TY - CHAP
T1 - Machine Learning Approach in Identifying Speed Breakers for Autonomous Driving
T2 - An Overview
AU - Choong, Chun Sern
AU - Ahmad, Ahmad Fakhri
AU - P. P. Abdul Majeed, Anwar
AU - Zakaria, Muhammad Aizzat
AU - Mohd Razman, Mohd Azraai
N1 - Funding Information:
Universiti Malaysia Pahang fully supports the facilities and resources for this research. The authors would like to acknowledge the support of the internal grants of Universiti Malaysia Pahang (RDU1703159 and RDU180383).
Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2020.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067643092&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-8323-6_35
DO - 10.1007/978-981-13-8323-6_35
M3 - Chapter
AN - SCOPUS:85067643092
T3 - Lecture Notes in Mechanical Engineering
SP - 409
EP - 424
BT - Lecture Notes in Mechanical Engineering
PB - Pleiades Publishing
ER -