State Estimation Of Vehicle Based On Multi-sensors Information Fusion

With the development of economy and the acceleration of urbanization,people's living standards have increased rapidly,so more and more people have been beginning to enjoy the benefits of vehicle.Nevertheless,lots of automobiles which are put into use lead to various serious urban traffic problems for technology is a double-edged sword.Therefore,how to solve the problem brought by automobiles has become one of the primary tasks in the current society.Among all problems,the research on the active safety technology of automobiles is one of the research hot-spots in the field of automobile engineering.Automotive electronic stability systems(such as Bosch's ESP)are one of the important active safety technologies that can improve the handling stability of the car under extreme conditions and ensure the safety of automobiles.Real-time and accurate acquisition of the current driving state of the automobile is an important step for ESP to work.In general,it is very important to study how to reduce the cost of sensor and accurately estimate the driving state of the automobile,which is of great significance for the large-scale production of the stable control system.This paper relies on the National Natural Science Foundation of China(U1564201)to carry out an estimation study of the driving state of the car.Firstly,according to the degree of freedom that ESP control needs to consider,the paper establishes a nonlinear seven-degree-of-freedom vehicle model based on the "magic formula" tire model under the condition of both precision and complexity,and verifies the feasibility of the model in CarSim software.Combined with the state parameter estimation idea,the vehicle dynamics model is transformed into the state equation and the measurement equation.The state estimation model is established by combining the state vector with the standard state equation and the standard measurement equation.Secondly,the Kalman filter algorithm,the most commonly used estimation algorithm in information fusion theory,is introduced in detail.Based on the latest research status at home and abroad,this paper innovates and improves the existingtheory.Due to the shortcoming of Kalman filter(KF)and extended Kalman filter(EKF),this paper uses the unscented Kalman filter algorithm(UKF).Based on the UKF fusion theory,the vehicle speed estimator is designed for longitudinal and lateral speed based on low-cost sensor signals such as longitudinal and lateral acceleration,steering wheel angle and wheel speed.And the estimation of the side-slip angle.According to the low-cost sensor signals such as longitudinal,lateral acceleration,steering wheel angle and wheel speed,the vehicle speed estimator is designed based on the UKF fusion theory to estimate the longitudinal and lateral speeds and the side-slip angle.Then,when the automobile is inevitably affected by its own noise or external environment during the road driving,this situation will cause the vehicle sensor equipment to be affected to some extent,which may result in the inaccurate acquisition of the noise statistical characteristics of the sensor device,that is,the statistical characteristics of the measured noise are time-varying or unknown.Therefore,adaptive filtering is added on the based UKF,this paper adopts adaptive unscented Kalman filter algorithm(AUKF)that estimates the noise covariance matrix by real-time estimation of the new interest sequence,which reduces the influence of external noise and improves the UKF's adaptive capabilities and accuracy.Finally,when CarSim-Simulink under the given conditions combines simulation with real vehicle test,the accuracy of vehicle state estimation under the above algorithm is verified.From the simulation and real vehicle test results,the minimum error of the estimation result based on AUKF algorithm can reach 1%;after adding noise interference,the advantage of AUKF algorithm is more obvious than that of UKF algorithm,the estimation performance of AUKF is higher,and the state estimation value is much closer to the true value.Therefore,the research results in this paper have important theoretical guidance for the application and promotion of automotive active safety control technologies such as ESP.