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Machine learning approach in identifying speed breakers for autonomous driving: an overview

Choong, Chun Sern and Ahmad Fakhri, Ab. Nasir and Anwar, P. P. Abdul Majeed and Muhammad Aizzat, Zakaria and Mohd Azraai, M. Razman (2019) Machine learning approach in identifying speed breakers for autonomous driving: an overview. In: The 6th International Conference For Robotics Intelligence And Applications 2018, 16 - 18 Disember 2018 , Pullman Lakeside Hotel, Putrajaya, Malaysia. pp. 1-15.. ISBN 978-981-13-8322-9

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Abstract

Advanced control systems for autonomous driving is capable of nav-igating vehicles without human interaction with appropriate devices by sensing the environment nearby the vehicle. Majority of such systems, autonomous ve-hicles implement a deliberative architecture that will pave the way for vehicle tracking, vehicle recognition, and collision avoidance. This paper provides a brief overview of the most advanced and recent approaches taken to detect and track speed breakers that employ various devices that allows pattern recognition. The discussion of various speed breaker detection will be limited to 3D recon-struction-based, vibration-based and vision-based. Moreover, the common ma-chine learning models that have been used to investigate speed breakers are also discussed.

Item Type: Conference or Workshop Item (Keynote)
Uncontrolled Keywords: Breakers; Autonomous driving; Machine learning
Subjects: T Technology > TS Manufactures
Faculty/Division: Faculty of Manufacturing Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 14 Oct 2019 09:05
Last Modified: 14 Oct 2019 09:05
URI: http://umpir.ump.edu.my/id/eprint/24517
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