Lidar-based Vehicle Target Recognition

Abstract

Vehicle target recognition technology is an important technology in the auxiliary safe driving system, which greatly improves Vehicle safety assist driving. This paper proposes a method to identify vehicle target recognition with lidar point cloud data and machine learning; This method first establishes an ROI (region of interest), and uses voxel grid filter to downsample the lidar point cloud data in this area to reduce the amount of processed data, then use RANSAC (random sampling consensus) to remove ground points that are useless for the recognition process, and then use Euclidean clustering for clustering. A rough classifier is set to initially eliminate obstacles that cannot be vehicles, then the features of each cluster is extracted. SVM (Support Vector Machine) is used as an accurate classifier, the parameters of SVM is optimized through cross-validation and grid search to achieve the best classification effect. Finally, the optimized SVM is used to identify each cluster. Experiments show that this method can effectively detect the target vehicle in the ROI and has a good recognition accuracy.