derivative at the particular timestamp is a second velocity along the second dimension. A velocity in direction of yaw is generated based on the first velocity, the second velocity, and a slip angle associated with the midpoint. A yaw angle is generated based on the velocity in direction of yaw.