Adaptive Turn Guidance via Missed-Turn Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current personal navigation devices do not adapt the level of prompting for turns based on situational factors such as traffic, weather, or time of day, providing the same level of instructions for all turns, which can be inadequate for difficult turns under certain conditions.
Innovation Solution
A personal navigation system that determines when a user misses a turn and reports this to a central server, which aggregates data from multiple devices to create a database of difficult turns, allowing devices to provide enhanced turn-by-turn directions with additional warnings for identified challenging turns, considering factors like time, weather, and vehicle type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the same level of prompting is provided for all turns, then the navigation system is simple to operate, but it fails to provide adequate warnings for difficult turns under certain conditions
Solution Approach 1:
The system pre-identifies difficult turns by analyzing historical missed turn data from multiple devices and stores this information in a database. When providing turn-by-turn directions, the system checks against this pre-analyzed database to determine if enhanced warnings are needed, rather than analyzing real-time conditions for each turn.
Solution Approach 2:
The system uses feedback from actual driver behavior (missed turns detected by multiple navigation devices) to continuously improve the identification of difficult turns. This collective feedback loop allows the system to adaptively enhance warnings for turns that historically pose problems, making the navigation more reliable without requiring complex real-time analysis.
2Reliability
If enhanced warnings are provided for all turns, then user safety is improved, but unnecessary warnings increase and reduce ease of operation
Solution Approach 1:
The system applies enhanced warnings selectively only to specific turns identified as difficult based on historical data, rather than providing uniform enhanced warnings for all turns. This localized approach ensures that safety enhancements are concentrated where actually needed, maintaining ease of operation for turns that are not problematic.
Solution Approach 2:
The system provides enhanced warnings partially - only for difficult turns identified through historical analysis - rather than applying enhanced warnings universally. This partial action approach avoids the information overload and reduced ease of operation that would result from excessive warnings on all turns.
3Measurement precision
If real-time situational factors are analyzed for each turn, then prompting accuracy is improved, but the system requires complex real-time data processing
Solution Approach 1:
The system performs turn difficulty analysis in advance by collecting and analyzing historical missed turn data from multiple devices, storing the results in a database. This preliminary analysis eliminates the need for complex real-time data processing during actual navigation, as the system only needs to query pre-analyzed results.
Solution Approach 2:
The system uses self-service by leveraging collective data from multiple navigation devices to automatically identify difficult turns without requiring manual input or complex real-time analysis. The aggregated historical data serves the system's needs for accurate turn difficulty assessment without additional processing burden.
Data Source
AI summary
Embodiment methods and systems enable personal navigation devices to warn drivers during turn-by-turn navigation directions when they are approaching a difficult turn. Personal navigation devices may report to a server when a turn is missed during turn-by-turn directions, including identifying the turn and situation information. The server may aggregate missed turn reports from many personal navigation devices to generate a difficult turn database. Personal navigation devices may access the difficult turn database when generating turn-by-turn directions to identify turns requiring enhanced directions. The difficult turn database may be stored on personal navigation devices, and/or may be maintained on the server. Personal navigation devices may be configured to recognize when turns are intentionally missed, and only report missed turns when they determined that the turn was missed unintentionally. Indications of turn difficulty may be correlated to situation information received in missed turn reports to more accurately reflect when turns are difficult.