Vehicle Weather Detection Using Image and Map Classification
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Solution Overview
Problem
Existing vehicle management systems struggle with inaccurate classification of road and weather conditions due to conflicting and similar image features, leading to inefficient and error-prone decision-making in managing fleets during inclement weather, and lack precise classification methods that are time-consuming and expensive to implement.
Innovation Solution
A machine learning model, such as a condition prediction model, is trained using a selective image sampling process to enhance accuracy in classifying road and weather conditions, employing a deep neural network and unified map data for improved confidence in predictions, and implementing active measures based on these classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image classification methods are used to classify road and weather conditions, then the system can process images quickly, but the classification accuracy is low due to conflicting and similar image features
Solution Approach 1:
The patent segments the image classification task into multiple specialized classifiers, each trained to detect specific road conditions (wet road, snowy road, icy road) and weather conditions (rain, snow, fog) independently. This segmentation allows each classifier to focus on specific features, improving overall accuracy while maintaining efficient processing through parallel evaluation of multiple classification results.
Solution Approach 2:
The patent introduces map data as an intermediary to resolve conflicts between image-based classifications. When image classifiers produce conflicting results (e.g., image suggests wet road but map data shows clear conditions), the system uses map data as a mediator to determine the final classification, thereby improving accuracy without requiring additional image processing time.
2Reliability
If precise classification methods are implemented to improve accuracy in classifying road and weather conditions, then the reliability of fleet management decisions improves, but the system becomes more complex and expensive to implement
Solution Approach 1:
The patent merges multiple data sources (image data from cameras, map data from geographic information systems, and weather data) into a unified classification system. By combining these diverse data sources, the system achieves high reliability in road and weather condition classification without requiring any single component to be overly complex, distributing the computational burden across multiple simpler modules.
Solution Approach 2:
The patent creates a universal classification system that handles multiple road conditions (wet, snowy, icy) and weather conditions (rain, snow, fog) using the same architectural framework. This multi-functional approach improves reliability across different conditions while avoiding the need for separate specialized systems, thereby reducing overall system complexity.
3Measurement precision
If multiple data sources are integrated to improve classification accuracy, then the precision of road and weather condition detection improves, but the device complexity increases
Solution Approach 1:
The patent segments the data integration process into distinct modules: image data processing module, map data processing module, and weather data processing module. Each module independently processes its data type and produces classification results, which are then combined. This segmentation improves detection precision by allowing specialized processing for each data type while reducing integration complexity through modular architecture.
Solution Approach 2:
The patent implements feedback mechanisms where classification results from different data sources are continuously compared and refined. When image classification and map data classification agree, confidence increases; when they conflict, the system uses feedback loops to resolve discrepancies by weighting reliable sources higher, thereby improving precision without requiring complex real-time integration of all data streams simultaneously.
Data Source
AI summary
The present application discloses a method, system, and computer system for detecting inclement weather driving conditions. The method includes obtaining an image captured by a camera mounted to a vehicle, determining a classification for road and weather conditions using a condition prediction model to analyze the image, in response to determining that the classification for road and weather conditions matches a particular predefined road and weather classification, determining an active measure associated with the particular predefined road and weather classification, and causing the active measure to be performed.