Collision Avoidance: A Literature Review on Threat-Assessment Techniques

• Published in: IEEE transactions on intelligent vehicles Vol. 4; no. 1; pp. 101 - 113
• Main Authors: Dahl, John, de Campos, Gabriel Rodrigues, Olsson, Claes, Fredriksson, Jonas
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-03-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; active-safety systems; Algorithms; Collision avoidance; Collisions; Decision making; decision-making methods; Intelligent vehicles; Literature reviews; Machine learning; Measurement; Optimization; Probabilistic methods; Robustness (mathematics); Safety; Threat-assessment algorithms; Vehicles
• ISSN: 2379-8858; 2379-8904
• DOI: 10.1109/TIV.2018.2886682

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.