The TUBS Road User Dataset: A New LiDAR Dataset and its Application to CNN-based Road User Classification for Automated Vehicles

Abstract

We present a novel approach for classifying pre-segmented laser scans of road users with consideration of real-time capability for applications in automated vehicles. Our classification approach uses 2.5D Convolutional Neural Networks (CNNs) to process range data as well as intensity information retrieved from reflected beams. We do not solely rely on publicly available laser scan datasets, which lack several features, but we provide an additional dataset from real-world sensor recordings, annotated by a tracking-based automatic labeling process. We evaluate the classification performance of our CNN regarding different feature configurations. For training, we use automatically and manually labeled data as well as mixtures with other public datasets. The results show promising classification capabilities. Training with automated labels shows similar results, providing a possibility to avoid the need for manual editing expense.