Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving

Sándor Kunsági-Máté, Levente Pető, Lehel Seres, Tamas Matuszka

Keywords: self-driving, automatic annotation, neural networks, 3D localization

Abstract: 3D detection of traffic management objects, such as traffic lights and road signs, is vital for self-driving cars, particularly for address-to-address navigation where vehicles encounter numerous intersections with these static objects. This paper introduces a novel method for automatically generating accurate and temporally consistent 3D bounding box annotations for traffic lights and signs, effective up to a range of 200 meters. These annotations are suitable for training real-time models used in self-driving cars, which need a large amount of training data. The proposed method relies only on RGB images with 2D bounding boxes of traffic management objects, which can be automatically obtained using an off-the-shelf image-space detector neural network, along with GNSS/INS data, eliminating the need for LiDAR point cloud data.

Dataset URL: https://www.kaggle.com/datasets/tamasmatuszka/aimotive-3d-traffic-light-and-sign-dataset

Code URL: https://github.com/aimotive/aimotive_tl_ts_dataset

Primary Area: Datasets & Benchmarks for applications in computer vision

Flagged For Ethics Review: true

Submission Number: 788