Research On Adaptive Cruise Control Of Electric Vehicle Based On Multi-Sensor Fusion

In order to further improve the environmental perception ability of new energy vehicles and reduce the probability of traffic accidents,multi-sensor fusion technology is combined with adaptive cruise control system(ACC).While improving the recognition rate and recognition accuracy of the target vehicle ahead,it can also reduce the driver ’s driving fatigue and reduce the risk of rear-end collision.Therefore,the research of adaptive cruise control based on multi-sensor fusion will become an indispensable part of intelligent transportation.The main contents of this paper are as follows:1.Establish of the mathematical model.A workshop safety distance model based on fixed workshop time distance algorithm is established.Then the longitudinal kinematics model of the workshop is established based on the kinematics relationship of the workshop.Then the vehicle inverse longitudinal dynamics model is established,and the driving and braking models are established.Considering the frequent switching of driving/braking in adaptive cruise driving,the switching area of driving/braking is determined by establishing the maximum coasting deceleration curve of the vehicle.Finally,the switching logic of braking/driving is established,which lays a foundation for the design of adaptive cruise controller.2.Multi-sensor fusion method based on strong tracking unscented Kalman filter(STUKF).Firstly,the multi-sensor fusion structure is determined and the environment-aware sensors are compared to determine the type of sensor.Then,the vehicle kinematics model and the unscented Kalman filter(UKF)algorithm are deduced.Aiming at the problem that the mutation of the vehicle driving environment will lead to the insufficient positioning accuracy of a single sensor to the target vehicle,the strong tracking filter(STF)is introduced in the calculation process of the UKF algorithm.The fading factor is used to correct the prior error covariance in real time to improve the positioning accuracy of the target vehicle.Finally,the effectiveness of the fusion algorithm is verified by simulation experiments.3.Research on adaptive cruise control algorithm considering multi-objective constraints.Firstly,the basic principle of model predictive control(MPC)algorithm is introduced.Secondly,based on the longitudinal kinematics model of the workshop and the MPC algorithm,the objective function of the adaptive cruise controller is designed.The constraints of the objective function are determined for the following safety and ride comfort in the adaptive cruise process.Then the working mode of ACC system is analyzed,and the switching logic of working mode is determined.Finally,the effectiveness of the algorithm is verified by building a joint simulation platform and selecting several common working conditions such as constant speed cruise,following cruise,and emergency braking of the front vehicle.4.Real vehicle platform construction and real vehicle test.Firstly,a real vehicle platform for multi-sensor fusion algorithm and adaptive cruise controller is built,and the hardware and software parts of the experimental platform are briefly introduced.Then,in terms of multi-sensor fusion algorithm,real vehicle tests are carried out on several different working conditions under day and night conditions.The experimental results show that the STUKF fusion algorithm has better positioning accuracy for the target vehicle than the classical UKF fusion algorithm.Secondly,in terms of adaptive cruise control algorithm,the ACC controller based on MPC algorithm and PID algorithm is compared.The results show that the ACC controller based on MPC algorithm has better control effect.