Proactive Travel Assistance via User Intent Detection
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Solution Overview
Problem
Current navigation systems often provide irrelevant information to users before they start driving, as they rely on general traffic patterns and require a connection to a vehicle, leading to delays and inefficiencies.
Innovation Solution
A computing platform that determines a user's intent to drive based on their activity, location, and routine, providing context-specific travel assistance before they reach their vehicle, including a list of predicted destinations and navigation data, using sensors and historical data to anticipate the user's needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If navigation systems provide travel information before driving based on general traffic patterns, then users receive advance information, but the information is not specific to the actual drive context and may be irrelevant
Solution Approach 1:
The system performs preliminary actions by detecting user departure and proactively providing predicted destinations and navigation information before the user actually starts driving. This early provision of context-specific information saves user time while ensuring relevance through real-time detection of actual driving intent rather than relying on general pre-programmed data.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring user activity, location, and vehicle connection status to dynamically determine when to provide navigation information. This feedback loop ensures that information is provided only when contextually appropriate, maintaining high relevance while minimizing unnecessary information delivery.
2Reliability
If the system requires a connection with the vehicle to receive navigation information, then information can be provided, but this causes delay and may provide information that is not context specific
Solution Approach 1:
The system performs preliminary detection of user departure and provides predicted destination information before the user reaches or connects with the vehicle. This eliminates the delay caused by waiting for vehicle connection while ensuring reliability through multiple detection methods including activity recognition, location tracking, and routine analysis.
Solution Approach 2:
The system uses the computing platform itself as an intermediary to detect departure and provide information, rather than requiring direct vehicle connection as the intermediary. This alternative mediation path eliminates the bottleneck of vehicle connection requirements while maintaining reliable information delivery through other sensing and detection mechanisms.
3Loss of time
If the system provides navigation information early before driving intent is confirmed, then user time is saved, but the information may be irrelevant to the actual drive made
Solution Approach 1:
The system performs preliminary detection and provides information based on predicted destinations derived from user routine and historical data. This preliminary action is enhanced with adaptability by continuously updating predictions based on actual user behavior patterns, ensuring that early-provided information remains context-specific to the actual drive the user is likely to make.
Solution Approach 2:
The system employs dynamic adjustment of predicted destinations based on real-time user activity, location, and historical patterns. This dynamic approach allows the system to adapt predictions as the user's actual intentions become clearer, maintaining context specificity while providing early information that evolves with the user's actual driving behavior.
Data Source
AI summary
Apparatus, systems, and/or methods may provide travel assistance. For example, a determination may be made that a user is to begin driving in the near future. In addition, a determination may be made of one or more predicted destinations when the user is to begin driving in the near future. Thus, travel assistance may be provided to the user based on the one or more predicted destinations when the user is to begin driving in the near future.