TM-fuzzer: fuzzing autonomous driving systems through traffic management

TM-fuzzer: fuzzing autonomous driving systems through traffic management | 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 Research Article TM-fuzzer: fuzzing autonomous driving systems through traffic management Shenghao Lin, Fansong Chen, Laile Xi, Gaosheng Wang, Rongrong Xi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4185312/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jul, 2024 Read the published version in Automated Software Engineering → Version 1 posted 11 You are reading this latest preprint version Abstract Simulation testing of Autonomous Driving Systems (ADS) is crucial for ensuring the safety of autonomous vehicles. Currently, scenarios searched by ADS simulation testing tools are less likely to expose ADS issues and highly similar. In this paper, we propose TM-fuzzer, a novel approach for searching ADS test scenarios, which utilizes real-time traffic management and diversity analysis to search security-critical and unique scenarios within the infinite scenario space. TM-fuzzer dynamically manages traffic flow by manipulating non-player characters near autonomous vehicle throughout the simulation process to enhance the efficiency of test scenarios. Additionally, the TM-fuzzer utilizes clustering analysis on vehicle trajectory graphs within scenarios to increase the diversity of test scenarios. Compared to the baseline, the TM-fuzzer identified 29 unique violated scenarios more than four times faster and enhanced the incidence of ADS-caused violations by 26.26%. Experiments suggest that the TM-fuzzer demonstrates improved efficiency and accuracy. Autonomous driving system Fuzzing Search-based testing Critical scenarios Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Jul, 2024 Read the published version in Automated Software Engineering → Version 1 posted Editorial decision: Revision requested 12 May, 2024 Reviews received at journal 10 May, 2024 Reviews received at journal 06 May, 2024 Reviews received at journal 06 May, 2024 Reviewers agreed at journal 30 Apr, 2024 Reviewers agreed at journal 08 Apr, 2024 Reviewers agreed at journal 08 Apr, 2024 Reviewers invited by journal 06 Apr, 2024 Editor assigned by journal 31 Mar, 2024 Submission checks completed at journal 30 Mar, 2024 First submitted to journal 28 Mar, 2024 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. 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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-4185312","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286350277,"identity":"50761d64-4fc9-4e48-9ce5-bb255c375ca2","order_by":0,"name":"Shenghao 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