Automatic labeling of vulnerable road users in multi-sensor data

A growing interest in technologies for autonomous driving emphasizes the demand for safe and reliable perception systems in various driving conditions. The current state-of-the-art perception solutions rely on data-driven machine learning approaches, and require large amounts of annotated data to train accurate models. In this study we have identified limitations in the existing radar-based traffic datasets, and propose a richer, annotated raw radar dataset. The proposed solution is a semi-automatic data labeling tool, which generates an initial set of candidate annotations using state-of-the-art automatic object recognition algorithms, and requires only minimal manual intervention. In the first qualitative evaluation ever for automotive radar datasets we measure the quality of automatically computed labels under various light conditions, occlusion, behavior and modeling bias based on a multitude of tracking metrics. We determined the specific cases where automatic labeling is sufficient and where a human annotator needs to inspect and manually correct errors made by the algorithms.