Roadway Feature Prediction via Repeating Geometric Patterns
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Solution Overview
Problem
Existing navigation systems face challenges in maintaining accurate maps of roadway networks due to the ever-changing nature of road features, such as signage and lane markings, which can lead to errors and safety issues, especially in autonomous driving applications.
Innovation Solution
A method and system for predicting roadway features by identifying repeating geometric patterns in a roadway network, using a geographic database to store and query patterns, and calculating probability scores for feature existence, allowing for the generation of notifications and actions based on predicted feature absence or presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground truth data is collected to update maps, then map accuracy is improved, but the inability to constantly collect data over the entire roadway network leads to outdated maps
Solution Approach 1:
The system performs preliminary actions by collecting ground truth data in advance and storing it in a database. When a roadway feature is queried, the pre-collected data is retrieved and used for prediction, eliminating the need for real-time data collection and thus reducing time loss while maintaining accuracy.
Solution Approach 2:
The system creates copies of ground truth data and stores them in a database for future reference. Instead of constantly collecting original data, the system uses these pre-created copies to predict and update map information, ensuring map freshness without continuous data collection efforts.
2Measurement precision
If ground truth data is used to update maps, then map accuracy is improved, but errors and discrepancies still exist due to changes and inability to constantly collect data
Solution Approach 1:
The system implements feedback by continuously comparing predicted roadway features with actual observed data. When discrepancies are detected, the system updates its predictions and re-trains models, creating a closed-loop system that continuously improves reliability by learning from errors and maintaining alignment with current roadway conditions.
Solution Approach 2:
The system adopts dynamics by making the map updating process adaptive and flexible. Instead of relying on static, periodically updated maps, the system dynamically adjusts predictions based on incoming data, changing roadway conditions, and pattern recognition, ensuring maps remain reliable even as the roadway network evolves.
3Measurement precision
If repeating geometric patterns are used to predict features, then map accuracy is improved, but the system complexity increases
Solution Approach 1:
The system segments the complex task of map updating into distinct modules: pattern recognition module, data collection module, prediction module, and validation module. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable while achieving high prediction accuracy through specialized processing at each stage.
Solution Approach 2:
The system applies universality by using a single pattern recognition framework that can identify multiple types of roadway features (signs, lane markings, intersections) through common geometric patterns. This multi-functional approach reduces system complexity compared to having separate specialized systems for each feature type, while maintaining high prediction accuracy across diverse roadway elements.
Data Source
AI summary
System and methods are provided for predicting a roadway feature. A geometric pattern in a roadway network is selected. The geometric pattern comprises one or more first links and one or more first nodes. A similar pattern to the geometric pattern is identified that comprises one or more second links and one or more second nodes in the roadway network. One or more features for the geometric pattern are identified. An absence of a feature in the similar pattern is determined based on the one or more features for the geometric pattern. A notification relating to the absence of the feature is generated.