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Machines 2017, 5(1), 6; doi:10.3390/machines5010006

Perception, Planning, Control, and Coordination for Autonomous Vehicles

1
Department of Mechanical Engineering, National University of Singapore, Singapore 119077, Singapore
2
Future Urban Mobility, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore
3
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Robert Parkin
Received: 3 January 2017 / Revised: 1 February 2017 / Accepted: 13 February 2017 / Published: 17 February 2017
(This article belongs to the Special Issue Mechatronics: Intelligent Machines)
View Full-Text   |   Download PDF [1113 KB, uploaded 28 February 2017]   |  

Abstract

Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed. View Full-Text
Keywords: autonomous vehicles; localization; perception; planning; automotive control; multi-vehicle cooperation autonomous vehicles; localization; perception; planning; automotive control; multi-vehicle cooperation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pendleton, S.D.; Andersen, H.; Du, X.; Shen, X.; Meghjani, M.; Eng, Y.H.; Rus, D.; Ang, M.H. Perception, Planning, Control, and Coordination for Autonomous Vehicles. Machines 2017, 5, 6.

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