Vehicle Test Route Planning Using Genetic Algorithm and Traffic Data
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Solution Overview
Problem
Current methods for planning test routes for vehicles on actual roads fail to accurately align with the actual travel speed distribution of users, leading to inadequate evaluation of key indexes such as performance, durability, and fuel consumption.
Innovation Solution
A method involving the division of road traffic scenes, calculation of traffic scene characteristics to derive VKT distribution and mean speed, construction of a preliminary road selection database, and the use of a genetic algorithm to plan test routes that better reflect user travel speed distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If hot spot road tracking method is used to plan test routes, then the test routes can cover areas with larger traffic flow, but the mean vehicle speed of the test routes becomes significantly lower than actual conditions due to congestion
Solution Approach 1:
The patent changes the selection parameters from purely traffic-flow-based to a composite metric that incorporates both traffic flow and speed characteristics. By weighting both factors, the system identifies roads that maintain high traffic flow while preserving realistic speed conditions, resolving the contradiction between coverage and speed accuracy.
Solution Approach 2:
The patent applies different evaluation criteria to different road segments based on their local characteristics. Instead of uniformly selecting high-traffic areas, it identifies specific road segments that have both sufficient traffic flow and acceptable speed conditions, allowing each segment to be evaluated according to its actual driving characteristics.
2Adaptability or versatility
If free driving method is used to plan test routes, then the test routes can simulate actual driving scenes, but the coverage of typical urban roads and traffic conditions is insufficient due to high randomness
Solution Approach 1:
The patent performs preliminary analysis of traffic data to identify and pre-select representative road segments before the actual testing begins. By pre-characterizing the traffic patterns and road conditions, the system ensures comprehensive coverage of typical urban roads while maintaining the ability to simulate various driving scenes through controlled selection criteria.
3Reliability
If test routes are planned based on hot spot areas with larger traffic flow, then the test routes can represent high-traffic conditions, but the evaluation of vehicle performance becomes inaccurate due to deviating mean speed
Solution Approach 1:
The patent incorporates feedback mechanisms by continuously monitoring actual traffic conditions and comparing them against the planned test routes. This feedback loop allows the system to adjust the test route selection to better match real-world conditions, ensuring both representativeness and measurement precision in vehicle performance evaluation.
Data Source
AI summary
Disclosed are a test routes planning method and apparatus of a vehicle on actual roads, a medium and a device. The method includes the following steps: dividing road traffic scenes; calculating traffic scene characteristics based on the divided road traffic scenes; constructing a preliminary road selection database for test routes; and completing a planning of the test routes of the vehicle on the actual roads at different speeds in different traffic scenes by leveraging a genetic algorithm based on the preliminary road selection database for the test routes. The present disclosure provides a solution for planning the test routes of the vehicle on the actual roads. In the solution, the travel speed distribution of a user is obtained through calculation based on traffic flow big data, and the planning of the test routes on the actual roads in different traffic scenes is completed by leveraging the genetic algorithm.