Identification of Road Adhesion Coefficient Based on Neural Network and Kalman Filter
2022-01-1103
03/29/2022
- Event
- Content
- As an important input parameter of intelligent vehicle active safety technology, road adhesion coefficient is of great significance in autonomous collision avoidance, emergency braking and collision avoidance, and variable adhesion road motion control. Traditional recognition methods based on vehicle dynamics require large data volume and low solution accuracy, while recognition methods based on optical sensors have strict requirements on the working environment, poor adaptability under complex working conditions, and high cost. This paper proposes an adhesion coefficient recognition method based on Elman neural network and Kalman filter. By establishing a four-degree-of-freedom vehicle dynamics model, the dynamic parameters such as yaw rate, longitudinal velocity, lateral velocity, and angular velocity of each wheel related to the road adhesion coefficient are analyzed as the input of the neural network model. Carsim/Simulink co-simulation is used to establish a data set, the Kalman filter algorithm is used to remove the noise of the neural network model input, and the Dropout regularization method is used to reduce the over-fitting phenomenon of the model. In this paper, roads with adhesion coefficients of 0.2～0.9 are identified, the average error of identification is 4.93%, and the accuracy is 91.23%. Compared with the traditional BP neural network, the average recognition error of this method is reduced by 2.23%, and the accuracy is increased by 9.83%. At the same time, real vehicle experiments were carried out on wet asphalt pavement and dry asphalt pavement to verify the feasibility of the method. This paper proposes a road adhesion coefficient recognition method, which can improve the applicability of intelligent vehicle active safety systems to complex scenarios.
- Citation
- Lu, X., "Identification of Road Adhesion Coefficient Based on Neural Network and Kalman Filter," SAE Technical Paper 2022-01-1103, 2022, .