Vehicle Camera Learning for Environmental Condition Prediction
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Solution Overview
Problem
Existing ADAS systems for predicting environmental conditions, such as rain and light, often exhibit margins of error, requiring driver intervention to override the system, leading to reduced reliability and safety.
Innovation Solution
A method for automated supervised training of a learning algorithm using image data from a vehicle's camera, combined with sensor and user input signals, to improve the detection and prediction of environmental conditions, reducing the need for manual overrides.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional environmental sensors are used to detect rain and light conditions, then the system can provide basic environmental condition detection, but the detection precision and reliability are insufficient leading to margin of error
Solution Approach 1:
The patent replaces traditional mechanical/environmental sensors with a camera-based vision system combined with machine learning algorithms. The camera captures visual information about environmental conditions (rain, light, weather), and the trained learning algorithm processes this visual data to detect and classify environmental conditions with higher precision and reliability than traditional sensors.
Solution Approach 2:
The patent changes the detection parameter from direct physical measurement (using environmental sensors) to visual feature analysis (using camera images). By training the learning algorithm on labeled image data, the system learns to recognize environmental conditions through visual parameters such as rain streak patterns, light intensity distribution, and weather-related visual features, achieving superior detection precision.
2Ease of manufacture
If traditional environmental sensors are used, then the system structure is simple, but dedicated sensors increase production costs and reduce scalability
Solution Approach 1:
The patent makes the camera device universal by enabling it to perform multiple functions: capturing images for environmental condition detection, providing visual data for training the learning algorithm, and potentially serving other vehicle functions. This eliminates the need for dedicated environmental sensors, reducing production costs while improving scalability since the same camera hardware serves multiple purposes.
Solution Approach 2:
The patent merges the environmental condition detection function with the existing camera system. Instead of adding separate dedicated sensors, the solution combines environmental detection capabilities with the camera's primary imaging function, processing visual data through a trained learning algorithm to achieve both cost reduction and enhanced versatility.
3Extent of automation
If automated sensor-based systems are used for environmental detection, then the system operates autonomously, but driver intervention is still required to override margin of error
Solution Approach 1:
The patent implements a feedback mechanism where the learning algorithm continuously processes camera images and compares detected environmental conditions with expected patterns. The system provides feedback to the driver through the user interface, allowing confirmation or correction of detected conditions. This feedback loop enhances reliability by reducing false detections while maintaining high automation levels.
Solution Approach 2:
The patent performs preliminary action by training the learning algorithm in advance using large datasets of labeled images. This pre-training ensures that when the system operates autonomously, the algorithm is already optimized for accurate environmental condition detection, minimizing the need for driver intervention and reducing margin of error before actual use.
Data Source
AI summary
The present disclosure relates to methods and systems for generating a trained learning algorithm configured to predict the presence of environmental conditions in image data, a vehicle for utilizing the algorithm, as well as methods and systems for generating the training data for the automated supervised training of a learning algorithm. In some embodiments the training data is in the form of labelled images generated by a camera device arranged on a vehicle, where the labels are indicative of an environmental sensor activation or deactivation signal, or a user-input signal which serve as a supervisory signal for the training of the learning algorithm.