IIUM Repository

Deep learning based prediction model of recurrent pedal pressing for low speed

Mohd Yusri, Muhamad Khairi Idzham and Ahmad, Salmiah and Abdullah, Muhammad (2022) Deep learning based prediction model of recurrent pedal pressing for low speed. In: 8th International Conference on Mechatronics Engineering (ICOM 2022), 9th - 10th August 2022, Kuala Lumpur (virtual event).

[img] PDF (Full Paper) - Published Version
Restricted to Registered users only

Download (686kB) | Request a copy


Traffic congestion in Malaysia's major cities has become a daily phenomenon, where it is common for a person to be stuck, at least twice daily from their house to the workplace. Being trapped in traffic for hours in a sitting position demands repetitive movements of manually pressing the accelerator and brake pedals excessively, which, if the correct seating posture is not maintained, it can cause significant fatigue and in a long-term, it will negatively impact the driver's health physically and psychologically. In light of this, this paper aims to investigate the relation between the vehicle speed and the recurrent brake pedal pressings pattern at certain leg postures while being trapped in the traffic using Deep learning technique. Several sensors were used for acquisition of input and output data, which are the leg postures and force produced during recurrent braking at low speed. The system utilizes Google Colaboratory to build a model, train and test the model using Python programming language to predict the vehicle speed during the traffic jams. This study begins with an experimental setup and data collection on the brake pedal pressing force and leg posture angle, followed by modeling of the relation using LightGBM deep learning-based model. The model validation was conducted to ensure a good model accuracy, which was found to have more than 80 % accuracy.

Item Type: Conference or Workshop Item (Invited Papers)
Uncontrolled Keywords: Deep Learning Technique, Low Speed Driving, Modelling
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ210.2 Mechanical devices and figures. Automata. Ingenious mechanism. Robots (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Mechanical Engineering
Depositing User: Ir. Dr. Salmiah Ahmad
Date Deposited: 12 Jun 2023 16:47
Last Modified: 16 Jun 2023 15:45
URI: http://irep.iium.edu.my/id/eprint/105030

Actions (login required)

View Item View Item


Downloads per month over past year