Vehicle Localization Using Change-Aware Digital Map Features
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle localization systems rely on digital maps that may become outdated or incorrect, posing challenges for accurate and reliable navigation, especially in autonomous driving modes where continuous map updates are not feasible.
Innovation Solution
A method and system that assign predefined attributes to digital map features to assess the probability of changes, allowing for the identification and prioritization of outdated or incorrect map data, enabling robust localization even without immediate updates, by using an additional map layer to store these attributes and a localization unit to determine change probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital map data is used for vehicle localization, then localization function is enabled, but map data may become outdated or incorrect reducing localization accuracy
Solution Approach 1:
The system performs preliminary actions by assigning attributes to map features that predict future changes before they occur. Change probability values are calculated in advance based on historical data and feature characteristics, allowing the localization system to anticipate which map features may become outdated and adjust accordingly.
Solution Approach 2:
The system changes the parameter of map data by introducing dynamic change probability attributes to static map features. This transforms the map from a static data structure into a dynamic system where each feature carries metadata about its likelihood of changing, enabling probabilistic reasoning about data currency without requiring continuous updates.
2Loss of information
If continuous map updates are implemented to maintain accuracy, then map currency is improved, but system complexity and resource requirements increase
Solution Approach 1:
Instead of continuously updating all map data, the system applies partial updates only to features with high change probability. By calculating change probabilities for individual map features and selectively updating or re-evaluating only those likely to have changed, the system achieves adequate map currency with significantly reduced complexity compared to continuous comprehensive updates.
3Measurement precision
If more map features are used for localization to improve accuracy, then localization precision is improved, but probability of containing outdated information increases
Solution Approach 1:
The system applies local quality by assigning different change probability characteristics to different map features based on their specific properties. Instead of treating all map features uniformly, each feature receives attributes tailored to its likelihood of change, allowing the localization system to weight and select features locally based on their individual reliability rather than global map age.
Data Source
AI summary
A method and a corresponding system for localizing a vehicle using a digital map are described. In accordance with the method, the digital map assigns to the features one or a plurality of predetermined attributes, which are provided for characterizing possibly occurring actual changes in the features comparison to the digital map existing at the moment. On the basis of the assigned attributes, probabilities relative to the changes are also determined, and the vehicle is localized in consideration of the determined probabilities.