Intelligent driving research has focused much attention on point cloud obstacles since they are a class of high-dimensional data that can adequately depict the shape and placement of obstacles, unlike picture data. Currently, deep learning technology is primarily employed for vehicle autonomy point cloud obstacle classification tasks. These techniques typically struggle with low classification accuracy, processing efficiency, and model stability. To tackle the abovementioned issues, this paper suggests a novel random forest algorithm that integrates the out-of-bag error theory and can consistently and accurately evaluate the influence of point cloud properties. Then, building on the novel algorithm, this paper suggests a modified PointNet network that incorporates the effects of both global and local features on the classification task, therefore increasing the conventional network’s classification accuracy. To assess the effectiveness of this novel approach in the experimental portion, we set up an evaluation system based on the metrics for average accuracy, overall accuracy, and a confusion matrix. According to the simulation results, the overall accuracy of the proposed network in terms of classification accuracy is 94.4% and the average accuracy is 84.9%, which are then compared to the prototype PointNet and its variants. The classification accuracies for the four types of obstacles are 97.6%, 63.6%, 92.5%, and 86.1%. In addition, the proposed method is effective at improving both the computational complexity and stability of the network.

This paper is based on model-free adaptive control with data-driven control and explores its application in the motion control of intelligent vehicles. Based on experiments investigating the feasibility and effectiveness of standard control algorithms, the paper examines four key parameters and error control metrics in the control algorithm. Using methods such as factor perturbation analysis, linear fitting, and correlation analysis, parameter sensitivity analysis was conducted. The parameter tuning problem was transformed into a multi-objective optimisation problem, and an optimisation model was established. An enhanced multi-objective genetic evolutionary algorithm (MO-GA) based on NSGA-II is proposed to address the multi-objective optimization problem. Through three experimental scenarios under the Hammerstein nonlinear system, the effectiveness of the improved algorithm was verified. Based on the MatlabR2024b/Carsim2020 platform, joint simulation experiments were conducted on the Audi A6-Avant models under dual-lane shifting and serpentine operating conditions, verifying the portability of the improved algorithm.