Accelerated Test Method of Intelligent Driving System Based on Surrogate Genetic Optimization Model

Abstract:

This paper proposed an accelerated test method for intelligent driving system based on the surrogate genetic optimization model. First， the Latin hypercube sampling interval in the parameter sampling module was improved by using the weights and optimal solution region features of the scenario element hierarchical analysis method， achieving a synergistic improvement in sampling efficiency and optimization effect. Next， by utilizing parameter sampling results and repeatability screening mechanism， the population diversity of the genetic optimization module was increased， overcoming the local convergence problem of traditional genetic algorithms. Then， the surrogate filtering module based on cyclic update mechanism was used to predict the test results of the scenario， which balanced the contradiction between the efficiency and accuracy of the accelerated algorithm and the application of the surrogate model. Finally， a simulation platform was built to accelerate test process and verification of the intelligent driving system to be tested in the front vehicle speed change scenario of high-dimensional time series decomposition. The results indicate that the method proposed in this paper can effectively search for a large number of key scenarios and improve testing efficiency.