Multi-Tracking Sensor Architectures for Reconstructing Autonomous Vehicle Crashes: An Exploratory Study

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aut.relation.endpage4194
aut.relation.issue13
aut.relation.journalSensors
aut.relation.startpage4194
aut.relation.volume24
dc.contributor.authorHaque, Mohammad Mahfuzul
dc.contributor.authorGhobakhlou, Akbar
dc.contributor.authorNarayanan, Ajit
dc.date.accessioned2024-07-03T02:52:21Z
dc.date.available2024-07-03T02:52:21Z
dc.date.issued2024-06-27
dc.description.abstractWith the continuous development of new sensor features and tracking algorithms for object tracking, researchers have opportunities to experiment using different combinations. However, there is no standard or agreed method for selecting an appropriate architecture for autonomous vehicle (AV) crash reconstruction using multi-sensor-based sensor fusion. This study proposes a novel simulation method for tracking performance evaluation (SMTPE) to solve this problem. The SMTPE helps select the best tracking architecture for AV crash reconstruction. This study reveals that a radar-camera-based centralized tracking architecture of multi-sensor fusion performed the best among three different architectures tested with varying sensor setups, sampling rates, and vehicle crash scenarios. We provide a brief guideline for the best practices in selecting appropriate sensor fusion and tracking architecture arrangements, which can be helpful for future vehicle crash reconstruction and other AV improvement research.
dc.identifier.citationSensors, ISSN: 1424-8220 (Online), MDPI AG, 24(13), 4194-4194. doi: 10.3390/s24134194
dc.identifier.doi10.3390/s24134194
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10292/17736
dc.languageen
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/1424-8220/24/13/4194
dc.rights© 2024 by the authors. 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/).
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject0301 Analytical Chemistry
dc.subject0502 Environmental Science and Management
dc.subject0602 Ecology
dc.subject0805 Distributed Computing
dc.subject0906 Electrical and Electronic Engineering
dc.subjectAnalytical Chemistry
dc.subject3103 Ecology
dc.subject4008 Electrical engineering
dc.subject4009 Electronics, sensors and digital hardware
dc.subject4104 Environmental management
dc.subject4606 Distributed computing and systems software
dc.titleMulti-Tracking Sensor Architectures for Reconstructing Autonomous Vehicle Crashes: An Exploratory Study
dc.typeJournal Article
pubs.elements-id558690
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