Vehicle Location Context Inference via Spatio-Temporal Clustering
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Solution Overview
Problem
Existing vehicle control systems lack accuracy in identifying significant locations associated with a vehicle's owner or operator, relying on basic attributes which are not sufficient for precise location context inference, and fail to effectively utilize this information for personalized route generation and service identification.
Innovation Solution
The system employs spatio-temporal clustering and density-based clustering of vehicle probe data to identify regular patterns and habits, assigning contextual labels to locations based on temporal data, and using these labeled locations for vehicle operation and service provision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic attributes are used for location identification, then the system is simple to operate, but the measurement precision of location context inference is insufficient
Solution Approach 1:
The patent transitions from basic spatial attributes to spatio-temporal dimensions by incorporating temporal data (time of day, day of week, duration of stay) alongside spatial coordinates. This dimensional expansion enables more accurate location context inference through density-based spatio-temporal clustering, resolving the contradiction between precision and complexity by adding informative dimensions rather than increasing system complexity arbitrarily
Solution Approach 2:
The system changes the parameters used for location identification from simple spatial coordinates to comprehensive spatio-temporal parameters including arrival time, stay duration, and frequency of visits. These parameter transformations enable the density-based clustering algorithm to distinguish significant locations more accurately, achieving higher measurement precision through meaningful parameter selection
2Measurement precision
If spatio-temporal clustering is performed on vehicle probe data, then the measurement precision of location identification is improved, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary density-based clustering on temporal data associated with destinations to pre-identify spatio-temporal patterns before final location significance determination. This preliminary action organizes the data in advance, reducing the computational burden during real-time operation and minimizing time loss while maintaining high measurement precision
Solution Approach 2:
The patent creates spatio-temporal clusters as simplified representations or copies of complex vehicle probe data patterns. Instead of processing all raw probe data continuously, the system generates clustered representations that capture essential temporal patterns, enabling efficient processing while preserving the accuracy needed for significant location identification
Data Source
AI summary
A method, a vehicle and a system that identify and use a location significant to a person associated with a vehicle based on vehicle probe data are described. Vehicle transportation network information representing a vehicle transportation network is identified, the vehicle transportation network information including destinations of a vehicle obtained from vehicle probe data. For at least some of the destinations, density-based clustering is performed using temporal data associated with the at least some of the destinations to form at least two spatio-temporal clusters. A contextual label is assigned to a location associated with a first spatio-temporal cluster of the at least two spatial-temporal clusters based on the temporal data associated with the first spatio-temporal cluster, the location being a labeled location, and the labeled location is used for vehicle operation. The labeled location may be used for navigation or service, for example.