{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T22:43:37Z","timestamp":1791240217187,"version":"4.1.0"},"reference-count":58,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The rapid progress of sensing technologies, artificial intelligence, and embedded computing has significantly accelerated the development of autonomous vehicles. Among the core challenges of higher-level driving automation, reliable environmental perception remains one of the most critical. This review presents a systematic PRISMA-based analysis of multimodal sensor technologies and fusion architectures applied in autonomous driving, based on 66 peer-reviewed studies published between 2014 and 2025. The study examines the operational characteristics, advantages, and limitations of major sensing modalities, including cameras, LiDAR, radar, ultrasonic sensors, and GNSS\/IMU-based localization systems. Particular attention is given to multimodal fusion strategies, covering early, mid-level, high-level, and transformer-based architectures that combine complementary sensor information to improve perception robustness and decision reliability. The review further synthesizes current evidence on performance under adverse environmental conditions, benchmark validation practices, real-time computational constraints, and the growing role of functional safety frameworks such as ISO 26262 and SOTIF. Emerging research directions, including 4D radar, self-supervised long-range fusion, foundation models, and cooperative V2X perception, are also discussed. The findings indicate that multimodal sensor fusion is a highly effective architectural strategy for improving scalability, fail-operational robustness, and certifiable safety in autonomous driving systems, particularly in higher-level automation scenarios. Future research should focus on uncertainty-aware fusion, explainable cross-modal reasoning, large-scale real-world validation, and efficient hardware\u2013software co-design to support robust Level 4\u20135 vehicle autonomy.<\/jats:p>","DOI":"10.3390\/s26113528","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T14:38:42Z","timestamp":1780411122000},"page":"3528","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multimodal Sensor Fusion in Autonomous Vehicles: Technologies, Architectures, and Open Challenges"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8689-2753","authenticated-orcid":false,"given":"Patrik","family":"Viktor","sequence":"first","affiliation":[{"name":"Keleti K\u00e1roly Faculty of Business and Management, Obuda University, 1034 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0447-9376","authenticated-orcid":false,"given":"Gabor","family":"Kiss","sequence":"additional","affiliation":[{"name":"Institute of Safety Science and Cybersecurity, Obuda University, 1034 Budapest, Hungary"},{"name":"Department of Computer Science, J. Selye University, 945 01 Komarno, Slovakia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"b2535","DOI":"10.1136\/bmj.b2535","article-title":"Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement","volume":"339","author":"Moher","year":"2009","journal-title":"BMJ"},{"key":"ref_3","unstructured":"SAE International (2021). Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles (SAE J3016), SAE International."},{"key":"ref_4","unstructured":"(2011). 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