Map Database Update via Sign Confidence Ratio
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current map data updating methods for road signs are inadequate for real-time or near real-time accuracy, particularly in scenarios where probe vehicles are insufficient or unreliable, leading to inaccurate reporting and increased processing burdens, especially in dynamic environments like construction zones.
Innovation Solution
A system and method that utilize sensor data from user vehicles to calculate a sign confidence ratio, determining the presence or absence of road signs by combining first sensor data with cumulative historic observations, allowing for automated addition or removal of road signs from the map database based on threshold values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If probe vehicles are used to update map data quarterly, then data coverage is maintained, but real-time accuracy deteriorates
Solution Approach 1:
The system enables self-service by allowing any user vehicle to contribute road sign observations to update the map database, eliminating the need for a dedicated probe vehicle fleet. Each vehicle independently captures and transmits data, creating a distributed, self-updating system that achieves real-time accuracy without specialized infrastructure.
Solution Approach 2:
The system transforms ordinary user vehicles into multi-functional units that simultaneously serve their primary transportation purpose and function as mobile mapping sensors. This universal approach allows any vehicle to contribute to map updates, converting the entire vehicle fleet into a distributed sensing network for real-time road sign detection.
2Loss of time
If user vehicle data is used to update map data in real-time, then update frequency improves, but data reliability deteriorates due to oscillations
Solution Approach 1:
The system performs preliminary actions by maintaining a confidence ratio threshold before accepting road sign updates. Observations are pre-validated against this threshold, and only data meeting the confidence requirement triggers map database updates. This preliminary filtering prevents oscillations by ensuring sufficient consensus before state changes occur.
Solution Approach 2:
The system implements feedback through the confidence ratio mechanism, where each new observation is evaluated against the existing confidence level. The confidence ratio provides continuous feedback on data reliability, dynamically adjusting update acceptance based on the consistency and volume of observations, thereby preventing premature updates from insufficient data.
3Measurement precision
If road sign updates are processed frequently, then real-time accuracy improves, but processing burden increases
Solution Approach 1:
The system applies partial action by processing only those observations that exceed the confidence ratio threshold. Instead of evaluating every incoming data point, the system selectively processes only the subset of observations that contribute meaningfully to update decisions, reducing computational burden while maintaining detection accuracy through threshold-based filtering.
Data Source
AI summary
A method, a system, and a computer program product may be provided for updating map data in a map database to indicate presence of a road sign. The method may include obtaining first sensor data associated with a road sign. The first sensor data may comprise at least one of one or more first positive observations of a road sign or one or more first negative observations of the road sign. The method may further include obtaining second sensor data for the road sign, for example, based on the first sensor data. The second sensor data may comprise cumulative historic road sign observation data for time duration. The method may include determining a sign confidence ratio based on the first sensor data and the second sensor data. The method may include updating the map data based on the sign confidence ratio.