Vehicular Visualization: Enhancing Mobility Decisions With Human–Machine Interactions

In the age of intelligence, humans pursue a safe and convenient mobility experience. Intelligent vehicles have integrated various machine intelligence to support humans in making decisions on mobility. However, the support fails to meet human expectations because machine intelligence lacks a method to communicate with humans—verify the understanding of human needs, and explain how machine intelligence works. In this study, the authors address this issue through vehicular visualizations. Specifically, the authors summarize the decision-making requirements of humans, introduce how can techniques of vehicular visualizations satisfy these needs, describe prospective application scenes, and discuss future directions of vehicular visualizations to inspire related scholars or developers.

• Record URL:
  http://dx.doi.org/10.1109/TIV.2023.3288907
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Wang, Xumeng
  • Wang, Xingxia
  • Ma, Siji
  • Chen, Wei
  • Wang, Fei-Yue
• Publication Date: 2023-11

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 4653-4663
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 8
  • Issue Number: 11
  • 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: Behavior; Decision making; Intelligent vehicles; Machine learning; Visualization
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01905987
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 26 2024 10:02AM