Active Braking/obstacle Avoidance Control And Experimental Research Of Intelligent Vehicle Based On Typical Scenarios

Automobile industry has become the pillar industry of China.In recent years,with the increasing number of motor vehicles in China,road congestion and traffic accidents occur frequently.In order to reduce the accident rate and improve the utilization rate of the road,automobile intelligence has become the research direction of many researchers.With the development of automobile intelligence,many intelligent auxiliary driving systems have been launched,and the technology is becoming more and more mature.In this paper,starting from the traffic accident scene,the traffic accident scene classification and accident case analysis are carried out,and the typical accident scene is finally determined.According to the specific typical accident scene,the autonomous braking and obstacle avoidance control strategy of intelligent vehicle is studied.The effectiveness of the control method is verified by analyzing the simulation test results in typical scenarios.In this paper,the scene is classified according to the road,mainly divided into urban road scene,highway scene and mountain road scene.Through the summary of previous achievements,this paper analyzes the characteristics of the above road scenarios,and analyzes the typical typical traffic accident cases of each road,selects the urban road scene for simulation test in terms of active braking,and selects the high-speed road scene for simulation test in the aspect of active obstacle avoidance.In the scene of urban road,the speed is slow.Generally,braking measures are taken to avoid rear end collision.If the driver fails to take braking measures in time,the active braking system will intervene to further avoid the occurrence of rear end collision.This paper mainly studies the control strategy of intelligent vehicle braking system.Firstly,a safety distance model based on collision time is established,and a graded braking strategy is proposed.Finally,the deceleration and TTC threshold for intelligent vehicles are determined according to the comfort and subjective feelings of drivers and passengers.Then,the intelligent vehicle active braking decision algorithm is proposed,and the control system model is established based on Simulink.Finally,six kinds of typical urban road simulation conditions are built by using prescan to carry out the simulation test work of intelligent vehicle autonomous braking effect.The simulation results show that the vehicle can avoid rear end collision with the vehicle ahead in the urban road scene,and can trigger different deceleration braking strategies according to TTC threshold accurately.Compared with the traditional one-stage braking,this method can effectively improve the safety and comfort of intelligent vehicles.In the highway scene,because the vehicle speed is too high,the longitudinal active brakingof the vehicle is usually difficult to avoid the occurrence of collision and rear end accidents.Therefore,this paper proposes a lateral active obstacle avoidance control strategy for intelligent vehicles.Firstly,a 3-DOF body dynamics model and a tire model are established as the basic prediction models to study and analyze the lateral motion characteristics of vehicles.The fifth order polynomial is used to plan the desired path.Combined with the constraints of relative speed,the minimum safe distance model for lane changing and obstacle avoidance of intelligent vehicles is constructed.Considering the comfort and stability of vehicles,the expected lane change trajectory is finally determined.Then,based on the model predictive control(MPC)algorithm,the intelligent vehicle dynamic system controller is proposed.The active front wheel steering control strategy is used to make the intelligent vehicle avoid the obstacles ahead,and ensure the vehicle steering stability and safety.Finally,the dynamic simulation software Car Sim and Simulink are used to establish the vehicle dynamics and control system model,and the autonomous obstacle avoidance effect of intelligent vehicle is simulated and tested for two typical highway scenarios.The simulation results show that the model predictive control algorithm can avoid the obstacles or vehicles in front of the vehicle on the premise of meeting the vehicle constraints.