Intersection Deadlock Scoring From Multisource Traffic Data
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Solution Overview
Problem
Existing methods for determining road network traffic conditions, such as human observation and modern sensor data, are limited in their applicability and accuracy, particularly in generating traffic information for simulations, navigation, and automated vehicle controls due to incomplete data availability.
Innovation Solution
A system utilizing entity observation data from various sources, static map features, and dynamic temporal context to generate an intersection deadlock score, predicting deadlock conditions through a machine learning model, and providing predictions to navigation and traffic applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources are integrated to improve prediction accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent combines multiple data sources (sensor data, map data, historical traffic data) into a unified machine learning model that processes all inputs simultaneously to generate intersection deadlock predictions, achieving high accuracy through data integration
Solution Approach 2:
The machine learning model serves multiple functions: it processes diverse data types (sensor readings, map features, historical patterns), performs real-time prediction, and provides outputs for various applications (navigation, traffic management), making the system versatile
2Productivity
If real-time data processing is implemented to improve response time, then productivity improves, but use of energy increases
Solution Approach 1:
The system pre-processes and stores historical traffic data and map features in advance, training the machine learning model offline with pre-collected data, so that real-time predictions only require processing current sensor readings rather than analyzing all historical data from scratch
Solution Approach 2:
The machine learning model uses dimensionality reduction and feature selection techniques to transform high-dimensional input data into a compact representation, reducing computational complexity and energy requirements for real-time inference while maintaining prediction accuracy
Data Source
AI summary
A method, apparatus, and non-transitory computer-readable storage medium for determining an intersection condition are provided. An example embodiment may obtain entity count data of an area comprising an intersection, obtain map feature data of the area comprising the intersection, obtain temporal context data of the area comprising the intersection, and generate an intersection deadlock score based on the entity count data, the map feature data, the temporal context data, or a combination thereof. The generated intersection deadlock score may further be used to provide a prediction of a deadlock condition based on the intersection deadlock score to a traffic application and/or a navigation application.