Title: Literature survey for autonomous vehicles: sensor fusion, computer vision, system identification and fault tolerance

Authors: Amr Mohamed; Jing Ren; Moustafa El-Gindy; Haoxiang Lang; A.N. Ouda

Addresses: Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe St N, Oshawa, ON L1H 7K4, Canada ' Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe St N, Oshawa, ON L1H 7K4, Canada ' Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe St N, Oshawa, ON L1H 7K4, Canada ' Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe St N, Oshawa, ON L1H 7K4, Canada ' Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe St N, Oshawa, ON L1H 7K4, Canada

Abstract: Autonomous vehicle technologies are receiving great attention with increasing demands for autonomy for both civilian and military purposes. In previous work (Mohamed et al., 2016), the recent developments in autonomous vehicles in the fields of advanced control, perception and motion planning techniques is surveyed. In this paper, the state of research w.r.t. autonomous vehicles from different perspectives will be described. The capability to integrate data and knowledge from different sensors are essential. In addition, advanced perception techniques and the capability to locate obstacles and targets are necessary to properly operate autonomous systems. Moreover, achieve reliable levels of performance by determining the faults and enabling the system to operate with these faults in mind. Fault tolerance is required to analysing the measured input/output signals of the system. This paper will briefly survey the recent developments in the field of autonomous vehicles from the perspectives of sensor fusion, computer vision, system identification and fault tolerance.

Keywords: autonomous vehicles; sensor fusion; computer vision; system identification; fault tolerance.

DOI: 10.1504/IJAAC.2018.095104

International Journal of Automation and Control, 2018 Vol.12 No.4, pp.555 - 581

Received: 10 May 2017
Accepted: 19 Aug 2017
Published online: 01 Oct 2018 *

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