Lane Marker Detection Using Road Segmentation in Adverse Conditions
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Solution Overview
Problem
Existing lane detection systems in autonomous vehicles struggle to accurately identify lane markings in harsh or unfavorable environmental conditions such as night, dark conditions, or adverse weather like snow or heavy rain, which obscures lane markings.
Innovation Solution
A system and method that utilizes a camera to obtain images of the road, extracts road features, performs lane detection algorithms, and employs a detection enhancement module to enhance lane detection by incorporating road segmentation and trajectory information from nearby vehicles, using neural networks and confidence value arbitration to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lane detection algorithms are used in adverse environmental conditions, then the system complexity remains low, but the lane detection accuracy deteriorates due to obscured lane markings
Solution Approach 1:
The patent combines multiple data sources including images from cameras, road segmentation information, and trajectory data from nearby vehicles into a unified lane detection framework. This integration of multiple information streams enables accurate lane detection in adverse conditions by compensating for the limitations of individual sensors through data fusion
Solution Approach 2:
The patent segments the lane detection process into multiple independent modules: image processing module, road segmentation module, trajectory extraction module, and confidence value arbitration module. Each module processes specific aspects of the data independently, then their results are combined to achieve accurate lane detection while maintaining system modularity
2Measurement precision
If multiple data sources are combined to improve lane detection in adverse conditions, then the lane detection accuracy improves, but the computational resources and processing time increase
Solution Approach 1:
The patent implements confidence value arbitration to selectively process data from multiple sources based on detection confidence levels. When confidence is high, full processing is performed; when confidence is low or conditions are favorable, the system can reduce processing intensity or skip certain verification steps, optimizing the balance between accuracy and processing time
3Reliability
If road segmentation and trajectory information from nearby vehicles are incorporated, then the reliability of lane detection in harsh conditions improves, but the device complexity increases
Solution Approach 1:
The patent introduces road segmentation information and trajectory data as intermediary elements that mediate between raw sensor data and final lane detection results. These intermediaries provide contextual information about the road structure and vehicle behavior, enabling more reliable lane detection in adverse conditions while maintaining a structured processing architecture
Data Source
AI summary
A vehicle, and a system and method of navigating the vehicle. The system includes a camera and a processor. The camera obtains an image of a road upon which the vehicle is moving. The processor is configured to extract a feature of the road from the image, perform a lane detection algorithm to detect a set of lane markers in the road using the image and the feature, and move the vehicle along the road by tracking the set of lane markers.