AI Lane Detection Plane Form Representation
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Solution Overview
Problem
Deep learning-based lane detection algorithms face performance degradation due to data imbalance between lane and non-lane areas, as the number of pixels corresponding to non-lane areas is overwhelmingly larger than those of lane areas, leading to inefficient prediction.
Innovation Solution
An AI model is trained to detect lane information in a plane form, rather than a segment form, using a multi-network architecture that includes an encoder for feature extraction, a decoder for lane detection, and an additional network for ego-lane recognition, with a loss function that balances pixel predictions and ensures neighboring pixels have consistent levels, addressing the data imbalance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based lane detection algorithms use traditional segment form (straight line or curved line) to represent lanes, then the model can learn lane patterns, but the prediction performance degrades due to data imbalance between lane and non-lane areas
Solution Approach 1:
The patent transforms the lane representation from traditional 1D segment form (straight lines or curved lines) to a 2D plane form, where lane information is expressed as a planar region in the image space. This dimensional change allows the model to treat lane detection as a pixel-wise classification problem rather than segment detection, effectively addressing the data imbalance issue by providing dense supervision signals across all lane pixels rather than sparse segment annotations.
Solution Approach 2:
The patent changes the output parameter representation from segment-based coordinates to plane-based pixel values. Instead of outputting lane parameters as mathematical curves, the model outputs a plane map where each pixel value indicates whether it belongs to a lane area. This parameter transformation enables the use of standard pixel-wise loss functions and improves training stability by utilizing all lane pixels for gradient computation.
2Adaptability or versatility
If the AI model expresses lane information in segment form, then it follows traditional approaches, but it cannot accurately detect disconnected lanes or recognize ego-lane reliably
Solution Approach 1:
By representing lanes as 2D planar regions instead of 1D segments, the model gains the ability to capture disconnected lane segments and understand the spatial extent of lane areas. The plane form representation allows the model to recognize that disconnected pixels may still belong to the same lane if they fall within the learned planar region, improving robustness to lane discontinuities and occlusions.
3Device complexity
If traditional lane detection methods are used, then the system complexity remains manageable, but the detection performance degrades in curved lanes or disconnected lane scenarios
Solution Approach 1:
The patent replaces traditional geometric lane modeling approaches (fitting straight lines or curved lines to detected points) with a data-driven plane-based representation learned directly from images. This substitution eliminates the need for complex post-processing geometric fitting and allows the model to naturally handle various lane configurations including curves and disconnected segments through the plane representation.
Data Source
AI summary
A method and an apparatus for detecting a lane is provided. The lane detection apparatus according to an embodiment includes: an acquisition unit configured to acquire a front image of a vehicle; and a processor configured to input the image acquired through the acquisition unit to an AI model, and to detect information of a lane on a road, and the AI model is trained to detect lane information that is expressed in a plane form from an input image. Accordingly, data imbalance between a lane area and a non-lane area can be solved by using the AI model which learns/predicts lane information that is expressed in a plane form, not in a segment form such as a straight line or curved line.