Ultrasonic Sensor Object Classification Using Region-Specific Models
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Solution Overview
Problem
Conventional ultrasonic sensor systems for mobile devices require a large number of signal characteristics for classification models, leading to high resource consumption and effort in training and application, as they need to account for various detection situations and environmental factors, resulting in inefficient object classification.
Innovation Solution
The method involves training and applying different classification models based on specific detection situations, using relevant subsets of signal characteristics, reducing dimensionality and resource consumption by masking out less relevant characteristics, and selecting the classification model based on the current detection situation to determine object properties with higher reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional ultrasonic sensor systems use a large number of signal characteristics for classification models to account for various detection situations and environmental factors, then the reliability of object classification is improved, but the resource consumption and effort in training and application increase significantly
Solution Approach 1:
The patent divides the detection situations into different regions (e.g., front, rear, left, right) and trains separate classification models for each region. Each regional model uses only the signal characteristics relevant to that specific region, rather than using all possible characteristics across all regions. This segmentation reduces the dimensionality of input data for each model while maintaining comprehensive coverage of all detection situations through the ensemble of regional models.
Solution Approach 2:
The patent applies local quality by tailoring the signal characteristics and model parameters to each specific detection region. For example, the front region model may focus on characteristics related to forward motion and front-facing obstacles, while the rear region model emphasizes characteristics relevant to backward detection. This localized approach ensures that each model processes only the most relevant features for its specific spatial context, reducing overall system complexity.
2Measurement precision
If conventional ultrasonic sensor systems use a large number of signal characteristics for classification models, then the accuracy in distinguishing collision-relevant objects is improved, but the training database creation effort and processing time increase
Solution Approach 1:
By segmenting the classification task into region-specific models, the patent reduces the amount of training data and computational resources needed for each individual model. Each regional model trains on a subset of data relevant to that region, rather than requiring the entire dataset. This parallelization of training across multiple smaller models significantly reduces total training time while maintaining detection accuracy through the combination of regional results.
Solution Approach 2:
The patent extracts and uses only the signal characteristics that are relevant to each specific detection region, discarding or excluding characteristics that are not applicable to that region. For example, characteristics related to lateral movement are extracted and used only for side-region models, while front-region models use characteristics related to forward motion. This extraction of relevant features reduces processing time and computational load.
3Adaptability or versatility
If conventional ultrasonic sensor systems use all signal characteristics for all detection situations, then the adaptability to different environmental factors is improved, but the resource consumption during operation increases
Solution Approach 1:
The patent segments the signal characteristics and processes only the subset relevant to the current detection region. When an object is detected in the front region, the system activates only the front-region model and processes only front-relevant characteristics, rather than processing all possible characteristics. This segmentation maintains adaptability to different detection situations while minimizing energy consumption by avoiding unnecessary processing of irrelevant features.
Solution Approach 2:
The patent implements a dynamic selection mechanism that adapts the active classification model based on the current detection situation. The system dynamically switches between different regional models depending on where objects are detected, and potentially adjusts which signal characteristics are processed based on the current operational context. This dynamic adaptation ensures energy efficiency by processing only what is necessary for the current situation while maintaining versatility across all possible detection scenarios.
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
This approach significantly reduces the effort in creating training databases and improves the reliability of object classification by using the classification result with the highest quality specification, ensuring efficient and accurate identification of collision-relevant environment objects.
Implementation Method 1
ultrasonic sensor systems for object detection. These often have a plurality of ultrasonic sensor devices, each having a plurality of ultrasonic transducers
Data Source
AI summary
A method for operating an ultrasonic sensor system having an ultrasonic sensor device for determining an object property of an environment object, in which classification models for determining classification results which specify a modeled object property of the environment object are provided for a plurality of detection situations of the environment object, each classification model trained for evaluation with a different subset of signal characteristics extracted from ultrasonic reception signals to provide a classification result of the corresponding classification model and an associated quality specification, includes: detecting the ultrasonic reception signals; determining the quantity of signal characteristics from the ultrasonic reception signals; determining classification results and associated quality specifications by evaluating the classification models with the corresponding subset of the quantity of the signal characteristics on the basis of the detection situation; and determining the object property based on the classification results and associated quality specifications.