GPS-Adaptive Lane Type Classification for Cross-Region Detection
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Solution Overview
Problem
Current lane and road marking detection algorithms for autonomous driving are limited to specific environments and conditions, failing to function effectively in various weather conditions and regions, and require separate algorithms for different countries or regions.
Innovation Solution
A device and method using a camera, GPS receiver, and controller to adapt a classifier based on GPS information for lane type determination, enabling detection and classification of lanes and road markings, and transmitting control signals for autonomous vehicle operation, independent of regional limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a lane detection algorithm is designed for a specific region or weather condition, then detection accuracy is improved in that specific condition, but the algorithm cannot be applied to other regions or weather conditions
Solution Approach 1:
The patent implements a universal lane detection algorithm that can operate across different regions and weather conditions by using a standardized processing pipeline (Canny edge detection, Hough transform, polynomial fitting) that is not region-specific. The system adapts to different conditions through parameter adjustment rather than requiring separate algorithms for each region, making the system multi-functional and universally applicable.
Solution Approach 2:
The patent adjusts detection parameters such as polynomial coefficients, lane marking colors, and detection thresholds based on GPS location and weather conditions. By changing parameters rather than the fundamental detection algorithm, the system maintains high accuracy across different regions while using a single unified detection framework.
2Measurement precision
If separate lane detection algorithms are developed for different countries or regions, then detection accuracy is improved for each specific region, but device complexity and algorithm maintenance become more difficult
Solution Approach 1:
The patent employs a single unified detection algorithm that serves multiple regions and conditions, eliminating the need to maintain separate algorithms for different countries. This universal approach reduces device complexity while maintaining detection accuracy through parameter adaptation rather than structural complexity.
Solution Approach 2:
The patent merges multiple region-specific detection approaches into a single unified algorithm that handles all regions through parameter configuration. By combining what would otherwise be separate detection systems into one integrated solution, the patent reduces overall system complexity while preserving the ability to adapt to different regional characteristics.
3Measurement precision
If lane detection is limited to good weather conditions, then detection accuracy is maintained, but the system cannot operate in various weather conditions
Solution Approach 1:
The patent adjusts detection parameters based on weather conditions, including lane marking color thresholds, edge detection sensitivity, and polynomial fitting parameters. This allows the system to maintain detection accuracy in various weather conditions by adapting parameters rather than requiring different algorithms for different weather scenarios.
Solution Approach 2:
The patent implements a dynamic detection system that adapts its parameters in real-time based on detected weather conditions and environmental factors. Rather than being static and limited to good weather, the system dynamically adjusts its detection characteristics to maintain accuracy across varying weather conditions.
Data Source
AI summary
A device for determining lane type and method thereof are provided. The device for determining lane type according to an embodiment of the present disclosure includes a camera for acquiring an around view image around a vehicle, a GPS receiver for receiving GPS information, and a controller communicatively connected to the camera and the GPS receiver. Here, the controller is configured to recognize a scene of the image acquired by the camera, detect lanes and road markings from the recognized scene, comprise a classifier adapted based on the GPS information, classify the detected lanes and road markings by the classifier, and confirm a type of the classified lanes.