Vehicle Speech Interface Context Tiling for Destination Disambiguation
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Solution Overview
Problem
Existing mapping systems lack the ability to provide location-dependent information effectively, especially when users interact with the system using a speech interface, as they often rely on limited data and struggle to disambiguate speech based on the user's location.
Innovation Solution
The use of context data, referred to as unified destination entry data (UDE data), which is partitioned into tiles that are loaded dynamically based on the user's location, allowing the speech interface to provide location-specific information and disambiguate speech interactions effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all map data and context data are loaded into memory, then the speech interface can provide comprehensive location-dependent information, but memory usage becomes excessively high
Solution Approach 1:
The patent divides the large map data and context data into smaller spatial units called tiles. Each tile contains data for a specific geographic region, allowing the system to load only the tiles relevant to the user's current location and nearby areas, rather than loading all data into memory simultaneously.
Solution Approach 2:
The system prioritizes loading high-quality, detailed context data for the user's immediate location (local tile) and surrounding neighborhoods, while using lower-detail or no data for distant regions. This ensures the speech interface has comprehensive information where needed most while conserving memory for less critical areas.
2Quantity of substance
If context data is partitioned into tiles and loaded dynamically, then memory usage is reduced, but the system complexity increases
Solution Approach 1:
The system dynamically determines which tiles to load based on the user's current location, movement velocity, and interaction context. Tiles are automatically loaded into memory when needed and unloaded when no longer relevant, creating a dynamic data management system that adapts to changing user needs without manual intervention.
Solution Approach 2:
The tile management system operates autonomously, automatically selecting, loading, and unloading tiles based on predefined criteria such as user location, velocity, and interaction type. This self-service mechanism reduces the need for complex manual data management while maintaining efficient memory usage.
3Adaptability or versatility
If user velocity is considered for tile replacement, then the system adapts to moving users effectively, but the decision-making logic becomes more complex
Solution Approach 1:
The system uses user velocity as a key parameter to determine tile replacement behavior. When velocity is high (indicating the user is in a moving vehicle), the system loads tiles for areas along the predicted path of travel. When velocity is low (user is stationary or walking), the system loads tiles for the immediate surrounding area. This parameter-based approach simplifies the decision logic while maintaining high adaptability to different user scenarios.
Data Source
AI summary
In an infotainment system of a vehicle, a mass storage unit stores map data and context data. The map data comprises geographic information about a geographic area around the vehicle and the context data comprises location-dependent context information about the geographic area for use when engaging in speech interaction with an occupant of the vehicle. The context data includes subsets of context data, each of which is pertinent to a different geographic area. A memory manager copies these subsets of context data into a dynamic memory based on the vehicle's movement.