Traffic Line Detection Training With 3D Geometry Constraints
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Solution Overview
Problem
Existing machine learning models for detecting traffic line markings struggle to accurately learn permissible 3D geometry due to the need for large amounts of data and accurate ground truth, often leading to impermissible detections from erroneous learning of outliers.
Innovation Solution
Incorporate prior physical knowledge about traffic line geometry, such as parallelism, transverse inclination, and curvature limits, into the training process using differentiable cost functions, allowing the model to be trained without ground truth data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If purely data-driven approaches are used to train machine learning models for traffic line detection, then the models can learn from large amounts of image data, but the models may learn impermissible geometry due to erroneous ground truth and outliers in the training data
Solution Approach 1:
The patent applies preliminary action by incorporating prior physical knowledge about traffic line geometry (parallelism, transverse inclination, curvature limits) into the training process before the model sees any training data. This is achieved through differentiable cost functions that encode these geometric constraints, allowing the model to learn permissible geometry from the outset rather than having to filter out impermissible detections after learning from large datasets.
Solution Approach 2:
The patent changes the training parameters by introducing differentiable cost functions that directly encode geometric constraints (parallelism, transverse inclination angles, curvature limits) into the loss function. This transforms the training objective from purely data-driven pattern recognition to constraint-satisfying geometry learning, where the model optimizes both detection accuracy and geometric permissibility simultaneously.
2Manufacturing precision
If large amounts of data and accurate ground truth are required to learn permissible geometry, then the model can achieve accurate detection, but the training process becomes slower and more resource-intensive
Solution Approach 1:
The patent applies preliminary action by pre-encoding geometric constraints into differentiable cost functions before training begins. This allows the model to learn permissible geometry directly from these constraints rather than requiring extensive training data and ground truth annotations, significantly reducing training time while maintaining detection accuracy.
Solution Approach 2:
The patent introduces differentiable cost functions as intermediaries between the model's predictions and the geometric constraints. These cost functions serve as a bridge that translates physical knowledge about traffic line geometry into trainable objectives, allowing the model to learn permissible geometry without requiring large amounts of annotated training data.
3Device complexity
If the neural network is not informed about physically sensible properties of lane and line geometry, then the training process is simpler, but the network learns detections with impermissible geometry
Solution Approach 1:
The patent changes the training parameters by incorporating geometric constraints directly into the loss function through differentiable cost functions. This adds information about physically sensible properties (parallelism, transverse inclination, curvature) to the training process while maintaining relative simplicity through the use of gradient-based optimization.
Solution Approach 2:
The patent replaces the mechanical approach of manually filtering impermissible detections after training with a software-based differentiable cost function approach. This substitution allows geometric constraints to be enforced continuously during training through gradient descent, eliminating the need for complex post-processing while ensuring geometry permissibility.
Data Source
AI summary
A method for training a machine learning model for detecting traffic line markings. The method includes: providing training data, wherein the training data comprise individual images of traffic scenes having traffic line markings, wherein the individual images result from a capturing by at least one sensor; defining a first cost function, wherein the first cost function describes a degree of correspondence between traffic line markings predicted by the machine learning model and at least one geometric property; and training the machine learning model using the defined first cost function, wherein the machine learning model predicts respective traffic line markings of the individual images of the training data during training, wherein the defined first cost function receives as input the traffic line markings predicted by the machine learning model. A computer program, a device, and a storage medium are also described.