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Accurate detection of vehicles in such data is challenging because outdoor environments contain a mixture of objects with varying geometry, density, and reflectivity. The proposed approach focuses on reliable vehicle segmentation and classification while maintaining low computational complexity and high interpretability. The processing pipeline combines terrain filtering, modified Euclidean clustering that incorporates LiDAR intensity information, and PointNet\u2010based classification. To evaluate the proposed approach under real\u2010world conditions, a dedicated urban LiDAR dataset (Danubia) containing several hundred vehicles was created using a mobile mapping platform. 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