Time Series Grammar Discovery for Scalable Vehicle Motion Prediction
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Solution Overview
Problem
Current time series analysis methods using context-free grammar are inefficient due to their cubic-time complexity, limiting the analysis to short-length data, and lack automatic grammar extraction, making them unsuitable for scalable applications.
Innovation Solution
A system that automatically extracts a subset of probabilistic context-free grammar (PCFG) from time series data using automatic segmentation, converting raw data into symbolic form and applying head-grammar rules to improve efficiency and scalability, specifically for analyzing moving vehicle data to predict vehicle movement and control autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unconstrained context-free grammar is used for time series analysis, then grammar extraction capability is improved, but computational complexity increases to cubic time
Solution Approach 1:
The patent extracts and applies only the necessary subset of grammar rules from the full context-free grammar, rather than using the complete grammar framework. This selective extraction reduces computational complexity while maintaining the essential grammar extraction capability for time series analysis.
Solution Approach 2:
The patent segments the time series data into discrete symbols through automatic segmentation before applying grammar rules. This segmentation transforms continuous time series data into a discrete sequence that can be processed more efficiently by grammar-based algorithms, reducing the overall computational burden.
2Adaptability or versatility
If unconstrained context-free grammar is used for time series analysis, then grammar extraction capability is improved, but analysis is limited to short-length data
Solution Approach 1:
The patent applies automatic segmentation to divide long time series data into discrete symbolic sequences. This transformation enables the grammar-based algorithm to process much longer data sequences by working with compressed symbolic representations rather than raw continuous data, thereby extending the analysis capability to long-length data.
3Measurement precision
If hand-crafted grammar is used for time series analysis, then analysis accuracy is improved, but automation is reduced
Solution Approach 1:
The patent implements automatic grammar extraction from time series data without requiring manual hand-crafting of grammar rules. The system autonomously learns and extracts relevant grammar rules directly from the data, achieving both high automation and maintained analysis accuracy through data-driven grammar discovery.
Data Source
AI summary
Described is a system for analyzing time series data. A sequence of symbols is generated from a set of time series input data related to a moving vehicle using automatic segmentation. A grammar is extracted from the sequence of symbols, and the grammar is a subset of a probabilistic context-free grammar (PCFG). Using the grammar, time series input data can be analyzed, and a prediction of the vehicle's movement can be made. Vehicle operations for an autonomous vehicle are determined using the prediction.