Machine Learning Approach in Identifying Speed Breakers for Autonomous Driving: An Overview

Chun Sern Choong, Ahmad Fakhri Ahmad*, Anwar P. P. Abdul Majeed, Muhammad Aizzat Zakaria, Mohd Azraai Mohd Razman

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)


Advanced control systems for autonomous driving is capable of navigating vehicles without human interaction with appropriate devices by sensing the environment nearby the vehicle. Majority of such systems, autonomous vehicles 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 reconstruction-based, vibration-based and vision-based. Moreover, the common machine learning models that have been used to investigate speed breakers are also discussed.

Original languageEnglish
Title of host publicationLecture Notes in Mechanical Engineering
PublisherPleiades Publishing
Number of pages16
Publication statusPublished - 2020
Externally publishedYes

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364


Dive into the research topics of 'Machine Learning Approach in Identifying Speed Breakers for Autonomous Driving: An Overview'. Together they form a unique fingerprint.

Cite this