ML Object Recognition Training with Reflection Factors
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Solution Overview
Problem
Current autonomous vehicle systems lack the capability for fully autonomous operation due to inadequate object recognition technology, relying on human intervention and requiring manual labor for training, which limits their ability to distinguish between objects with similar reflection and absorption characteristics.
Innovation Solution
A method for training a machine learning system using sensing element data that incorporates signal intensities and reflection characteristics, allowing for improved object recognition by distinguishing between objects like trees, lamp posts, metallic surfaces, and people through 'static learning' and feature maps, even in varying weather conditions and dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object recognition methods are used without incorporating reflection and absorption factors, then the system is simpler to implement, but the object recognition accuracy deteriorates when distinguishing between objects with similar visual characteristics
Solution Approach 1:
The patent applies preliminary action by pre-collecting and storing reflection and absorption factors for various objects in a database before actual object recognition occurs. During training, the machine learning system accesses this pre-prepared data to learn characteristic patterns, enabling more accurate differentiation without increasing real-time processing complexity.
Solution Approach 2:
The patent introduces reflection and absorption factors as intermediary parameters that mediate between raw sensor data and object classification. These factors serve as additional features that the machine learning system uses to distinguish objects, effectively acting as a bridge that enhances discrimination capability without requiring direct modification of the core recognition architecture.
2Extent of automation
If manual labeling and training data preparation is used, then the training process requires significant human labor, but the system can achieve reasonable initial performance
Solution Approach 1:
The patent implements self-service by enabling the machine learning system to automatically utilize reflection and absorption factor data from the database during training without requiring manual annotation of each training sample. The system autonomously extracts relevant features and performs classification, significantly reducing the need for human intervention in the training process.
Solution Approach 2:
By pre-compiling reflection and absorption factor databases for various objects, the patent eliminates the need for time-consuming manual labeling during the training phase. This preliminary preparation of reference data allows the training process to proceed more quickly and with less human labor while maintaining or improving accuracy.
3Adaptability or versatility
If the system only uses basic sensor data without reflection characteristics, then the processing is faster and simpler, but the system cannot distinguish between objects like trees, lamp posts, and metallic surfaces
Solution Approach 1:
The patent applies universality by creating a unified machine learning framework that processes both basic sensor data and reflection/absorption factor data through the same architecture. This multi-functional system can handle various object types (trees, lamp posts, metallic surfaces) using a single trained model, eliminating the need for separate processing pipelines for different object categories.
Solution Approach 2:
Reflection and absorption factors serve as intermediary features that enable the system to differentiate between object types with similar visual appearances. These additional parameters provide supplementary information that helps the machine learning system distinguish between objects like trees, lamp posts, and metallic surfaces without requiring fundamentally different processing approaches.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances object recognition accuracy and reliability in autonomous vehicles by enabling the system to learn and differentiate between various surfaces and objects effectively, improving safety and reducing the need for manual labor in training processes.
Implementation Method 1
a method of training a machine learning system for an object recognition device, wherein sensing element data are provided; and a machine learning system is trained, using the provided sensing element data; at least one object being recognized from the sensing element data; signal intensities of the sensing element data being used together with a reflection and/or absorption factor associated with the object
Data Source
AI summary
A method of training a machine learning system for an object recognition device. The method includes: providing sensing element data; and training a machine learning system, using the provided sensing element data; at least one object being recognized from the sensing element data; and signal intensities of the sensing element data being used together with a reflection and/or absorption factor associated with the object.