Telematics Route Learning via Pattern Recognition
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Solution Overview
Problem
Existing telematics systems require manual configuration of commute routes on a provider's website for retrieving traffic congestion information, which is inconvenient and does not automatically adapt to the user's learned travel patterns.
Innovation Solution
A commuter route learning program that automatically learns common travel routes through a Learn Mode, recognizes patterns in trips during Pattern Recognition Mode, and provides traffic reports through a voice activation system in Execution Mode, allowing for automatic traffic information retrieval without prior configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of commute routes is required on provider's website, then traffic congestion information can be retrieved, but user convenience deteriorates and operation complexity increases
Solution Approach 1:
The system automatically learns and identifies commute routes by monitoring vehicle trips and analyzing travel patterns without user intervention. The telematics device autonomously configures routes based on learned data, eliminating the need for manual website configuration while maintaining reliable traffic information retrieval
Solution Approach 2:
The system performs preliminary route learning and pattern recognition before traffic information is needed. By continuously monitoring trips and pre-configuring routes based on learned patterns, the system prepares route data in advance, ensuring reliable traffic information retrieval without requiring manual setup at the time of use
2Reliability
If manual configuration is required, then route-specific traffic information can be obtained, but system adaptability to user travel patterns deteriorates
Solution Approach 1:
The system continuously monitors vehicle trips and uses the collected data to refine and update route patterns. By implementing feedback loops that analyze actual travel behavior and adjust learned routes accordingly, the system adapts to changing user travel patterns while maintaining reliable route-specific traffic information
Solution Approach 2:
The system transitions from static manual configuration to dynamic automatic learning. Routes are continuously updated based on real-time trip data and evolving travel patterns, allowing the system to adapt flexibly to changes in user behavior while maintaining accurate traffic information for current routes
3Reliability
If manual route configuration is required, then traffic reports can be retrieved, but time consumption for setup increases
Solution Approach 1:
The system automatically performs route configuration and setup without requiring user time investment. The telematics device autonomously learns routes, categorizes them as commute patterns, and configures traffic monitoring, eliminating setup time while maintaining reliable traffic report retrieval
Solution Approach 2:
The system performs all configuration steps preliminarily and automatically during the route learning phase. By completing route setup, pattern recognition, and traffic information configuration in advance without user intervention, the system eliminates setup time while ensuring reliable traffic reports are ready when needed
Data Source
AI summary
A commuter route learning program learns a telematics subscriber's common travel routes (e.g. work-home, home-school, etc.) and offers traffic reports based on the routes. In one aspect, three modes are used to establish routes and offer traffic reports. These modes include a Learn Mode during which the commuter route learning program learns new commuter trips, a Pattern Recognition Mode during which the commuter route learning program recognizes and categorizes routes from patterns of trips, and an Execution Mode during which the commuter route learning program automatically provides commuter route traffic congestion information to a subscriber.