Deep Learning-Based Point Cloud Classification of Obstacles for Intelligent Vehicles

Yiqi Xu, Dengke Wu, Mengfei Zhou, Jiafu Yang

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.