Intelligent vehicle lateral tracking algorithm based on neural network predictive control

Introduction Intelligent vehicles and autonomous driving have been the focus of research in the field of transport, but current autonomous driving models have significant errors in lateral tracking that cannot be ignored.Methods In view of this, this study innovatively proposes a lateral trajectory algorithm for intelligent vehicles based on improved radial basis function (RBF). The algorithm first models the lateral trajectory behaviour of the car based on the pre-scanning steering theory, and then proposes an improved RBF network model to compensate for the error of the lateral trajectory model and further improve the accuracy.Results According to the simulation test results, after 20 iterations, the proposed algorithm always shows the highest accuracy with the same number of iterations. When the number of iterations reaches 370, the accuracy of the algorithm is stable at 88%. In addition, the bending test shows that the proposed algorithm performs best at low speeds with an overall error of 0.028 m, which is a higher accuracy compared to the algorithm without neural network compensation.Discussion The maximum error of the proposed algorithm does not exceed 0.04 m in complex continuous curved terrain, which is safe within the normal road width. Overall, the lateral tracking algorithm proposed in this research has better lateral tracking capability compared to other improved algorithms of the same type. The research results are of some significance to the field of lateral tracking of automatic driving, which provides new ideas and methods for the field of lateral tracking of automatic driving technology and helps to promote the overall development of automatic driving technology. By reducing the lateral tracking error, the driving stability and safety of the self-driving car can be improved, creating favourable conditions for the wide application of the self-driving technology.