Research On Vehicle Time Headway And Control Strategy Optimization Of CACC Based On Multi-Front Vehicles

In the networked environment,coordinated adaptive cruise system achieved V2 V and V2 X with the help of car communication technology.Realizing the coordinated control with Multi-front vehicles with the technology of queue Shared data fusion and sensor data.from the time headway strategy,the upper control and virtual test,study the CACC multi-objective system control method.The paper's main work is as follows:(1)Designing a layered CACC system.Based on the simulation software Simulink and Carsim,the lower controller and vehicle dynamic model of the vehicle are built.By establishing the inverse dynamics model of the brake and the engine,the conversion relationship between the brake pedal force,the throttle opening degree and the expected acceleration is obtained.In order to ensure a more accurate test of the CACC system designed in this paper,the joint modeling method of Simulink and Carsim is adopted in this paper to meet the vehicle model as close as possible to the dynamics of the real vehicle mechanical system.(2)Constructing an extended model based on the optimal speed model,and the stability criteria of the extended model were obtained using linear and non-linear stability theories,and the suppressing effect of the information of the leading vehicles on the fluctuation of traffic flow was analyzed.Based on this,a strategy for designing the time interval of the workshop based on the artificial potential field theory is proposed,and the existing variable time interval strategy for the workshop is extended to consider multiple front cars,and the traditional artificial potential field is modified to meet the vehicle longitudinal follow-up.characteristic.The minimum expected inter-vehicle distance of the vehicle is reduced by 20%,and the driving comfort and road utilization of the vehicle areimproved.(3)Considering the lack of upper-level control of the CACC system,an optimal acceleration control method based on Hopfield neural network and MPC was designed.Compared with the existing MPC controller,the optimized controller can more accurately predict the driving state of the own vehicle in the predicted time domain by connecting the desired acceleration controller parameters with the network neurons,thereby improving the prediction accuracy.(4)Based on a driving simulation platform,a CACC simulation system was built by using Simulink,Carsim and Prescan.According to the verification requirements,the comprehensive performance of the system built in this paper is tested under the typical working conditions of the five middle schools.The results show that the speed fluctuation of the improved CACC system is more gentle.Under the conditions of changing lanes,changing lanes,and emergency braking,the acceleration is adjusted earlier than the traditional CACC system,and the limit value of the acceleration change rate is reduced.