VistaGPT: Generative Parallel Transformers for Vehicles With Intelligent Systems for Transport Automation

Diverse transport demands have resulted in the wide existence of heterogeneous vehicle automation systems. While these systems have demonstrated effectiveness, they also pose challenges in terms of the share of technological advancements among different organizations and lead to poor generalization ability of individual systems. This article proposes a Transformer-based unified framework, VistaGPT, to address these challenges. VistaGPT, composed of Modular Federations of Vehicular Transformers (M-FoV) and Automated Composing of Autonomous Driving Systems (AutoAuto), aims to overcome the information barriers due to system-level and module-level heterogeneity. M-FoV collects and organizes Transformer-based models in a modular fashion to facilitate system integration by providing diversity and versatility. AutoAuto utilizes large language models (LLMs) to automatically compose end-to-end autonomous driving systems with a “Dividing and Recombining” strategy. Besides, the authors deploy Scenario Engineering systems to evaluate the composed systems and provide systematic feedback for the optimization of AutoAuto, and Federated intelligence to contribute to diverse training samples and applications. With its capacity, scalability, and diversity, VistaGPT provides a new paradigm of LLM-aided system development for transport automation, which promotes virtual-real interactive parallel driving and advances progress toward “6S” objectives.

• Record URL:
  http://dx.doi.org/10.1109/TIV.2023.3307012
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Tian, Yonglin
  • Li, Xuan
  • Zhang, Hui
  • Zhao, Chen
  • Li, Bai
  • Wang, Xiao
  • Wang, Xiao
  • Wang, Fei-Yue
• Publication Date: 2023-9

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 4198-4207
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 8
  • Issue Number: 9
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2379-8858
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Intelligent transportation systems; Machine learning; Navigation; Trajectory control; Transformers
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01900710
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 28 2023 10:37AM