An Auto-Annotation Pipeline for Automotive Data Sequences

Abstract

Abstract The field of autonomous driving advances through the expanding utilization of deep learning methods. Training deep learning models on automotive data sequences allows for predictions of an object’s location and its future movements. Harnessing the benefits of deep learning generally requires accompanying annotations, and since the annotation process poses a significant bottleneck, new methods to mitigate this challenge are urgently needed. To address this issue, we propose an auto-annotation pipeline consisting of three modules. First, a 3D object detector is trained on annotated single-frame data and thereafter applied to each frame in a sequence. Second, a model-based tracker connects the bounding boxes across frames and improves low-confidence detections via filtering. Third, we introduce a smoothing network that further refines the detections by also incorporating future frames. The smoothing network considers both bounding boxes and point clouds. With our smoothing network, we show an improvement in center point, size and rotational error. This progress aligns with the efforts of previous work that have developed pipelines integrating object detection, multi-object tracking, and bounding box refinements. However, we distinguish ourselves by working offline with limited sequence annotations. In particular, our pipeline works with sequences where only one frame is annotated. Additionally, we contribute by proposing random window slides as a data augmentation approach. Our work serves as a baseline for object detection, multi-object tracking and smoothing for the Zenseact Open Dataset.