Lane Marker Detection via End-to-End CNN and Horizontal Reduction
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Solution Overview
Problem
Existing lane detection systems in vehicles are slow, require significant manual configuration, and lack robustness across various driving scenarios, making them unsuitable for modern vehicles with advanced driver assistance systems.
Innovation Solution
An efficient end-to-end lane marker detection architecture that directly predicts lane marker vertices using a convolutional neural network, reducing computational complexity by modeling lane markers as a row-wise classification task and utilizing horizontal reduction modules to compress spatial information, without costly post-processing steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing lane detection techniques are used, then lane detection can be performed, but the system is slow and requires significant manual configuration
Solution Approach 1:
The system performs self-configuration by automatically adapting to different driving scenarios and lane marker types without requiring manual setup. The neural network model learns optimal detection parameters during training and adjusts automatically during operation, eliminating the need for manual configuration while maintaining high detection speed.
Solution Approach 2:
The system dynamically adjusts detection parameters based on input image characteristics and driving conditions. By changing parameters such as detection thresholds, region of interest boundaries, and model configuration automatically, the system achieves both high speed and adaptability without manual intervention.
2Reliability
If existing lane detection techniques are used, then lane detection can be performed, but the system lacks robustness across many driving scenarios
Solution Approach 1:
The detection system is divided into specialized components including shared backbone features, lane-specific heads for different lane marker types (solid, dashed, curved), and scenario-specific processing modules. This segmentation allows each component to be optimized for specific tasks while maintaining overall system robustness and efficient parallel processing.
Solution Approach 2:
The system employs a universal neural network architecture that can detect multiple types of lane markers (solid lines, dashed lines, curved lines, construction zones) using a single trained model. This multi-functional approach ensures robustness across diverse driving scenarios while maintaining fast detection speed through unified processing.
3Measurement precision
If traditional lane detection methods with post-processing are used, then accurate lane markers can be detected, but computational complexity and time increase
Solution Approach 1:
The system extracts and removes the computationally expensive post-processing steps from the detection pipeline. By directly outputting final lane marker parameters from the neural network without requiring subsequent geometric processing or validation steps, the system maintains high accuracy while significantly reducing computational time.
Solution Approach 2:
Traditional mechanical post-processing algorithms are replaced with a learned neural network model that directly predicts lane marker parameters. This substitution transfers the detection logic from explicit algorithmic processing to implicit learned patterns, achieving both high accuracy and speed through end-to-end learning.
Data Source
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AI summary
Certain aspects of the present disclosure provide a method for lane marker detection, including: receiving an input image; providing the input image to a lane marker detection model; processing the input image with a shared lane marker portion of the lane marker detection model; processing output of the shared lane marker portion of the lane marker detection model with a plurality of lane marker-specific representation layers of the lane marker detection model to generate a plurality of lane marker representations; and outputting a plurality of lane markers based on the plurality of lane marker representations.