Driver Analysis Learning Platform for Clustered LSTM Adaptation
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Solution Overview
Problem
Existing driving analysis methods fail to accurately account for individual driver characteristics, leading to incomplete and inaccurate driving metric outputs.
Innovation Solution
A computing platform that receives sensor data from vehicles, generates pattern deviation outputs, clusters these outputs to maximize variance, and trains long short term memory (LSTM) models for each cluster to verify consistency and modify the sensor data analysis model accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional driving analysis methods are used, then the analysis process is simple, but the accuracy of driving metrics is insufficient because individual driver characteristics are ignored
Solution Approach 1:
The patent segments drivers into distinct clusters based on their driving characteristics and patterns. By dividing the driver population into groups with similar behaviors, the system can apply customized analysis models to each segment, improving measurement precision while managing complexity through structured categorization
Solution Approach 2:
The system dynamically adapts analysis models to individual drivers through continuous learning. LSTM networks are trained on each driver's specific data patterns, allowing the system to evolve and personalize its approach over time, thereby improving accuracy without requiring static complex configurations for all drivers
2Adaptability or versatility
If customized analysis models for each driver are implemented, then the relevance of driving analysis is improved, but the computational resources and time required increase
Solution Approach 1:
The system performs preliminary clustering of drivers based on their characteristics before applying detailed customized analysis. This pre-grouping allows the system to prepare and train models in advance for each cluster, reducing the time required when individualized analysis is needed while maintaining high adaptability
Solution Approach 2:
The system changes the level of customization dynamically based on available resources and driver needs. By adjusting parameters such as cluster granularity and model complexity, the system can balance adaptability with computational efficiency, allowing customized analysis when resources permit while using more efficient methods when time is constrained
Data Source
AI summary
Aspects of the disclosure relate to enhanced processing systems for providing dynamic driving metric outputs using improved machine learning methods. A computing platform may receive sensor data from vehicle sensors. The computing platform may generate a pattern deviation output corresponding to an output of a sensor data analysis model, an actual outcome associated with a lowest TTC value, and driving actions that occurred over a prediction horizon corresponding to the pattern deviation output. The computing platform may cluster the pattern deviation outputs to maximize a ratio of inter-cluster variance to intra-cluster variance. The computing platform may train a long short term memory (LSTM) for each cluster, and may verify consistency of the pattern deviation outputs in the respective clusters. After verifying the consistency of the pattern deviation outputs in each cluster, the computing platform may modify the sensor data analysis model to reflect pattern deviation outputs associated with verified consistency.