Research On Adaptive Cruise Control For Multi-target Scenario And Curved Road

With the advantages of intelligent vehicle,the configuration rate of Advanced Driving Assistant System(ADAS)in intelligent vehicle is increasing year by year,and it becomes an important part of the final trend of driverless vehicle.As a kind of ADAS,Adaptive Cruise Control(ACC)has two functions: constant speed cruise and distance following,which can relieve driver's fatigue,improve riding comfort and reduce traffic accidents.In this thesis,ACC is taken as the research object,and the research is mainly focused on the small curvature detours of multiple targets,aiming at the selection of the front vehicle and the horizontal and vertical control under the condition of camera and radar fusion to complete the algorithm simulation.The main research contents are as follows:At first,a multi-target tracking system is studied to determine the preceding vehicle of ACC.In the environment of multiple vehicles and clutter ahead,to determine the preceding vehicle of ACC,a multi-target tracking method is established after receiving the synchronous data jointly detected by vision and radar,which creates the initial target trajectory based on the logic method,then obtain the confirmed trajectories of multiple targets based on data association and filtering algorithm.The simulation experiment of designing target parallel and cross movements proves the effectiveness of the system.Secondly,the hierarchical control strategy of ACC control part is determined.Aiming at the established single-track dynamic model,taking it as the control object,the upper controller based on Model Predictive Control(MPC)is determined.The controller is used to calculate the expected acceleration in the distance following mode and the front wheel angle.Then,the lateral and longitudinal controller based on MPC is established.For the solution of the longitudinal expected acceleration,the mutual longitudinal dynamic model of the workshop is established,and the multi-objective constraints such as safety following,economy and comfort are considered.For the calculation of the front wheel angle,the prediction model based on the closed-loop system combining the man,vehicle and road is established by monorail vehicle dynamics model,and added on the basis of the front wheel angle constraints.Then,QP solver is selected to solve the optimal problem combined with objective function in the limited time domain.The results of simulation experiments show the adaptability and effectiveness.Finally,this thesis builds the overall verification platform.The automatic driving toolbox of MATLAB and Simulink is used to complete the process from sensor configuration,scene design to algorithm verification,forming an integrated verification method.Through changing the number of vehicles,state to complete the overall simulation.The applicability of the algorithm and design framework in this thesis is proved.