Context-Sensitive Navigation Routing Using In-Vehicle Sensor Data
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Solution Overview
Problem
Current navigation systems fail to provide personalized routing and guidance tailored to individual users' unique contexts and preferences, leading to user dissatisfaction and abandonment of navigation services.
Innovation Solution
A system that collects in-vehicle sensor data to determine contextual parameters, such as passenger presence and trip intentions, and adjusts routing factors like efficiency, resilience, safety, and point-of-interest discovery to offer context-sensitive navigation routes and guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If navigation systems provide generic routing without personalization, then device complexity is reduced, but user satisfaction deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting sensor data and determining user contexts in advance of route calculation. Contextual parameters such as passenger presence, time of day, and trip purpose are determined before routing decisions are made, enabling personalized navigation without adding complexity to the core routing algorithm.
Solution Approach 2:
The navigation system serves itself by automatically determining user contexts and selecting appropriate routing cost factors without requiring manual user input. The system self-adjusts routing preferences based on detected contexts such as whether passengers are present or what time it is, eliminating the need for complex user interface interactions.
2Measurement precision
If navigation systems collect and process sensor data to determine user context, then routing accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial action by selectively determining only the most relevant contextual parameters needed for routing decisions. Rather than analyzing all possible sensor data, the system focuses on key factors such as passenger presence and time of day that have the greatest impact on routing preferences, reducing processing overhead while maintaining accuracy.
3Adaptability or versatility
If navigation systems adjust routing based on multiple contextual factors, then user satisfaction is improved, but computational requirements increase
Solution Approach 1:
The system applies local quality by adjusting routing cost factors specifically for different contextual scenarios rather than recalculating entire routing algorithms. Each context (e.g., passengers present, time of day) triggers selective adjustments to specific cost factors like safety or efficiency, reducing computational requirements while maintaining context sensitivity.
Data Source
AI summary
An approach is provided for context sensitive routing. The approach, for example, involves collecting sensor data from one or more in-vehicle sensors of a vehicle. The approach also involves processing the sensor data to determine one or more contextual parameter signals associated with the vehicle, one or more passengers of the vehicle, or a combination thereof. The approach further involves determining a context associated with a trip engaged by the vehicle or the one or more passengers based on the one or more contextual parameter signals. The approach further involves determining a routing cost factor based on the context and then determining a navigation route, navigation guidance information, or a combination thereof based on the routing cost factor. The approach further involves providing the navigation route, the navigation guidance information, or a combination thereof as an output.