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Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion

The reliability of autonomous driving sensing systems impacts the overall safety of the driving system. However, perception system fault diagnosis is currently a weak area of research, with limited attention and solutions. In this paper, we present an information-fusion-based fault-diagnosis method...

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Detalles Bibliográficos
Autores principales: Hou, Wenkui, Li, Wanyu, Li, Pengyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255210/
https://www.ncbi.nlm.nih.gov/pubmed/37299837
http://dx.doi.org/10.3390/s23115110
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author Hou, Wenkui
Li, Wanyu
Li, Pengyu
author_facet Hou, Wenkui
Li, Wanyu
Li, Pengyu
author_sort Hou, Wenkui
collection PubMed
description The reliability of autonomous driving sensing systems impacts the overall safety of the driving system. However, perception system fault diagnosis is currently a weak area of research, with limited attention and solutions. In this paper, we present an information-fusion-based fault-diagnosis method for autonomous driving perception systems. To begin, we built an autonomous driving simulation scenario using PreScan software, which collects information from a single millimeter wave (MMW) radar and a single camera sensor. The photos are then identified and labeled via the convolutional neural network (CNN). Then, we fused the sensory inputs from a single MMW radar sensor and a single camera sensor in space and time and mapped the MMW radar points onto the camera image to obtain the region of interest (ROI). Lastly, we developed a method to use information from a single MMW radar to aid in diagnosing defects in a single camera sensor. As the simulation results show, for missing row/column pixel failure, the deviation typically falls between 34.11% and 99.84%, with a response time of 0.02 s to 1.6 s; for pixel shift faults, the deviation range is between 0.32% and 9.92%, with a response time of 0 s to 0.16 s; for target color loss, faults have a deviation range of 0.26% to 2.88% and a response time of 0 s to 0.05 s. These results prove the technology is effective in detecting sensor faults and issuing real-time fault alerts, providing a basis for designing and developing simpler and more user-friendly autonomous driving systems. Furthermore, this method illustrates the principles and methods of information fusion between camera and MMW radar sensors, establishing the foundation for creating more complicated autonomous driving systems.
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spelling pubmed-102552102023-06-10 Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion Hou, Wenkui Li, Wanyu Li, Pengyu Sensors (Basel) Article The reliability of autonomous driving sensing systems impacts the overall safety of the driving system. However, perception system fault diagnosis is currently a weak area of research, with limited attention and solutions. In this paper, we present an information-fusion-based fault-diagnosis method for autonomous driving perception systems. To begin, we built an autonomous driving simulation scenario using PreScan software, which collects information from a single millimeter wave (MMW) radar and a single camera sensor. The photos are then identified and labeled via the convolutional neural network (CNN). Then, we fused the sensory inputs from a single MMW radar sensor and a single camera sensor in space and time and mapped the MMW radar points onto the camera image to obtain the region of interest (ROI). Lastly, we developed a method to use information from a single MMW radar to aid in diagnosing defects in a single camera sensor. As the simulation results show, for missing row/column pixel failure, the deviation typically falls between 34.11% and 99.84%, with a response time of 0.02 s to 1.6 s; for pixel shift faults, the deviation range is between 0.32% and 9.92%, with a response time of 0 s to 0.16 s; for target color loss, faults have a deviation range of 0.26% to 2.88% and a response time of 0 s to 0.05 s. These results prove the technology is effective in detecting sensor faults and issuing real-time fault alerts, providing a basis for designing and developing simpler and more user-friendly autonomous driving systems. Furthermore, this method illustrates the principles and methods of information fusion between camera and MMW radar sensors, establishing the foundation for creating more complicated autonomous driving systems. MDPI 2023-05-26 /pmc/articles/PMC10255210/ /pubmed/37299837 http://dx.doi.org/10.3390/s23115110 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hou, Wenkui
Li, Wanyu
Li, Pengyu
Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion
title Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion
title_full Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion
title_fullStr Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion
title_full_unstemmed Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion
title_short Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion
title_sort fault diagnosis of the autonomous driving perception system based on information fusion
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10255210/
https://www.ncbi.nlm.nih.gov/pubmed/37299837
http://dx.doi.org/10.3390/s23115110
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