Traffic Line Geometry Modeling With B-Splines for Accurate Detection
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Solution Overview
Problem
Existing methods for detecting traffic line markings in complex environments, such as urban spaces, fail to accurately represent and model the geometry of the lane and the shape of the lane and the shape of the lane, leading to inaccuracies in detection due to discrete anchor or key point representations, and require complex post-processing to achieve smooth curves, which increases runtime.
Innovation Solution
A method using a geometric representation based on B-spline functions to model traffic line markings, where control points are determined orthogonally to define the shape, allowing for a more precise and efficient depiction of complex lane shapes by limiting the degrees of freedom to two, and using a machine learning model to predict line parameters and visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If discrete anchor or key point representations are used to describe traffic line geometry, then the detection method can be implemented with predefined fixed points, but the accuracy of detection becomes highly dependent on the predetermined position and number of these points, and complex post-processing is required
Solution Approach 1:
The patent transforms the discrete point representation into a continuous parametric curve representation. Instead of using fixed anchor points, the invention parameterizes the traffic line as a continuous curve defined by mathematical functions (polynomials, B-splines, or Bezier curves), where the geometry is described by continuous parameters rather than discrete fixed points. This eliminates the dependency on predetermined point positions and numbers while improving detection accuracy.
2Device complexity
If discrete anchor or key point representations are used, then the detection algorithm can be simplified initially, but complex post-processing by clustering and/or curve fitting is required to obtain a smooth and continuous curve, which has a negative impact on the runtime
Solution Approach 1:
The patent implements continuous curve representation from the outset, eliminating the need for post-processing steps. The neural network directly outputs continuous parametric curve parameters that describe the traffic line geometry, providing a smooth and continuous representation without requiring subsequent clustering or curve fitting operations. This continuous approach maintains algorithmic simplicity while eliminating runtime overhead from post-processing.
3Measurement precision
If parametric representations with continuous curves are used, then the geometry can be described directly by mathematical function parameters, but the complexity of the model increases
Solution Approach 1:
The patent segments the complex problem of traffic line detection into manageable components: (1) feature extraction from images, (2) parameterization using mathematical curves, and (3) neural network prediction of curve parameters. By dividing the problem into these distinct segments, the model handles complexity in a structured way, using appropriate mathematical tools for each segment while keeping the overall system manageable.
Solution Approach 2:
The patent employs universal mathematical curve representations (polynomials, B-splines, Bezier curves) that can describe various types of traffic lines (straight, curved, intersecting) with a single unified framework. These parametric forms serve multiple functions: they provide continuous representation, enable smooth interpolation, and facilitate efficient computation, thereby reducing overall model complexity despite the increased representational power.
Data Source
AI summary
A method for providing a geometric representation of a traffic line marking. A computer program, an apparatus, and a storage medium are also described.