The invention relates to a vehicle speed prediction method based on adaptive Kalman filtering. The method comprises steps that 1), in an RFID radio frequency identification system, a vehicle travelingat the high speed is automatically identified through radio frequency signals, and relevant data is acquired to achieve acquisition of the data of the road surface; 2), the adaptive Kalman filteringalgorithm is applied to speed prediction of the high-speed moving vehicle for the acquired relevant information, a state space model is utilized to transform a speed formula into a state equation andan observation equation, the Kalman filtering algorithm is further utilized to predict the speed of the high-speed moving vehicle, and Kalman filtering is divided into two parts for speed update, timeupdate and measurement update . The method is advantaged in that automobile control stability and active safety are improved, and the traffic accident rate is reduced.

Vehicle speed prediction method based on adaptive Kalman filtering

The invention discloses a method and device for controlling vehicle running and belongs to the technical field of automobile control. The method comprises the steps of obtaining an overall running path, a real-time position and a first distance of a vehicle; dividing the overall running path into a plurality of node road sections, and determining a turning position of the first node road section; obtaining a to-be-turned distance between the vehicle and the next node in the running direction; comparing the to-be-turned distance and a turning distance; when the to-be-turned distance is larger than the turning distance, calculating and obtaining a first enhanced signal, and adopting a reinforcement learning algorithm to determine a first running policy according to the first distance and the first enhanced signal; when the to-be-turned distance is not larger than the turning distance, calculating and obtaining a second enhanced signal, and adopting a reinforcement learning algorithm to determine a second running policy according to the first distance, the to-be-turned distance, the turning distance and the second enhanced signal; and controlling a running track of the vehicle according to the first running policy and the second running policy. According to the method and device for controlling vehicle running, automatic driving of the vehicle is implemented.

Method and device for controlling vehicle running

The invention relates to a vehicle roll-over index prediction method based on gravity center height online estimation. The vehicle roll-over index prediction method comprises: establishing a vehicle roll-over dynamic model according to a dynamic base theory; obtaining a parameter identification model related to gravity center height through an adaptive filtering theory, wherein the parameter identification model only requires the lateral acceleration and the relative roll angle sensor signal as the input; based on the parameter identification model, providing an adaptive rate driven by parameter error information by designing a filtering matrix so as to achieve the online real-time adaptive estimation of the gravity center height; and finally obtaining the roll-over index prediction method based on the gravity center height online estimation according to a horizontal load transfer rate basic formula. The vehicle roll-over index prediction method of the present invention has advantages of high reliability, good real-time performance, less signal measurement demand, low cost, and good universality.

Vehicle roll-over index prediction method based on gravity center height online estimation

The invention discloses a side slip angle estimation method and system based on robust unscented kalman filtering The method includes the steps that a three-freedom-degree vehicle dynamics model related to a side slip angle is established, a state equation and an observation equation of an unscented kalman filter are determined according to the model, and the input quantity, the state quantity and the observation quantity are determined, wherein the input quantity comprises the front wheel rotation angle and the longitudinal accelerated speed, the state quantity comprises the side slip angle,the yaw velocity and the longitudinal vehicle speed, and the observation quantity comprises the lateral accelerated speed and the yaw velocity; the weight factor is calculated through an M estimatoralgorithm; and in combination with the weight factor, the state quantity at the current moment, the state equation and the observation equation, the side slip angle at the next moment is estimated through an unscented kalman filtering algorithm The M estimator algorithm and the unscented kalman filtering algorithm are combined to restrain the influence caused by outliers, the robustness of the unscented kalman filtering algorithm to the outliers in observation signals is improved, and the estimation accuracy of the vehicle side slip angle is improved

Side slip angle estimation method and system based on robust unscented kalman filtering

The invention relates to the technical field of intelligent driving, and discloses a self-adaptive cruising method. The method comprises the steps that the driving state information of a vehicle and the information of the surrounding driving environment of the vehicle are collected in real time; the obtained real-time information is processed, and according to the processing result, a self-adaptive cruising system enters a corresponding self-adaptive cruising state; according to the current state of the self-adaptive cruising system, a target vehicle is determined, and the target vehicle is displayed on a vehicle-mounted display device. The invention further discloses the self-adaptive cruising system. The system comprises a real-time information collection module, a real-time data processing module and a target vehicle display module. By means of the self-adaptive cruising method and system, the display information of the target vehicle can be provided for a driver in different statesof the self-adaptive cruising system so that the driver can better know the surrounding driving environment and can know the current state of the system in real time, and it is convenient for the driver to better use the self-adaptive cruising function.

Self-adaptive cruising method and system

The invention discloses a key target identification method for an automobile cruising system Based on the fuzzy control theory, a multi-target automobile lane changing fuzzy logic controller is designed, a dangerous lane changing automobile is prejudged, and effective identification of a straight lane key target is determined The tilting motion influence of automobile bend running is considered, an extension Kalman filtering on-line real-time estimation of an automobile mass center side slip angle and a road curvature is achieved, the bend key target identification method is provided, a key target judgment basis is given, and effective identification of the bend key target is achieved

Key target identification method for automobile cruising system

The invention discloses an automobile driving state estimation method with the influence of rolling considered. The method includes the steps that with the influence of automobile rolling motion considered, a three-degree-of-freedom motion differential equation which includes automobile mass center deviation, yawing motion and rolling motion and has nonlinear characteristics is established; the nonlinear three-degree-of-freedom motion differential equation is linearized; a state equation about the mass center deviation angle and the yaw velocity and a measurement equation are established, the mass center deviation angle and the yaw velocity are linearized and iterated into an extended Kalman filtering equation to obtain optimal estimated values of the mass center deviation angle and the yaw velocity, and meanwhile the mass center deviation angle and the yaw velocity of the nonlinear three-degree-of-freedom motion differential equation are input into an extended Kalman filtering model. Thus, comparison of the estimated values and actual values can be verified.

Automobile driving state estimation method with influence of rolling considered

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Key target identification method for automobile cruising system

Automobile driving state estimation method with influence of rolling considered