Driver performance under simulated and actual driving conditions: Validity and orthogonality

Groeger, John A.; Murphy, Gillian

doi:10.1016/j.aap.2020.105593

Cited by 16 publications

(12 citation statements)

References 14 publications

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“…The previous research objects are mainly private car drivers and it is uncertain whether the results apply to bus drivers. Moreover, some research methods based on driving experiments (especially the driving simulator experiments) encounter challenges [26]. Using naturalistic driving data of bus drivers for research provides a feasible solution to these problems [32].…”

Section: Discussionmentioning

confidence: 99%

“…For instance, driving simulators have been widely applied [24,25] due to their low cost, safety, ease to use, and ability to switch to multiple traffic environments in a short time. However, former findings suggest that performance in driving simulators was not a strong predictor of performance in the on-road assessment [26]. Thus, drivers' reactions could be different when interacting with an ADAS through the simulator and in real-world conditions.…”

Section: Introductionmentioning

confidence: 95%

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Investigation of Bus Drivers’ Reaction to ADAS Warning System: Application of the Gaussian Mixed Model

Zhou

et al. 2021

Sustainability

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Road crashes cause serious loss of life and property. Among all vehicles, buses are more likely to encounter crashes. In recent years, the advanced driving assistance system (ADAS) has been widely used in buses to improve safety. The warning system is one of the key functions and has proven effective in reducing crashes. However, drivers often ignore or overreact to ADAS warnings during naturalistic driving scenarios. Therefore, reactions of bus drivers to warnings need further investigation. In this study, bus drivers’ responses to lane departure warning (LDW) and forward collision warning (FCW) were investigated using 20-day naturalistic driving data. These reactions could be classified into three categories, namely positive, negative, and overreaction or emergency, by employing the Gaussian mixture model. The authors constructed a framework to quantify drivers’ reactions to the warning and study the reaction characteristics in different environments. The results indicate that drivers’ reactions to FCW were more positive than to LDW, drivers reacted more positively to LDW and FCW while driving on highways than on urban roads, and drivers reacted more positively at night to LDW and FCW than during daytime. This study gives support to an adaptive ADAS considering varying bus driver characteristics and environments.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Investigation of Bus Drivers’ Reaction to ADAS Warning System: Application of the Gaussian Mixed Model

Zhou

et al. 2021

Sustainability

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“…During the study, the subjects were instructed to press the ipsilateral or contralateral foot pedal, depending on the operation mode, with their right foot as quickly as possible when the circle was lit. Meanwhile, Groeger and Murphy (2020) performed a study to determine whether the simulators validly reflect real-world driving and to what extent do different aspects of driving relate to each other.…”

Section: Research Focus and Target Groupmentioning

confidence: 99%

“…The target group (participants) who were involved in several studies related to pedal error are among the young and senior drivers. The research targeted young drivers are in aged between 18 to 27 years old (Consiglio et al, 2003;Groeger & Murphy, 2020). Meanwhile, most of the other studies recruited participants aged between 22 to 65 years old (Deng et al, 2019;Jammes et al, 2017b;McGehee et al, 2016;Mcgehee et al, 2000;Tran et al, 2012;Wu et al, 2014).…”

Section: Research Focus and Target Groupmentioning

confidence: 99%

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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“…Then, the driving simulator was calibrated by evaluating the behaviors of the driver in observing the left, right, front-viewing behavior, the speed of the intersection, the speed of following the vehicle and the observance of traffic lights and stop signs under two conditions. John A. Groeger et al [19] compared the drivers' behavior characteristics of simulator driving and the real car driving under different curvature curves based on real vehicle experiment and two simulator simulation experiments. Through the analysis of vehicle speeds, position steering behavior and other parameters on curved road sections with different radii, the correlation of these parameters between the real vehicle and the simulator is attained to complete the effectiveness calibration of the driving simulator.…”

Section: Introductionmentioning

confidence: 99%

Validation of Vehicle Driving Simulator from Perspective of Velocity and Trajectory Based Driving Behavior under Curve Conditions

Chen

Xie

et al. 2021

Energies

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With their advantages of high experimental safety, convenient setting of scenes, and easy extraction of control parameters, driving simulators play an increasingly important role in scientific research, such as in road traffic environment safety evaluation and driving behavior characteristics research. Meanwhile, the demand for the validation of driving simulators is increasing as its applications are promoted. In order to validate a driving simulator in a complex environment, curve road conditions with different radii are considered as experimental evaluation scenarios. To attain this, this paper analyzes the reliability and accuracy of the experimental vehicle speed of a driving simulator. Then, qualitative and quantitative analysis of the lateral deviation of the vehicle trajectory is carried out, applying the cosine similarity method. Furthermore, a data-driven method was adopted which takes the longitudinal displacement, lateral displacement, vehicle speed and steering wheel angle of the vehicle as inputs and the lateral offset as the output. Thus, a curve trajectory planning model, a more comprehensive and human-like operation, is established. Based on directional long short-term memory (Bi–LSTM) and a recurrent neural network (RNN), a multiple Bi–LSTM (Mul–Bi–LSTM) is proposed. The prediction performance of LSTM, MLP model and Mul–Bi–LSTM are compared in detail on the validation set and testing set. The results show that the Mul–Bi–LSTM model can generate a trajectory which is very similar to the driver’s curve driving and have a preferable generalization performance. Therefore, this method can solve problems which cannot be realized in real complex scenes in the simulator validation. Selecting the trajectory as the validation parameter can more comprehensively and intuitively reflect the simulator’s curve driving state. Using a speed model and trajectory model instead of a real car experiment can improve the efficiency of simulator validation and lay a foundation for the standardization of simulator validation.

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Driver performance under simulated and actual driving conditions: Validity and orthogonality

Abstract: Driver performance under simulated and actual driving conditions: validity and orthogonality.

Cited by 16 publications

References 14 publications

Investigation of Bus Drivers’ Reaction to ADAS Warning System: Application of the Gaussian Mixed Model

Investigation of Bus Drivers’ Reaction to ADAS Warning System: Application of the Gaussian Mixed Model

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

Validation of Vehicle Driving Simulator from Perspective of Velocity and Trajectory Based Driving Behavior under Curve Conditions

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