Research On Longitudinal Active Safety Control System Of Intelligent Vehicles

With the development of vehicle transportation technology and the steady growth of the domestic economy,the ownership of cars and drivers has increased year by year.Vehicle safety is the cornerstone of ensuring the safety of the life and property of the driver or passengers,as one of the main components of the active safety of the vehicle,the collision warning system,with the potential danger of warning the driver function and the vehicle automatically take braking measures when the limit is dangerous,this paper considers the driver’s own conditions and environmental factors to optimize the longitudinal safety control effect,improve the individual needs and safety level of vehicle users.The main work of this paper is as follows:First of all,aiming at the problem of traditional safety fixed value distance,based on the dynamic analysis and calculation of vehicle braking process,the early warning and braking limit distance under typical working conditions are established,and then the influencing factors in the expected safety distance are analyzed,and the improved expected safety distance is proposed,so that it can adapt to drivers with different personalities and have certain environmental adaptability.Secondly,the influencing factors in the safety distance model are analyzed,and a two-level safety distance model that can change with its own conditions and environmental changes is established by using fuzzy theory for the driver’s reaction time,and then the least squares estimation algorithm is used to predict the pavement adhesion coefficient based on the functional principle,and the predicted pavement adhesion coefficient is used to improve the minimum safety distance by adding the self-driving speed factor,and the influencing factors of the front time distance are analyzed under the condition of self-driving following,and the variable head time distance is predicted based on the double hidden layer BP neural network.The simulation of the safety distance model and comparison with the traditional safety distance model show that the improved safety distance model can meet the individual needs of drivers and passengers,and changes with the change of environmental characteristics.Then,this paper uses Car Sim’s own vehicle to establish the required dynamic model,and based on the vehicle mechanical analysis,Simulink is used to establish the vehicle inverse dynamic model.The loopholes of four typical safety control strategies were analyzed,the longitudinal safety control strategy was established on the basis of the improved safety distance model,the series controller was established on the basis of the existing vehicle dynamics,the optimal control was selected as its superior controller,the subordinate controller was selected for PID control,the RBF neural network was used to achieve PID tuning,improve its tracking speed and accuracy,and PID control was selected for cruise control,and genetic algorithm was used to realize the self-tuning of PID.Optimized cruise control.Finally,the Matlab program was written and imported into Simulink and Car Sim to build a co-simulation to verify the effectiveness and accuracy of the safe distance model,control strategy and vertical controller.