Research On Emergency Braking Control Strategy Of Commercial Vehicle And Application Of Typical Working Conditions

With the rapid development of China’s automobile industry,commercial vehicle ownership has increased year by year,which has promoted the vigorous development of the logistics industry and played an extremely important role in the transportation industry.However,the frequent occurrence of traffic accidents has brought serious impact on people’s life.The application of autonomous driving technology can reduce the driver’s labor intensity and accident rate,and improve traffic efficiency.As an advanced auxiliary driving function of active safety,Automatic Emergency Braking(AEB)can avoid accidents or reduce injury after collision,so it has important research significance.Based on the schoolenterprise cooperation with a car company in Guangxi,this paper has done the following work around the commercial vehicle AEB system:1.Overall system scheme is designed and dynamic system is modeled.The principle and composition of the AEB system are analyzed.The overall scheme is designed according to its functional requirements.The overall logic of the control system is described,and the principles of each part of the system are introduced.The vehicle model in Trucksim is configured by real vehicle parameters,and the dynamic model of commercial vehicle is built.Based on vehicle dynamics and control theory,the inverse longitudinal dynamics model is established in Simulink software,including the design of vehicle throttle control model,brake system control model and switching logic model between throttle and brake system.2.The hierarchical warning/braking control strategy based on road adhesion coefficient is designed.Considering that the road adhesion coefficient has a great influence on the actual braking effect,the EKF-based road adhesion coefficient estimation algorithm is designed through the three-degree-of-freedom vehicle model and the Dugoff tire model.The simulation verified that the algorithm can estimate the road adhesion coefficient in real time.Visual and auditory warnings are used to warn the driver,and the timing of early warning intervention is calculated according to different road characteristics.According to the TTC model and the brake response safety distance model,the timing of the graded braking intervention of the AEB system is calculated,and the braking deceleration is determined according to the change of the road adhesion coefficient,which made the AEB system more reasonable and adaptive.3.The hierarchical controller of AEB system is designed and the typical working conditions are simulated.The upper controller is designed by finite state machine,and the expected deceleration of the vehicle under different driving states is calculated.The fuzzy PID is used to design the lower controller,which converted the expected deceleration of the vehicle into the master cylinder pressure of the vehicle braking,and realized the real-time follow-up of the actual deceleration to the expected deceleration.The effectiveness of the lower controller is verified by simulation.The simulation platform including vehicle model,road adhesion coefficient estimation,upper and lower controllers and throttle / braking pressure calculation is built by Trucksim / Simulink.The current AEB test regulations are analyzed,and the design strategy is simulated and verified according to the test standards.The simulation results show that the designed control strategy can achieve early warning and anti-collision functions under the simulation conditions of CCRs,CCRm,minimum starting speed and low road adhesion coefficient,and meet the actual test standards.4.The hardware and software integration of the AEB system and the real vehicle verification are carried out.The low level driven CAN of the industrial computer is designed to realize the communication between the perception layer,the control layer and the execution layer.The related programs are debugged and integrated,the real test platform of the real vehicle is constructed.The feasibility of the control strategy is verified by the real vehicle test at low speed condition.