Instance Segmentation in CARLA: Methodology and Analysis for Pedestrian-oriented Synthetic Data Generation in Crowded Scenes

Abstract

The evaluation of camera-based perception functions in automated driving (AD) is a significant challenge and requires large-scale high-quality datasets. Recently proposed metrics for safety evaluation additionally require detailed per-instance annotations of dynamic properties such as distance and velocities that may not be available in openly accessible AD datasets. Synthetic data from 3D simulators like CARLA may provide a solution to this problem as labeled data can be produced in a structured manner. However, CARLA currently lacks instance segmentation ground truth. In this paper, we present a back projection pipeline that allows us to obtain accurate instance segmentation maps for CARLA, which is necessary for precise per-instance ground truth information. Our evaluation results show that per-pedestrian depth aggregation obtained from our instance segmentation is more precise than previously available approximations based on bounding boxes especially in the context of crowded scenes in urban automated driving.