Identifying Pedal Misapplication Behavior Using Event Data Recorders

Smith, Colin P.; Sherony, Rini; Gabler, Hampton C.; Riexinger, Luke E.

doi:10.4271/2022-01-0817

Cited by 2 publications

(8 citation statements)

References 6 publications

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“…In addition to this, Smith et al (2021) further examined the characteristics of crashes resulting from pedal misapplication. This study also reviewed crash descriptions and narratives in the North Carolina state crash database (between 1 st January 2014 and 31 st May 2020) alongside the National Motor Vehicle Crash Causation Survey (NMVCCS) database (between 1 st January 2005 and 31 st December 2007).…”

Section: Crash Characteristicsmentioning

confidence: 99%

“…Alongside examining crash characteristics, Smith et al (2021) also explored the effect of driver characteristics such as gender on pedal misapplication. The study identified that of the drivers that cause collisions resulting from pedal misapplication, the majority are female.…”

Section: Gender and Staturementioning

confidence: 99%

“…However, the hypothesis that older drivers are more likely to cause collisions resulting from pedal misapplication has academic support. Smith et al (2021) also identified an overrepresentation of 65-94-year-olds when they examined NMVCCS and North Carolina state crash databases for crashes resulting from pedal misapplication. They found that the highest proportion of collisions resulting from pedal misapplication for any single age group involved drivers aged 65-74 years.…”

Section: Agementioning

confidence: 99%

“…Many of the studies identified used collision data from just North Carolina. This means that multiple reports (Schmidt and Young, 2010;NHTSA, 2015;Smith et al, 2021;Collins et al, 2014) have used the same or similar data in their studies. This could mean that the evidence used in this review only represents driving behaviour in North Carolina, as there are universal differences in driving behaviour and road conditions which may impact pedal misapplication.…”

Section: Limitationsmentioning

confidence: 99%

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Pedal Misapplication Study

Ekambaram,

Frox,

Bull

et al. 2023

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Pedal misapplication is when the driver applies the wrong pedal of a vehicle, for example: accelerating instead of braking. When followed by the inability to regain control, this can result in collisions, causing damage to vehicles, infrastructure, and potentially, fatal injury. Current figures of pedal misapplication occurrences are potentially underestimated as depends heavily on driver statements, which are often unreliable. As a result, investigators are reliant on other data sources such as camera footage and examining the vehicle controls to determine the cause of the collision. This study examined the characteristics of collisions related to pedal misapplication. A systematic literature review was performed to explore the topic of pedal misapplication. Previous research found that pedal misapplication can occur throughout the entire driving cycle and can impact all drivers. However, certain risk factors were identified, including; old age, cognitive impairment, small stepover pedal configuration, incorrect foot positioning, hesitant braking, short stature, smaller shoe size and possibly being female. The review also presented countermeasures, including technologies that are available in the market, which have the potential to mitigate collisions caused by pedal misapplication. In addition, real-world collision data (RAIDS) from 2012 were interrogated to identify pedal misapplication related collisions for further analysis. The majority of the analysed collisions were caused by the driver accelerating instead of applying brakes and a high proportion occurred on smaller roads or car parks at low speed limits, with just under 50% occurring at initial start-up. Within the sample, similar contributing factors were identified to those found in the literature. Elderly drivers and female drivers were most commonly involved with impairments of driver distraction and illness reported. Vehicles fitted with automatic transmissions were more frequently associated with pedal misapplication cases. This study provides useful insights about crash characteristics and contributory factors of collisions related to pedal misapplication. As crash mitigation systems are growing in popularity amongst vehicle manufacturers, the development and fitment of a system that can detect and mitigate pedal misapplication should be encouraged. This can be achieved through rewarding points in consumer test programmes or by making fitment mandatory through relevant standards.

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Section: Crash Characteristicsmentioning

confidence: 99%

Section: Gender and Staturementioning

confidence: 99%

Section: Agementioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

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Pedal Misapplication Study

Ekambaram,

Frox,

Bull

et al. 2023

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“…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 ]. However, the last three types of data are relatively difficult to acquire during naturalistic driving as they require body posture sensors and medical physiometer devices, which are not usually found in vehicles.…”

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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Identifying Pedal Misapplication Behavior Using Event Data Recorders

Cited by 2 publications

References 6 publications

Pedal Misapplication Study

Pedal Misapplication Study

Electric Bus Pedal Misapplication Detection Based on Phase Space Reconstruction Method

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