Collision Avoidance: A Literature Review on Threat-Assessment Techniques
Journal article, 2019

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, we present a literature review and analysis of threat-assessment methods used for collision avoidance. We 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.

Safety

Optimization

Measurement

Vehicles

Decision making

Acceleration

Collision avoidance

Author

John Dahl

Chalmers, Electrical Engineering, Systems and control

Zenuity AB

Gabriel Rodrigues de Campos

Zenuity AB

Claes Olsson

Zenuity AB

Jonas Fredriksson

Zenuity AB

IEEE Transactions on Intelligent Vehicles

23798858 (eISSN)

Vol. 4 1 101-113 574961

Areas of Advance

Transport

Subject Categories

Robotics

Computer Science

Computer Systems

DOI

10.1109/TIV.2018.2886682

More information

Latest update

1/3/2024 9