Adaptive Navigation System Using Personalized Attribute Models
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Solution Overview
Problem
Conventional navigation systems are not adaptive enough, failing to consider changes over time, multiple route attributes, and individual user preferences, leading to repetitive mistakes and frustration.
Innovation Solution
The development of an adaptive navigation system that generates attribute models based on user driving history, using explicit and implicit conditions to learn and adjust route recommendations, incorporating road speed, familiarity, safety, and other attributes, and employing probabilistic modeling to predict travel times and route desirability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional navigation systems use standardized map data and fixed routing algorithms, then system complexity is reduced and ease of operation is improved, but adaptability to individual user preferences and changing conditions deteriorates
Solution Approach 1:
The patent segments the routing decision-making process into multiple independent attribute models (e.g., road speed model, road familiarity model, road safety model, user preference model). Each model evaluates specific aspects of route quality separately, allowing the system to maintain complexity in a modular way while improving adaptability. The route generator combines these segmented models to produce final routing decisions.
Solution Approach 2:
The patent implements dynamic attribute models that continuously learn and adapt to changing conditions. The system updates road speed estimates based on real-time traffic data and historical patterns, adjusts road familiarity based on repeated user exposure, and modifies routing decisions based on explicit user feedback (re-routes accepted or rejected). This dynamic adaptation improves versatility without requiring complete system redesign.
2Adaptability or versatility
If navigation systems aggregate data from all users to improve route recommendations, then representative coverage is improved, but individual user preferences and personal driving history are lost
Solution Approach 1:
The patent applies local quality by creating personalized attribute models for each individual user rather than using a single aggregated model. Each user develops their own road speed estimates, familiarity levels, and preference patterns based on their specific driving history. The system maintains these local, user-specific models while optionally incorporating aggregated traffic data from other sources, thus preserving individual preferences while benefiting from collective data.
Solution Approach 2:
The patent uses an intermediary layer of attribute models that translate raw driving data into personalized routing decisions. These models act as mediators between aggregated traffic information and individual user needs, filtering and adapting general data to match personal preferences. The system can incorporate anonymized aggregate data while maintaining user-specific models that preserve individual characteristics.
3Productivity
If navigation systems consider multiple route attributes simultaneously (speed, safety, familiarity, etc.), then route optimization is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the evaluation of multiple route attributes into separate, specialized models. Instead of one complex optimization algorithm considering all factors simultaneously, the system uses distinct models for road speed, road safety, road familiarity, and user preferences. Each model focuses on specific attributes, reducing the computational burden of evaluating each individual factor while maintaining the ability to consider all factors in the final routing decision.
Solution Approach 2:
The patent implements partial action by allowing the route generator to selectively weight or emphasize certain attribute models based on current conditions and user preferences. Not all attributes need to be equally considered in every situation - the system can prioritize speed during commutes, safety in adverse conditions, or familiarity on weekends. This selective approach reduces computational complexity while maintaining high route optimization quality when needed.
Data Source
AI summary
Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.