| In the context of the rapid development of active safety systems and intelligent braking,the safety requirements for bus automatic emergency braking(AEB)systems are increasing,and in-depth research on bus AEB systems can reduce the loss of life and property in its collisions and promote the intelligent development of buses.This thesis takes the AEB bus with pneumatic braking method as the research object,and designs the control strategy of AEB system under emergency conditions under the premise of guaranteeing safety and considering the system effectiveness and comfort,and carries out the test verification based on dSPACE test platform.Based on the idea of hierarchical control theory,this thesis establishes a unified AEB system control model with upper and lower controllers,and designs upper controllers under the framework of two algorithms,finite element state machine(FSM)and model predictive control(MPC),respectively.Based on the FSM framework,the AEB graded warning and graded braking control strategies are designed,and the key performance parameters in the control strategies are analyzed and studied: to improve the accuracy of AEB system intervention timing and reduce the disturbance caused to occupants by the system intervention timing and intervention level,a neural network-based design of a pavement adhesion coefficient estimator to determine the graded braking strength taking into account the adhesion conditions;to improve the adaptability of the AEB system to In order to improve the adaptability of the AEB system to various emergency conditions,a time-scale algorithm for TTC values combined with a distance-scale algorithm for air brake characteristics is used to determine the intervention timing of the AEB system.Considering safety,comfort and fuel economy,an upper-level controller based on the collision time model and MPC algorithm is designed to optimize the braking deceleration trend and change rate to improve the economy and comfort of the bus emergency braking process.For the bus model braking system using air pressure braking,simulation tests based on Trucksim are conducted,and the inverse drive model and inverse braking model of the bus are designed by regression method.In order to improve the phenomenon that the actual braking deceleration rate follows the desired braking deceleration rate due to the characteristics of the air pressure braking system,the lower controller based on PID control is designed,and the verification of the lower controller is carried out to realize the accurate control of the bus model.To verify the effectiveness of the AEB control strategy,a hardware-in-the-loop test platform was built based on dSPACE,PC and AEB controller,etc.The MPC control strategy design was implemented on the PC,and the code generation of the FSM control strategy was realized using Simulink.HIL tests were conducted on typical operating conditions such as approaching forward stationary,slow speed,uniform deceleration target and minimum start speed on high and low adhesion roads respectively,and the results showed that the AEB system under both FSM and MPC frameworks could achieve effective warning and effective braking,with the final safety distance ranges of 0.96～4.702 m and 1.646～2.465 m under each operating condition respectively;in terms of comfort,compared with In terms of comfort,compared with the FSM algorithm,the MPC-based AEB system has a smaller braking deceleration variation rate and is basically in the comfort zone,which can improve the comfort to a certain extent while ensuring the effectiveness and safety of the system. |
