Article
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Preserved in Portico This version is not peer-reviewed
Object Segmentation for Autonomous Driving Using IseAuto Data
Version 1
: Received: 1 March 2022 / Approved: 4 March 2022 / Online: 4 March 2022 (21:43:06 CET)
A peer-reviewed article of this Preprint also exists.
Gu, J.; Bellone, M.; Sell, R.; Lind, A. Object Segmentation for Autonomous Driving Using iseAuto Data. Electronics 2022, 11, 1119. Gu, J.; Bellone, M.; Sell, R.; Lind, A. Object Segmentation for Autonomous Driving Using iseAuto Data. Electronics 2022, 11, 1119.
Abstract
Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR-camera fusion and semi-supervised learning implemented in a fully-convolutional neural network. Our method is tested on real-world data acquired using our custom vehicle iseAuto shuttle. The data include all-weather scenarios, featuring night and rainy weather. In this work, it is shown that LiDAR-camera fusion with only a few annotated scenarios and semi-supervised learning, it is possible to achieve robust performance on real-world data in a multi-class object segmentation problem. The performance of our algorithm is measured in terms of intersection over union, precision, recall and area-under-the-curve average precision. Our network achieves 82% IoU in vehicle detection in day fair scenarios and 64% IoU in vehicle segmentation in night rain scenarios.
Keywords
object segmentation; LiDAR-camera fusion; autonomous driving; artificial intelligence; semi-supervised learning; iseAuto
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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