Collision Avoidance: A Literature Review on Threat-Assessment Techniques

For the last few decades, a lot of attention has been given to intelligent vehicle systems, and in particular to automated safety and collision avoidance solutions. In this paper, the authors present a literature review and analysis of threat-assessment methods used for collision avoidance. The authors will cover algorithms that are based on single-behavior threat metrics, optimization methods, formal methods, probabilistic frameworks, and data driven approaches, i.e., machine learning. The different theoretical algorithms are finally discussed in terms of computational complexity, robustness, and most suited applications.

• Record URL:
  https://doi.org/10.1109/TIV.2018.2886682
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2019, IEEE.
• Authors:
  • Dahl, John
  • de Campos, Gabriel Rodrigues
  • Olsson, Claes
  • Fredriksson, Jonas
• Publication Date: 2019-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 101-113
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 4
  • Issue Number: 1
  • 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: Algorithms; Crash avoidance systems; Decision making; Intelligent vehicles; Literature reviews; Machine learning; Optimization; Risk assessment; Traffic safety
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01700091
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 29 2019 10:15AM