Research On Optimization And Evaluation Method Of Car Following Model Based On SUMO Application Test Scenario

With the development of connected vehicles,testing and verification become a great challenge in marketization progress.As a key point for laboratory tests,scenario emulation progress is considered an important research point.However,the current emulation algorithm has some kinds of problem,which influences the effectiveness and validity of emulation scenarios.This thesis focuses on the test scenario generation model,which is used as the basis for laboratory tests.The details are as follows:Firstly,typical security application scenarios of the connected vehicle are analyzed.A configuration file is generated according to the scenario feature and injected into the microscopic traffic flow simulation software SUMO(Simulation of Urban Mobility)as an input.Traffic control interface Tra CI(Traffic Control Interface)client is employed to realize vehicle control function.Then emulation vehicle trajectory is output to testbed node to realize scene reproduction and in-loop testing.Secondly,based on Krauss car-following model,an improved following model,IFM is proposed to solve the speed mutation problem of the Krauss model.Moreover,an acceleration and deceleration conversion model is constructed to improve the rationality of emulation scenarios.At last,both micro and macro experiments are designed to prove the effectiveness of the proposed model.Experiment results show that the proposed model could solve the speed mutation problem and improve the effectiveness of emulation scenarios.