Vehicle Route Selection Using Cluster Analysis for Testing
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Solution Overview
Problem
Existing methods for simulating vehicle behavior on roadways lack efficiency in selecting suitable routes for testing vehicle components or vehicles, particularly in characterizing route properties and features effectively.
Innovation Solution
A computer-implemented method and device that provide routes for testing by associating routes with first and second variables characterizing route properties and features, respectively, and selecting routes based on target values and tolerances, using cluster analysis to group routes for efficient testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If routes are selected based on multiple route properties and features with target values and tolerances, then the suitability of routes for testing is improved, but the complexity of the selection process increases
Solution Approach 1:
The patent segments the route selection process into distinct stages: first filtering routes based on route properties (first variables) against target values and tolerances, then grouping the filtered routes by route features (second variables) using cluster analysis. This segmentation reduces the complexity of simultaneously considering all criteria by breaking them into sequential processing steps.
Solution Approach 2:
The patent introduces an intermediary clustering process that acts as a mediator between the initial route filtering and the final route selection for testing. The cluster analysis groups routes by similar features, and then one route is selected from each group, ensuring both diversity and suitability without requiring direct comparison of all routes against all criteria.
2Productivity
If cluster analysis is used to group routes by route features, then the efficiency of route selection is improved, but the computational requirements increase
Solution Approach 1:
The patent performs preliminary filtering of routes based on route properties and target values before applying cluster analysis. This preliminary action reduces the number of routes that need to be processed by the computationally intensive clustering algorithm, thereby improving efficiency while reducing computational energy consumption.
3Measurement precision
If multiple route properties and features are considered with target values and tolerances, then the precision of route selection is improved, but the time required for route identification increases
Solution Approach 1:
The patent segments the selection criteria into route properties (first variables) and route features (second variables), processing them in sequential stages. This allows precision to be maintained through multiple criteria while reducing time by avoiding simultaneous evaluation of all criteria against all routes.
Solution Approach 2:
The patent creates clusters as simplified representations or copies of groups of similar routes. Instead of evaluating each individual route against all criteria, the cluster structure allows one representative route to be selected from each group, maintaining selection precision while significantly reducing evaluation time.
Data Source
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AI summary
Device and computer-implemented method for providing routes for testing a vehicle component or a vehicle, a set of routes is provided (304), wherein the routes are each associated with values of first variables, wherein the first variables each characterize a route property, in particular a travel duration, a travel length, a departure time, a day of the week, a geographical region, a starting point or an end point or geo-referenced telemetry data of the respective route, wherein the routes are each associated with values of second variables, wherein the second variables each characterize a route feature, in particular a road type, a gradient or a curvature, of the respective route, wherein a target value is provided for several of the first variables (302), wherein depending on the plurality of target values, a set of routes is selected from the set (306), each associated with a value of the first variable,for which the target value is provided, which is equal to the respective target value or deviates from the respective target value by less than a tolerance, whereby routes from the set of routes are divided into groups depending on the second variables.