Functional Road Class Assignment for Vehicle Route Prediction
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Solution Overview
Problem
Current methods for determining the most probable route in vehicle operations, based on road classes in digital maps, are inadequate for predicting vehicle operating variables as they rely on optimized road classes for navigation, which do not accurately reflect real-world usage patterns.
Innovation Solution
A method that assigns functional road classes to route sections based on actual usage data from a vehicle fleet, using navigation attributes such as frequency and average velocity to determine the most probable route, allowing for improved prediction of vehicle operating variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If road classes from digital maps optimized for navigation are used to determine the most probable route, then the route determination is simple and fast, but the accuracy of vehicle operating variable predictions is insufficient
Solution Approach 1:
The system pre-calculates and stores functional road classes for all route sections before they are needed for prediction. By continuously updating these classes based on historical fleet navigation data, the system prepares accurate route characteristics in advance, allowing fast and accurate route determination without real-time computation complexity.
Solution Approach 2:
The system uses actual navigation behavior feedback from the vehicle fleet to continuously refine and update the functional road classes. This feedback loop ensures that the road classes reflect real-world usage patterns rather than theoretical navigation optimization, improving prediction accuracy over time.
2Reliability
If traditional road class values optimized for navigation are used, then the route selection follows standard navigation logic, but the route may not reflect actual usage patterns and destinations
Solution Approach 1:
The system transforms traditional road class parameters into functional road classes by changing the underlying data basis from navigation optimization metrics to actual usage metrics. This parameter transformation incorporates information about real destinations and usage patterns that traditional road classes miss, making the route determination more reliable for vehicle operation predictions.
Solution Approach 2:
The functional road class acts as an intermediary that bridges traditional road classification and actual usage patterns. It translates raw navigation data from the fleet into meaningful route characteristics that capture real-world behavior, serving as a mediator between theoretical road types and practical usage scenarios.
Data Source
AI summary
A method provides a functional road class of route sections of a digital map. The method regularly or continuously provides present geographic positions of a plurality of vehicles of a vehicle fleet in a central unit; determines navigation attributes for all route sections of a digital map in dependence on the geographic positions of the plurality of vehicles; and respectively assigns functional road classes to the route sections in dependence on the navigation attributes. A most probable route can be determined by joining route sections to one another, wherein starting from a respective observed route section, which initially corresponds to the geographic position of the vehicle, the route sections are expanded by adding a further route section in dependence on the functional road class.