Vehicle Parameter Prediction With Clustered Sensor Data
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Solution Overview
Problem
Existing methods for predicting vehicle-specific parameters are inefficient, costly, and lack accuracy, particularly when the relationship between sensor data and the parameter is indirect, requiring high training effort for each new variable.
Innovation Solution
A method involving unsupervised clustering, tokenization, and a trained prediction-specific model using a feature extraction network to convert sensor data into a numerical representation, enabling flexible and accurate prediction of vehicle-specific parameters without direct causal connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learned models specifically adapted to each variable are used to calculate indirectly determinable parameters, then prediction accuracy can be improved, but training effort increases significantly for each new variable
Solution Approach 1:
The patent applies universality by creating a single multi-purpose model that can predict multiple different vehicle-specific parameters (such as driver identity, road surface type, weather conditions) using the same trained feature extraction network. This eliminates the need to train separate machine-learned models for each parameter, thereby maintaining high prediction accuracy across multiple variables while significantly reducing the training effort and time required compared to creating specialized models for each parameter individually.
Solution Approach 2:
The patent applies preliminary action by performing unsupervised clustering and creating a comprehensive vocabulary representation of sensor data in advance. This pre-processing creates a universal numerical representation that captures essential patterns in the data before any specific prediction task. The feature extraction network is trained on this pre-processed data structure, enabling it to efficiently adapt to different prediction targets without retraining from scratch, thus reducing training effort for new parameters while maintaining accuracy.
2Adaptability or versatility
If multiple different vehicle-specific parameters are predicted using separate models, then comprehensive parameter coverage is achieved, but system complexity and cost increase
Solution Approach 1:
The patent implements universality by designing a single prediction system that handles multiple vehicle-specific parameters through one unified feature extraction network. The system processes various parameters including driver identity, road surface characteristics, weather conditions, and vehicle dynamics using the same underlying model architecture. This approach achieves comprehensive parameter coverage while avoiding the complexity and cost of deploying and maintaining multiple separate prediction models.
Solution Approach 2:
The patent applies merging by combining the prediction functionality for multiple parameters into a single integrated system. Instead of having separate models for each parameter type, the system merges them into one unified prediction framework that uses shared feature extraction and processing pipelines. This consolidation reduces system complexity, lowers computational costs, and simplifies deployment while maintaining the ability to predict diverse vehicle-specific parameters.
3Measurement precision
If sensor data is processed with detailed clustering and tokenization, then prediction precision improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies extraction by using unsupervised clustering to identify and extract the most significant patterns and features from raw sensor data. The clustering process groups similar data points together, and the tokenization algorithm converts these clusters into a compact numerical representation. This extraction approach captures the essential information needed for accurate predictions while discarding redundant details, thereby maintaining high prediction precision while reducing the computational burden and processing time compared to analyzing all raw sensor data in detail.
Data Source
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AI summary
The invention relates to a method for predicting a vehicle-specific parameter (100).The method comprises acquiring sensor data (110) by sensors (20) designed to record vehicle-specific measured values, converting sensor data (110) into a numerical representation (120), wherein the vehicle-specific measured values are assigned to several clusters (115) using an unsupervised clustering method, and the clusters (115) are converted into a sequence of cluster IDs, the cluster IDs are encoded, and a vocabulary is created by a tokenization algorithm, each vocabulary item corresponding to a coded sequence (130), and feeding the numerical representation (120) to at least one trained prediction-specific model (140) designed to output a prediction of a vehicle-specific parameter (100), wherein the trained prediction-specific model (140) uses a feature extraction network (150) that was trained with stored sensor data (160) in the numerical representation (120).Furthermore, the invention relates to a method for creating a model for predicting a vehicle-specific parameter (100), a computer program product and a system.