Research on Target Detection for AutonomousDriving Based on ECS-Spiking Neural Networks

Research on Target Detection for AutonomousDriving Based on ECS-Spiking Neural Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research on Target Detection for AutonomousDriving Based on ECS-Spiking Neural Networks miao jin, Xiaohong Wang, Ce Guo, Shufan Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5839825/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 6 You are reading this latest preprint version Abstract In response to the increasing demands for improved model performance and reduced energy consumption in object detection tasks relevant to autonomous driving, this research presents an advanced YOLO model, designated as ECSLIF-YOLO, which is based on the Leaky Integrate-and-Fire with Extracellular Space (ECS-LIF) framework. The primary aim of this model is to tackle the issues associated with the high energy consumption of traditional artificial neural networks (ANNs) and the suboptimal performance of existing spiking neural networks (SNNs). Empirical findings demonstrate that ECSLIF-YOLO achieves a peak mean Average Precision (mAP) of 0.917 on the BDD100K and KITTI datasets, thereby aligning with the accuracy levels of conventional ANNs while exceeding the performance of current direct-training SNN approaches without incurring additional energy costs. These findings suggest that ECSLIF-YOLO is particularly well-suited to assist the development of efficient and reliable systems for autonomous driving. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 08 Apr, 2025 Reviews received at journal 07 Apr, 2025 Reviewers agreed at journal 05 Apr, 2025 Reviewers invited by journal 03 Apr, 2025 Submission checks completed at journal 31 Mar, 2025 First submitted to journal 31 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5839825","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":438923220,"identity":"e08d670d-7d6c-453c-a026-43ca9e66e418","order_by":0,"name":"miao jin","email":"","orcid":"","institution":"LiaoNing Petrochemical University","correspondingAuthor":false,"prefix":"","firstName":"miao","middleName":"","lastName":"jin","suffix":""},{"id":438923221,"identity":"cdd2c121-2621-4b9f-8f2a-3187e27b7dd9","order_by":1,"name":"Xiaohong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYNACAzBiYPhQAOFLEK2FcYYB0VoguhiYeYjRYnD87OHXPAV2cubsvYdf2xjcyTM4wHzwNg+DXR5OLWfy0ixnGCQbW/acS7POMXhWbHCALdmahyG5GJcWswM5ZgYfDJgTN9zIMTPOMTicuOEAj5k0D8OBxAZcWs6/MTNIMKiHaLEAa+H/hl/LjRzjBx9AKoGMxwwQW9jwarG/8cYMGLbHjQ3OnDFj7DE4XCx5mM3Yco5BMk4tkv05xp95/lTLGRzvMf7wo+JwHt/x5oc33lTY4dQCBGwSyIwEBmYQ2wC3eiBg/oDMSMCrdhSMglEwCkYkAADCtVnGI+zA1QAAAABJRU5ErkJggg==","orcid":"","institution":"LiaoNing Petrochemical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Wang","suffix":""},{"id":438923222,"identity":"f761f71d-d2da-44d5-ad3c-75713bcca7b9","order_by":2,"name":"Ce Guo","email":"","orcid":"","institution":"Bengbu Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ce","middleName":"","lastName":"Guo","suffix":""},{"id":438923223,"identity":"4534d3f9-8dac-49f7-bf82-bbf5457e5d97","order_by":3,"name":"Shufan Yang","email":"","orcid":"","institution":"The Second Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Shufan","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-01-16 08:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5839825/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5839825/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-97913-4","type":"published","date":"2025-04-21T15:56:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81569602,"identity":"83014a68-56f9-4903-b4bf-490c09fd2e72","added_by":"auto","created_at":"2025-04-28 16:07:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4697184,"visible":true,"origin":"","legend":"","description":"","filename":"qvbpnkqdhsmhbppxxrkkskxpttrmxgmk.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5839825/v1_covered_14c40cd3-e634-4483-b8b4-4c15d0321883.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Target Detection for AutonomousDriving Based on ECS-Spiking Neural Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5839825/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5839825/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In response to the increasing demands for improved model performance and reduced energy consumption in object detection tasks relevant to autonomous driving, this research presents an advanced YOLO model, designated as ECSLIF-YOLO, which is based on the Leaky Integrate-and-Fire with Extracellular Space (ECS-LIF) framework. 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