Bai, SaveetaKilby, JeffPrasad, Krishnamachar2026-03-162026-03-162026-03-05Sensors (Basel), ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI AG, 26(5), 1658-. doi: 10.3390/s260516581424-82201424-8220http://hdl.handle.net/10292/20777Vehicular communication networks demand highly efficient and accurate channel estimation to ensure reliable data exchange in high mobility scenarios. The IEEE 802.11p standard is widely regarded as the foundation of the Vehicle-to-Vehicle (V2V) communication channel; however, it is constrained by limited pilot resources and a fixed pilot structure, which degrade the performance and effectiveness of traditional estimation techniques, particularly in dynamic environments. Recent advances in deep learning offer significant potential for addressing these issues by improving estimation accuracy and modelling complex channel dynamics. Though deep learning-based methods introduce trade-offs in computational complexity and accuracy, these are crucial constraints in latency-sensitive V2V scenarios. This article presents a comprehensive review of deep learning-based channel estimation techniques, analysing methods for the IEEE 802.11p standard and critically examining their limitations in both classical and deep learning-based approaches. Additionally, the article highlights improvements introduced by IEEE 802.11bd, which features an enhanced pilot structure and advanced modulation schemes, providing a more robust framework for adaptive, efficient channel estimation. By identifying future research pathways that balance delay, complexity, and accuracy, an intelligent and effective transportation system can be established.© 2026 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.DSRCIEEE802.11bdIEEE802.11pV2Vchannel estimationdeep learning 802.11bd VANET46 Information and Computing Sciences4006 Communications Engineering40 EngineeringNetworking and Information Technology R&D (NITRD)Machine Learning and Artificial Intelligence0301 Analytical Chemistry0502 Environmental Science and Management0602 Ecology0805 Distributed Computing0906 Electrical and Electronic EngineeringAnalytical Chemistry3103 Ecology4008 Electrical engineering4009 Electronics, sensors and digital hardware4104 Environmental management4606 Distributed computing and systems softwareJournal ArticleOpenAccess10.3390/s26051658