A Survey of Driving Safety with Sensing, Vehicular Communications, and Artificial Intelligence-Based Collision Avoidance

Accurately discovering hazards and issuing appropriate warnings to drivers in advance or performing autonomous control is the core of the Collision Avoidance (CA) system used to solve traffic safety problems. More comprehensive environmental awareness, diversified communication technologies, and autonomous control can make the CA system more accurate and effective, thereby improving driving safety. In addition, the assistance of Artificial Intelligence (AI) technology can make the CA system adapt to the environment and facilitate fast and accurate decisions. Considering the current lack of a thorough survey of driving safety with sensing, vehicular communications, and AI-based collision avoidance, in this paper, the authors survey existing researches for state-of-the-art data-driven CA techniques. Firstly, the authors discuss the major steps of CA and key research issues. For each step, the authors review the existing enabling techniques and research methods for CA in detail, including sensing and vehicular communication for safe driving, as well as CA algorithm design. Particularly, the authors present a comparison between the most common AI algorithms for different functions in the CA system. Testbeds and projects for CA are summarized next. Finally, several open challenges and future research directions are also outlined.

• Record URL:
  • https://doi.org/10.1109/TITS.2021.3083927
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022 Institute of Electrical and Electronics Engineers (IEEE).
• Authors:
  • Fu, Y
  • Li, Chao
  • Yu, F R
  • Luan, T H
  • Zhang, Yu
• Publication Date: 2022-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 6142-6163
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 7
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Artificial intelligence; Crash avoidance systems; Driver performance; Driver vehicle interfaces; Vehicle design
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01856608
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 29 2022 11:32AM