Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles

This paper presents a fail-safe algorithm for the exteroceptive sensors of autonomous vehicles. The proposed fault diagnosis mechanism consists of three parts: (1) fault detecting by a duplication-comparison method, (2) fault isolating by possible area prediction and (3) in-vehicle sensor fail-safes...

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Main Authors: Donghoon Shin, Kang-moon Park, Manbok Park
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/9/11/1774
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spelling doaj-3b67e4bf82a547ff8173ba254f1695ee2020-11-25T03:58:31ZengMDPI AGElectronics2079-92922020-10-0191774177410.3390/electronics9111774Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous VehiclesDonghoon Shin0Kang-moon Park1Manbok Park2Department of Mechanical Systems Engineering, Sookmyung Women’s University, Seoul 04310, KoreaDepartment of Computer Science, College of Natural Science, Republic of Korea Naval Academy, Changwon-si 51704, KoreaDepartment of Electrical Engineering, College of Convergence Technology, Korea National University of Transportation, Chungju-si 27469, KoreaThis paper presents a fail-safe algorithm for the exteroceptive sensors of autonomous vehicles. The proposed fault diagnosis mechanism consists of three parts: (1) fault detecting by a duplication-comparison method, (2) fault isolating by possible area prediction and (3) in-vehicle sensor fail-safes. The main ideas are the usage of redundant external sensor pairs, which estimate the same target, whose results are compared to detect the fault by a modified duplication-comparison method and the novel fault isolation method using target predictions. By comparing the estimations of surrounding vehicles and the raw measurement data, the location of faults can be determined whether they are from sensors themselves or a software error. In addition, faults were isolated by defining possible areas where existing sensor coordinates could be measured, which can be predicted by using previous estimation results. The performance of the algorithm has been tested by using offline vehicle data analysis via MATLAB. Various fault injection experiments were conducted and the performance of the suggested algorithm was evaluated based on the time interval between injection and the detection of faults.https://www.mdpi.com/2079-9292/9/11/1774fail-safefault injectionfault isolation mechanismtarget predictions
collection DOAJ
language English
format Article
sources DOAJ
author Donghoon Shin
Kang-moon Park
Manbok Park
spellingShingle Donghoon Shin
Kang-moon Park
Manbok Park
Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles
Electronics
fail-safe
fault injection
fault isolation mechanism
target predictions
author_facet Donghoon Shin
Kang-moon Park
Manbok Park
author_sort Donghoon Shin
title Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles
title_short Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles
title_full Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles
title_fullStr Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles
title_full_unstemmed Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles
title_sort development of fail-safe algorithm for exteroceptive sensors of autonomous vehicles
publisher MDPI AG
series Electronics
issn 2079-9292
publishDate 2020-10-01
description This paper presents a fail-safe algorithm for the exteroceptive sensors of autonomous vehicles. The proposed fault diagnosis mechanism consists of three parts: (1) fault detecting by a duplication-comparison method, (2) fault isolating by possible area prediction and (3) in-vehicle sensor fail-safes. The main ideas are the usage of redundant external sensor pairs, which estimate the same target, whose results are compared to detect the fault by a modified duplication-comparison method and the novel fault isolation method using target predictions. By comparing the estimations of surrounding vehicles and the raw measurement data, the location of faults can be determined whether they are from sensors themselves or a software error. In addition, faults were isolated by defining possible areas where existing sensor coordinates could be measured, which can be predicted by using previous estimation results. The performance of the algorithm has been tested by using offline vehicle data analysis via MATLAB. Various fault injection experiments were conducted and the performance of the suggested algorithm was evaluated based on the time interval between injection and the detection of faults.
topic fail-safe
fault injection
fault isolation mechanism
target predictions
url https://www.mdpi.com/2079-9292/9/11/1774
work_keys_str_mv AT donghoonshin developmentoffailsafealgorithmforexteroceptivesensorsofautonomousvehicles
AT kangmoonpark developmentoffailsafealgorithmforexteroceptivesensorsofautonomousvehicles
AT manbokpark developmentoffailsafealgorithmforexteroceptivesensorsofautonomousvehicles
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