Digital Road Map Plausibility Check Using Road User Behavior
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Solution Overview
Problem
Digital road maps used by automated vehicles and driving assistance systems may not accurately reflect real-time traffic conditions, leading to inappropriate vehicle actions due to outdated or incorrect map data.
Innovation Solution
A method to check if a digital road map correctly represents the actual circumstances visible from a predefined pose by comparing the actual behavior of other road users with the reference behavior determined from the map features, and adjusting the pose or map data as needed to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital road map data is updated frequently from external sources, then the map data becomes more current, but the accuracy and reliability of the map data decreases due to potential errors in user-generated corrections
Solution Approach 1:
The system implements a feedback mechanism where actual road user behavior is observed and used to verify map accuracy. When inconsistencies are detected between expected behavior (based on map data) and actual behavior, the system triggers map verification and correction processes, ensuring continuous improvement of map accuracy without relying solely on external updates
Solution Approach 2:
The system performs self-verification by using observed road user behavior to automatically detect and report map inaccuracies. Instead of relying entirely on external providers to update and verify map data, the system autonomously identifies discrepancies and initiates correction workflows, making the map data self-correcting and continuously reliable
2Reliability
If the system continuously monitors and verifies map accuracy by comparing actual road user behavior with expected behavior, then the accuracy of the digital road map is maintained, but the computational resources and processing time increase
Solution Approach 1:
The system performs partial verification by focusing computational resources only on specific scenarios where map accuracy is critical or where inconsistencies are detected. Instead of continuously analyzing all road user behaviors, the system selectively monitors relevant behaviors and triggers verification only when necessary, reducing overall computational load while maintaining map accuracy
Solution Approach 2:
The system dynamically adjusts verification parameters such as the threshold for triggering map verification, the frequency of behavior monitoring, and the level of detail in behavior analysis. By changing these parameters based on current operational conditions, the system optimizes the balance between maintaining map accuracy and consuming computational resources
3Reliability
If the system uses multiple poses and multiple road users for verification, then the comprehensiveness of the check increases, but the complexity of the verification process increases
Solution Approach 1:
The verification process is segmented into distinct phases: data collection from multiple poses and road users, individual behavior analysis, aggregation of results, and final verification decision. By dividing the complex verification process into manageable segments, the system can comprehensively verify map accuracy across multiple perspectives while keeping each individual processing step relatively simple and tractable
Data Source
AI summary
A method is for checking whether a digital road map correctly reproduces actual circumstances visible from at least one predefined pose. The method includes procuring observations of a scenario from the at least one predefined pose, determining an actual behavior of one or more other road users from the procured observations, and determining a reference behavior of the one or more other road users based on one or more features of the digital road map. The method further includes, in response to the determined actual behavior being consistent with the determined reference behavior, establishing that the digital road map correctly reproduces the actual circumstances visible from the at least one predefined pose at least with regard to the one or more features from which the reference behavior was determined.