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Synergizing Generative Intelligence: Advancements in Artificial Intelligence for Intelligent Vehicle Systems and Vehicular Networks

Archismita Ghosh Gaddam Prathik Kumar Paarth Prasad Dheeraj Kumar Samyak Jain Jatin Chopra

Subject area: Science,Engineering and Technology  ·  Area of research: Artificial intelligence

Abstract

This research paper presents a comprehensive exploration of generative artificial intelligence (AI) and its transformative impact on intelligent vehicles and vehicular networks. In the context of intelligent vehicles, the current state and future potential of generative AI technologies, emphasizing their applications in speech, audio, vision, and multimodal interactions are examined. The paper outlines critical future research areas, including domain adaptability, alignment, and multimodal integration, addressing associated ethical challenges. Simultaneously, recognizing the immense benefits of integrating generative AI into intelligent transportation systems, applications and challenges within vehicular networks are discussed. The integration of generative AI enhances various aspects, including navigation optimization, traffic prediction, and data generation, while facing challenges such as real-time processing and privacy concerns. To address these challenges, a multi-modality semantic-aware framework is proposed, leveraging text and image data to enhance generative AI service quality. A deep reinforcement learning (DRL)--based approach for resource allocation in generative AI-enabled vehicle-to-vehicle (V2V) communication is presented in a case study form. By synthesizing insights from both domains, this paper advocates for collaborative research efforts to unlock the full potential of generative AI, fostering transformative advancements in the driving experience and the evolution of intelligent vehicles and vehicular networks.

Keywords

Generative AI, Intelligent Vehicles, Vehicular Networks, Multi-modal Interactions, V2V, DRL

How to cite this paper

Archismita Ghosh, Gaddam Prathik Kumar, Paarth Prasad, Dheeraj Kumar, Samyak Jain; Jatin Chopra "Synergizing Generative Intelligence: Advancements in Artificial Intelligence for Intelligent Vehicle Systems and Vehicular Networks" Iconic Research And Engineering Journals, vol. 7, no. 6, Dec. 2023
Archismita Ghosh, Gaddam Prathik Kumar, Paarth Prasad, Dheeraj Kumar, Samyak Jain; Jatin Chopra (2023). Synergizing Generative Intelligence: Advancements in Artificial Intelligence for Intelligent Vehicle Systems and Vehicular Networks. Iconic Research And Engineering Journals, 7(6).
Archismita Ghosh, Gaddam Prathik Kumar, Paarth Prasad, Dheeraj Kumar, Samyak Jain; Jatin Chopra "Synergizing Generative Intelligence: Advancements in Artificial Intelligence for Intelligent Vehicle Systems and Vehicular Networks" Iconic Research And Engineering Journals, vol. 7, no. 6, Dec. 2023.
@article{1705287,
      author = {Archismita Ghosh, Gaddam Prathik Kumar, Paarth Prasad, Dheeraj Kumar, Samyak Jain; Jatin Chopra},
      title = {Synergizing Generative Intelligence: Advancements in Artificial Intelligence for Intelligent Vehicle Systems and Vehicular Networks},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {6},
      pages = {92-104},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705287.pdf},
      abstract = {This research paper presents a comprehensive exploration of generative artificial intelligence (AI) and its transformative impact on intelligent vehicles and vehicular networks. In the context of intelligent vehicles, the current state and future potential of generative AI technologies, emphasizing their applications in speech, audio, vision, and multimodal interactions are examined. The paper outlines critical future research areas, including domain adaptability, alignment, and multimodal integration, addressing associated ethical challenges. Simultaneously, recognizing the immense benefits of integrating generative AI into intelligent transportation systems, applications and challenges within vehicular networks are discussed. The integration of generative AI enhances various aspects, including navigation optimization, traffic prediction, and data generation, while facing challenges such as real-time processing and privacy concerns. To address these challenges, a multi-modality semantic-aware framework is proposed, leveraging text and image data to enhance generative AI service quality. A deep reinforcement learning (DRL)--based approach for resource allocation in generative AI-enabled vehicle-to-vehicle (V2V) communication is presented in a case study form. By synthesizing insights from both domains, this paper advocates for collaborative research efforts to unlock the full potential of generative AI, fostering transformative advancements in the driving experience and the evolution of intelligent vehicles and vehicular networks.},
      keywords = {Generative AI, Intelligent Vehicles, Vehicular Networks, Multi-modal Interactions, V2V, DRL},
      month = {December},
  }