Factors Contributing to the Pedal Error or Pedal Misplacement Among Malaysian Car Drivers: A Survey

Baharom, Mohamad Zairi; Ahmad, Zair Asrar; Ismail, Nursya Mimie Ayuny; Hassan, Mohd Hasnun Arif; Karjanto, Juffrizal; Kassim, Khairil Anwar Abu

doi:10.1007/978-981-19-1577-2_5

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The researchers prepared a vehicle equipped with a camera, a force pressure sensor, a light cueing device, an audio cueing device, and Arduino hardware instrumentation to explore the emergency braking response characteristics under normal driving and emergency braking conditions. The results show that the farther the driver’s foot is from the target pedal, the greater the error occurrence will be [ 18 ]. Furthermore, based on a video camera, a physiograph, a pedal pressure sensor, and an angular tilt sensor, Wu et al explored the driver’s motion patterns and the electrophysiological characteristics during emergency brake pedal switching [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

“…Research conducted over the past 20 years has identified several factors that contribute to driver pedal misapplication [ 13 ]. These studies have demonstrated the effect of pedal construction design [ 14 ], age and gender [ 15 , 16 , 17 ], shoe contact area with the pedal, single/dual foot operation mode, foot position [ 18 , 19 ], and driver cognitive function [ 20 , 21 ] on pedal misapplication. Currently, studies on pedal misapplication rely mainly on questionnaire surveys [ 22 ], analysis of vehicle driving data [ 23 ], driver physiological data [ 15 ], video-based foot motion trajectory [ 19 ], and combinations of vehicle driving data and foot posture data [ 12 , 13 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Electric Bus Pedal Misapplication Detection Based on Phase Space Reconstruction Method

Lyu,

Li,

Zhang

et al. 2023

Sensors

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Due to the environmental protection of electric buses, they are gradually replacing traditional fuel buses. Several previous studies have found that accidents related to electric vehicles are linked to Unintended Acceleration (UA), which is mostly caused by the driver pressing the wrong pedal. Therefore, this study proposed a Model for Detecting Pedal Misapplication in Electric Buses (MDPMEB). In this work, natural driving experiments for urban electric buses and pedal misapplication simulation experiments were carried out in a closed field; furthermore, a phase space reconstruction method was introduced, based on chaos theory, to map sequence data to a high-dimensional space in order to produce normal braking and pedal misapplication image datasets. Based on these findings, a modified Swin Transformer network was built. To prevent the model from overfitting when considering small sample data and to improve the generalization ability of the model, it was pre-trained using a publicly available dataset; moreover, the weights of the prior knowledge model were loaded into the model for training. The proposed model was also compared to machine learning and Convolutional Neural Networks (CNN) algorithms. This study showed that this model was able to detect normal braking and pedal misapplication behavior accurately and quickly, and the accuracy rate on the test dataset is 97.58%, which is 9.17% and 4.5% higher than the machine learning algorithm and CNN algorithm, respectively.

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Section: Related Workmentioning

confidence: 99%