Multimodal Generative AI for Vehicular Applications at Edge

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume :16 | Issue : 01 | Page : 1-10
By
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Sundaresan Poovalingam,

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Bhoomi Shah,

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Rani Malhotra,

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Nikhil Nandanwar,

  1. 1Distinguished Technologist, Advanced Engineering Group, Infosys Limited, Bengaluru, Karnataka, India.
  2. Constultant, Advanced Engineering Group, Infosys Limited, Bengaluru, Karnataka, India.
  3. Lead Constultant, Advanced Engineering Group, Infosys Limited, Bengaluru, Karnataka, India.
  4. Lead Constultant, Advanced Engineering Group, Infosys Limited, Bengaluru, Karnataka, India.

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This paper explores the rapid advancements in vehicular technology, including autonomous driving and intelligent transportation systems, have driven the need for real-time data processing and decision-making. Multimodal generative AI, when deployed at the edge, offers a powerful solution for vehicular applications by leveraging diverse data sources such as video, audio, sensor inputs, and environmental data. This paper explores the integration of multimodal generative AI with edge computing in vehicular networks, particularly for use cases like autonomous platooning, predictive maintenance, anomaly detection, and enhanced security. By processing data locally at the edge using AI chips, vehicles can respond instantly to critical events without relying on distant cloud servers, reducing latency and improving safety. Furthermore, vehicular ad-hoc networks (VANETs) play a crucial role in supporting decentralized, low-latency communication between vehicles and infrastructure. This fusion of multimodal AI and edge computing unlocks the potential for more intelligent, efficient, and resilient vehicular systems, paving the way for next-generation transportation and smart city infrastructures.

Keywords: VANETs, AI chips, Platooning, multimodal AI, networks, traffic conditions

[This article belongs to Journal of Control & Instrumentation (joci)]

How to cite this article:
Sundaresan Poovalingam, Bhoomi Shah, Rani Malhotra, Nikhil Nandanwar. Multimodal Generative AI for Vehicular Applications at Edge. Journal of Control & Instrumentation. 2024; 16(01):1-10.
How to cite this URL:
Sundaresan Poovalingam, Bhoomi Shah, Rani Malhotra, Nikhil Nandanwar. Multimodal Generative AI for Vehicular Applications at Edge. Journal of Control & Instrumentation. 2024; 16(01):1-10. Available from: https://journals.stmjournals.com/joci/article=2024/view=0

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Regular Issue Subscription Review Article
Volume 16
Issue 01
Received 09/12/2024
Accepted 14/12/2024
Published 27/12/2024
283