Road Lane Classification Using CNNs for Unmarked Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately determining lane classifications on roads, particularly when manual designations in maps are inaccurate or lacking, leading to inefficiencies in processing resources and time, and the inability to navigate roads without physical markings.
Innovation Solution
A method and system that utilize a computer system with processors to receive and process image data from roads, employing convolutional neural networks to determine lane classifications based on lane markings and attributes, generating accurate lane classification data for autonomous vehicle navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual designations are used for lane classifications in maps, then lane classification data can be obtained, but accuracy deteriorates when markings are absent or incorrect
Solution Approach 1:
The system enables roads to classify their own lanes automatically through image processing and machine learning algorithms, eliminating dependence on manual map designations. The convolutional neural network analyzes road images directly to determine lane classifications, allowing the system to self-determine accurate lane types even for unmarked roads.
Solution Approach 2:
The patent replaces manual mechanical designation processes with automated optical image processing and computational algorithms. Instead of relying on human-maintained map data, the system uses computer vision technology to automatically extract and classify lane information from road images.
2Measurement precision
If comprehensive image processing is performed to determine lane classifications, then navigation accuracy improves, but processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-processing images to identify and extract only the relevant lane marking features before classification. The convolutional neural network is trained to focus on critical visual patterns, performing feature extraction and filtering in advance to reduce the computational burden during actual lane classification operations.
Solution Approach 2:
The image processing is divided into segmented stages: initial image acquisition, feature detection, lane marking identification, and final classification. This segmentation allows the system to process only relevant portions of the image data at each stage, reducing overall processing time while maintaining accuracy.
3Ease of operation
If physical lane markings are required for classification, then lane identification becomes straightforward, but roads without markings cannot be navigated
Solution Approach 1:
The system achieves universality by being capable of handling multiple road types through a single unified approach. The convolutional neural network is trained to recognize both marked and unmarked roads, allowing the same system to process diverse road conditions without requiring separate specialized algorithms for each type.
Solution Approach 2:
Instead of requiring physical markings to enable classification, the system inverts the approach by using the absence or presence of markings as one of several classification criteria. The system can classify lanes based on road geometry, position, and contextual features even when traditional markings are absent, turning the limitation into a valid classification scenario.
Data Source
AI summary
A computer system including one or more processors programmed or configured to receive image data associated with an image of one or more roads, where the one or more roads comprise one or more lanes, determine a lane classification of the one or more lanes based on the image data associated with the image of the one or more roads, and provide lane classification data associated with the lane classification of the one or more lanes.