Vehicle Contextual Search with Gesture-Target Correlation
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Solution Overview
Problem
Existing vehicle search systems fail to account for contextual aspects beyond simple search terms, leading to inaccurate and irrelevant search results.
Innovation Solution
A search system that integrates contextual cues by acquiring data from sensors, including external and internal vehicle environments, occupant information, and personal electronic devices, and correlates gestures with the surroundings to construct context-based search queries using neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If map-based searching uses rigid processes with simple search terms, then the searching process is simple and fast, but the search results are not accurate or relevant to the search context
Solution Approach 1:
The system performs preliminary actions by collecting contextual data (sensor data, occupant information, vehicle data, personal device data) before executing the search. This pre-collection of context information enables more accurate search results without adding complexity to the actual search execution process.
Solution Approach 2:
The system introduces an intermediary layer (contextual processing module) that bridges the simple search term input and the search results. This intermediary processes contextual cues from multiple data sources and integrates them with the search query, improving accuracy while keeping the user interface simple.
2Adaptability or versatility
If the search system collects and processes multiple contextual data sources, then the search results become more relevant, but the system complexity increases
Solution Approach 1:
The system implements a universal contextual processing framework that handles multiple data sources (sensor data, occupant data, vehicle data, personal device data) through a single integrated process. This multi-functional approach allows the system to adapt to different search contexts without requiring separate processing paths for each data type.
Solution Approach 2:
The system dynamically adjusts search parameters based on contextual data analysis. By changing search parameters (such as location radius, time constraints, category filters) based on processed contextual cues, the system achieves high adaptability while maintaining a consistent processing architecture.
3Loss of information
If the system integrates gesture detection with search queries, then the search becomes more contextually relevant to the occupant's intent, but the detection and measurement difficulty increases
Solution Approach 1:
The system replaces complex mechanical gesture analysis with optical and sensor-based detection methods. By using cameras and sensors to capture gesture data and processing it through image recognition and spatial analysis algorithms, the system reduces the difficulty of detecting and measuring gestures while improving intent accuracy.
Solution Approach 2:
The system creates a digital representation (copy) of the gesture in virtual space by transforming the physical gesture into a digital projection and overlaying it onto the surrounding environment data. This copying approach simplifies the correlation process by working with digital representations rather than raw sensor data.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to improving searching systems for vehicles. In one embodiment, a method includes, in response to detecting a gesture performed by an occupant of a vehicle, defining a context of the gesture. The method also includes correlating the gesture with a target. The method also includes constructing a search query based on the context, the target correlated with the gesture, and an occupant request. The method also includes executing the search query to acquire search results. The method further includes communicating the search results to the occupant to provide assistance to the occupant based on the target.