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

• URL: http://arxiv.org/abs/2409.12620v2
• Date: Mon, 23 Sep 2024 09:54:59 GMT
• Title: Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving
• Authors: Sándor Kunsági-Máté, Levente Pető, Lehel Seres, Tamás Matuszka,
• Abstract summary: 3D detection of traffic management objects, such as traffic lights and road signs, is vital for self-driving cars. This paper introduces a novel method for automatically generating 3D bounding box annotations for traffic lights and signs, effective up to a range of 200 meters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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