Low-Definition Map Reliability Determination for Autonomous Vehicles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autonomous vehicle driving aid systems face challenges in reliably activating driving aids using low definition cartography due to its lack of precision and regular updates, which can lead to unstable and unreliable indicators of cartography reliability.
Innovation Solution
A process is developed to determine the reliability of a low definition cartography by calculating a correlation value from slope and trajectory indicators, which are updated periodically and take into account historical data to ensure robustness against random errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If low-definition mapping is used to reduce costs, then manufacturing cost is reduced, but mapping reliability deteriorates
Solution Approach 1:
The system continuously compares mapped road characteristics (from low-definition maps) with measured characteristics (from perception systems and GPS) to generate reliability indicators. This feedback mechanism allows the driving assistance system to adaptively use map data only when reliability is sufficient, thereby maintaining cost-effectiveness while ensuring safety through dynamic validation.
Solution Approach 2:
The low-definition mapping system performs self-validation by using the vehicle's own perception systems (cameras, GPS, inertial sensors) to verify map accuracy in real-time. This self-service approach eliminates the need for expensive external verification infrastructure while maintaining reliability through autonomous cross-checking.
2Reliability
If instantaneous indicator calculation is performed continuously, then mapping reliability assessment is improved, but computational load increases
Solution Approach 1:
Instead of continuous calculation, the system performs reliability indicator calculations at periodic intervals based on significant events such as GPS position updates, road segment transitions, or perception system triggers. This periodic approach maintains adequate reliability assessment while dramatically reducing computational burden compared to continuous calculation.
Solution Approach 2:
The system pre-calculates and stores road characteristics from low-definition maps before the vehicle reaches those locations. When the vehicle approaches a mapped segment, the relevant map data and expected characteristics are already prepared in memory, eliminating the need for real-time map processing and reducing computational load during critical reliability assessment moments.
3Measurement precision
If perception information is processed in real-time, then trajectory accuracy is improved, but computational load increases
Solution Approach 1:
The system extracts only the essential trajectory characteristics needed for reliability comparison (such as lateral position, curvature, and slope) from the full perception data stream. By filtering out redundant information and focusing only on the critical parameters needed for map validation, the system maintains high trajectory accuracy while significantly reducing computational requirements.
Data Source
Figure 1
Figure 2
AI summary
The invention relates to a method and a device for determining the reliability of a low-definition map in order to improve the reliability of the activation of at least one driving-assistance system of an autonomous vehicle, said autonomous vehicle travelling on a road and comprising a navigation system and a perception system, the navigation system comprising said map and supplying at least one slope and at least one curvature of the road around said vehicle, referred to as a mapped slope and mapped curvature, the perception system supplying a slope and a trajectory of the road around said vehicle, referred to as a measured slope and measured trajectory, said method comprising the steps of: Determining (201) a trajectory of the road; Determining (202) a slope indicator; Determining (203) a trajectory indicator; Calculating (204) a correlation value; Determining (205) a map reliability indicator.