Map Data Updating Using Distance Filters for Road Work Signs
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Solution Overview
Problem
Existing navigation systems generate false positive road work warnings due to construction signs being moved to the side of the road after work completion, leading to inaccurate navigation for autonomous and semi-autonomous vehicles.
Innovation Solution
A system and method for updating map data by determining the location of road objects using sensor data, calculating distances, and applying predefined thresholds to suppress or retain observations based on actual road work presence and vehicle speed, utilizing machine learning for filtering construction-related objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If road work signs are observed and reported in real-time, then navigation accuracy is improved, but false positive warnings increase when signs are moved to the side of the road after work completion
Solution Approach 1:
The system dynamically updates map data based on real-time observations of road objects and their locations. By continuously monitoring the position of road work signs and comparing them against predefined thresholds, the system adapts its navigation instructions to reflect current road conditions, eliminating false positive warnings while maintaining accuracy.
Solution Approach 2:
The system uses real-time sensor data from user equipment as feedback to continuously update map data. By comparing observed road object locations with expected locations and using this feedback loop, the system can distinguish between active road work and completed work where signs have been moved to the side of the road.
2Reliability
If map data is updated in real-time based on sensor observations, then navigation reliability is improved, but system complexity increases
Solution Approach 1:
The system pre-establishes thresholds and criteria for identifying road work signs and their valid locations before real-time processing. By having these predefined parameters ready, the system can quickly process real-time observations without complex calculations, reducing computational complexity while maintaining high reliability.
Solution Approach 2:
The system introduces an intermediary processing layer that filters and validates sensor observations before updating map data. This intermediary layer handles the complexity of distinguishing between relevant and irrelevant observations, shielding the core navigation system from unnecessary computational burden.
3Reliability
If distance threshold is used to filter road observations, then false positive suppression is improved, but measurement precision may be compromised
Solution Approach 1:
The system uses distance as a filtering parameter to distinguish between road objects on the road versus on the side of the road. By changing the state of observations based on their distance from the road centerline and comparing them against predefined thresholds, the system maintains both reliability in suppressing false positives and precision in identifying actual road work.
Data Source
AI summary
The disclosure provides a system, a method, and a computer program product for updating map data. The system, for example, obtains sensor data from one or more user equipment. The sensor data is associated with a road object. Further, the system, determines a first location of a road observation sight and a second location of the road object based on a timestamp associated with the first location. Further, a distance associated with the second location of the road object and a center point of a link, is calculated. The link is a map matched link associated with the second location. Further, the system updates the map data based on the calculated distance.