Collision Avoidance: A Literature Review on Threat-Assessment Techniques
Artikel i vetenskaplig tidskrift, 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.

Acceleration

Safety

Optimization

Decision making

Collision avoidance

Measurement

Vehicles

Författare

John Dahl

Zenuity

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

Gabriel Rodrigues de Campos

Zenuity

Claes Olsson

Zenuity

Jonas Fredriksson

Chalmers, Elektroteknik, System- och reglerteknik, Mekatronik

IEEE Transactions on Intelligent Vehicles

2379-8904 (eISSN)

Vol. 4 1 101-113 574961

Styrkeområden

Transport

Ämneskategorier

Robotteknik och automation

Datavetenskap (datalogi)

Datorsystem

DOI

10.1109/TIV.2018.2886682

Mer information

Senast uppdaterat

2020-04-22