In the world of automated driving, sensing accuracy is of the utmost importance, and proving that your sensors can do the job is serious business. This is where ground-truth labeling has an important role in Autoliv’s validation process. Currently, annotating ground-truth data is a tedious and manual effort, involving finding the important events of interest and using the human eye to determine objects from LiDAR point cloud images. We present a workflow we developed in MATLAB to alleviate some of the pains associated with labeling point cloud data from a LiDAR sensor and the advantages that the workflow provides to the labeler. We discuss the capabilities of a tool we developed to assist users in visualizing, navigating, and annotating objects in point cloud data, tracking these objects through time over multiple frames, and then using the labeled data for developing machine learning based classifiers. We describe how the output of the labeling process is used to train deep neural nets to provide a fully automated way to produce vehicle objects of interest which can be used to find false-negative events. To do this with a human analyst takes as much time as to play back the entire data set. However, with a fully automated approach it can be run on many computers to reduce the analysis time. We present this time savings as well as the accuracy of the labels achieved and show how this approach provides substantial benefit to Autoliv’s validation process.