Pedal Misapplication: Interruption Effects and Age-Related Differences

Hasegawa, Kunihiro; Kimura, Motohiro; Takeda, Yuji

doi:10.1177/0018720820936122

Cited by 8 publications

(8 citation statements)

References 26 publications

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“…The rates of accelerator error were consistently two or three times higher than the rates of brake error in both younger and older drivers (Wu et al, 2014). Hasegawa et al (2020) also found that the pedal response times of older adults were higher than younger adults. The rates of accelerator error were consistently two or three times higher among the older drivers which is more than the rates of brake error in both younger and older drivers (Hasegawa et al, 2020).…”

Section: Resultsmentioning

confidence: 63%

“…Hasegawa et al (2020) also found that the pedal response times of older adults were higher than younger adults. The rates of accelerator error were consistently two or three times higher among the older drivers which is more than the rates of brake error in both younger and older drivers (Hasegawa et al, 2020). Schmidt and Young (2010) have conducted a review that pedal misapplications can occur in other ways than at vehicle start-up.…”

Section: Resultsmentioning

confidence: 82%

“…A GPS system was used in both of the studies by Wu et al (2017) andTransportation (2013). A study by Hasegawa et al (2020) used Driving Force G29 to measure the pedal choice responses.…”

Section: Methodological Approachmentioning

confidence: 99%

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Pedal Error Among Car Drivers: A Review on The Research Approach and Setup

Baharom

Ahmad²,

Hassan³

et al. 2021

JSAEM

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The movement of the driver’s foot from one pedal to another is a blind positioning movement. Variability of driver’s foot movement might lead to pedal error or pedal misapplication. In this paper, we reviewed the research focus in the pedal error study, who are the participants involved in the study, what kind of approach or method used to perform the pedal error study, how is the test setup, test condition, and flow of the study, and what are the factors influencing the pedal error accidents. The review searched relevant articles based on five keywords: pedal errors, pedal misapplication, sudden unintended acceleration (SUA), automotive foot pedals, and sudden acceleration accidents. Based on the review, the research focus is focusing on the factors causing the pedal error, understanding the fundamental principles of pedal misapplications, and characteristics of pedal errors among older drivers. The pedal error study has been conducted either using the naturalistic driving, driving simulation, or survey. The pedal error among ASEAN drivers is essential since there is no study related to this topic has been conducted in any ASEAN countries yet.

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Section: Resultsmentioning

confidence: 63%

Section: Resultsmentioning

confidence: 82%

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Pedal Error Among Car Drivers: A Review on The Research Approach and Setup

Baharom

Ahmad²,

Hassan³

et al. 2021

JSAEM

View full text Add to dashboard Cite

show abstract

“…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%

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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“…In order to prevent the source of operation errors, many studies have attempted to detect inattention [13] [14] [15], cognitive distraction [16] [17] or driver emotion [4] [18] [19] [20] [21]. As for the pedal misapplication due to the surprise event, the previous studies focused on the foot behavior [22], foot placement [23] or interruption of the other task [24]. There is limited knowledge of what happens with the internal states and what should be improved when countermeasures fail to prevent the error.…”

Section: Introductionmentioning

confidence: 99%

Driver State Detection Based on Cardiovascular System and Driver Reaction Information Using a Graphical Model

Nguyễn¹,

Aoki²,

Son³

et al. 2021

JTTs

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Traffic accidents are mainly caused by human error. In an aging society, the number of accidents attributed to elderly drivers is increasing. One noteworthy reason for this is operation misapplication. Studies have been conducted on the use of human-machine interfaces (HMIs) to inform the driver when he or she makes an error and encourage appropriate actions. However, the driver state during the erroneous action has not been investigated. The purpose of this study is to clarify the difference in the driver's state between normal and surprising situations in a misapplication scenario, utilizing multimodal information such as biometric information and driver operation. We found significant changes in the interaction of components between the normal and the surprised driving state. The results could provide basic knowledge for the future development of a driver assistance system and driver state estimation using data acquired from multiple sensors in the vehicle.

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Pedal Misapplication: Interruption Effects and Age-Related Differences

Cited by 8 publications

References 26 publications

Pedal Error Among Car Drivers: A Review on The Research Approach and Setup

Pedal Error Among Car Drivers: A Review on The Research Approach and Setup

Electric Bus Pedal Misapplication Detection Based on Phase Space Reconstruction Method

Driver State Detection Based on Cardiovascular System and Driver Reaction Information Using a Graphical Model

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