Lane Line Detection Training With Difficulty-Aware Loss

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Solution Overview

Problem

Traditional lane line detection methods suffer from poor robustness and stability, and deep learning-based models face errors in complex road scenarios, posing safety hazards due to overfitting and overconfidence, especially in environments with high lane line detection difficulty.

Innovation Solution

A lane line detection model is trained using a dataset with varying detection difficulty levels, employing a loss function that incorporates lane line detection difficulty metrics, such as Mahalanobis distance, to adjust model parameters and provide predictions with appropriate uncertainty levels.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If deep learning-based lane line detection models are used, then generalization and adaptability are improved, but reliability and safety deteriorate due to errors in complex road scenarios

Engineering Contradiction:

Improvegeneralization and adaptabilityVSAvoidreliability and safety

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

An image segmentation model is introduced as an intermediary to extract lane line feature vectors from road images. These feature vectors serve as a bridge between the raw image data and the lane line detection model, providing structured intermediate representations that improve both adaptability and reliability by capturing essential lane line characteristics before final detection

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent changes the parameter representation by extracting lane line feature vectors (such as color, shape, and position features) from road images using the image segmentation model. These extracted features are then used to train the lane line detection model, transforming the raw image data into meaningful parameters that enhance detection accuracy and reliability across diverse road scenarios

Inventive Principle:

Principle #35Parameter changes

2Device complexity

If traditional lane line detection methods are used, then device complexity is reduced, but robustness and stability deteriorate

Engineering Contradiction:

Improvemethod complexityVSAvoidrobustness and stability

Core Design Contradiction:

Device complexityVSStability of the object's composition

Solution Approach 1:

Traditional mechanical detection methods (color thresholding, edge detection, Hough transform) are replaced with an image segmentation model that uses learned features to identify lane lines. This substitution maintains relatively simple device architecture while significantly improving robustness and stability through data-driven feature extraction

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The image segmentation model acts as an intermediary layer between simple image input and lane line detection output. This intermediate feature extraction step provides stable and robust lane line representations without requiring complex detection algorithms, thereby improving stability while keeping overall system complexity manageable

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If lane line detection models are trained without considering detection difficulty, then training efficiency is improved, but accuracy and reliability deteriorate in challenging environments

Engineering Contradiction:

Improvetraining efficiencyVSAvoiddetection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent applies local quality by differentiating training based on detection difficulty. Road images are categorized into different difficulty levels (easy, medium, hard) based on extracted lane line feature vectors. The training process then applies different loss function weights or sampling strategies to different difficulty levels, ensuring that challenging cases receive appropriate attention without compromising overall training efficiency

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The training approach becomes dynamic by adapting the loss function based on detection difficulty. The system dynamically adjusts training parameters and loss weights according to the complexity of each road image sample, allowing efficient processing of easy cases while allocating more computational resources to difficult cases, thereby improving both efficiency and accuracy

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260030875A1Method and apparatus for detecting lane lines

Publication Date: 2026.01.29 ROBERT BOSCH GMBH

AI summary

A method for training a lane line detection model includes (i) obtaining a dataset comprising a plurality of road image samples, wherein the road image samples have lane line labels, (ii) extracting lane line feature vectors of the road image samples by way of an image segmentation model and the lane line labels of the road image samples, (iii) determining the lane line detection difficulty of the road image samples based on the lane line feature vectors of the road image samples, and (iv) training the lane line detection model using the road image samples in the dataset and a loss function based on the lane line detection difficulty of the road image samples.