{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T03:14:55Z","timestamp":1791515695103,"version":"4.3.4"},"reference-count":66,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Lilly Endowment (AnalytixIN)"},{"name":"Enhanced Mentoring Program with Opportunities for Ways to Excel in Research (EMPOWER)"},{"name":"Indiana University\u2013Purdue University Indianapolis"},{"name":"Lilly Endowment, Inc."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The recent advancements in autonomous driving come with the associated cybersecurity issue of compromising networks of autonomous vehicles (AVs), motivating the use of AI models for detecting anomalies on these networks. In this context, the usage of explainable AI (XAI) for explaining the behavior of these anomaly detection AI models is crucial. This work introduces a comprehensive framework to assess black-box XAI techniques for anomaly detection within AVs, facilitating the examination of both global and local XAI methods to elucidate the decisions made by XAI techniques that explain the behavior of AI models classifying anomalous AV behavior. By considering six evaluation metrics (descriptive accuracy, sparsity, stability, efficiency, robustness, and completeness), the framework evaluates two well-known black-box XAI techniques, SHAP and LIME, involving applying XAI techniques to identify primary features crucial for anomaly classification, followed by extensive experiments assessing SHAP and LIME across the six metrics using two prevalent autonomous driving datasets, VeReMi and Sensor. This study advances the deployment of black-box XAI methods for real-world anomaly detection in autonomous driving systems, contributing valuable insights into the strengths and limitations of current black-box XAI methods within this critical domain.<\/jats:p>","DOI":"10.3390\/s24113515","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T06:08:14Z","timestamp":1717049294000},"page":"3515","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["On Evaluating Black-Box Explainable AI Methods for Enhancing Anomaly Detection in Autonomous Driving Systems"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1163-6448","authenticated-orcid":false,"given":"Sazid","family":"Nazat","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering Department, Purdue School of Engineering and Technology, Indiana University\u2014Purdue University Indianapolis, Indianapolis, IN 46202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8007-754X","authenticated-orcid":false,"given":"Osvaldo","family":"Arreche","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Purdue School of Engineering and Technology, Indiana University\u2014Purdue University Indianapolis, Indianapolis, IN 46202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9554-9260","authenticated-orcid":false,"given":"Mustafa","family":"Abdallah","sequence":"additional","affiliation":[{"name":"Computer and Information Technology Department, Purdue School of Engineering and Technology, Indiana University\u2014Purdue University Indianapolis, Indianapolis, IN 46202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1007\/s40534-016-0117-3","article-title":"Autonomous vehicles: Challenges, opportunities, and future implications for transportation policies","volume":"24","author":"Bagloee","year":"2016","journal-title":"J. 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