Effects of a front-seat passenger on driver attention: An electrophysiological approach

Chan, Michelle; Nyazika, Simbarashe; Singhal, Anthony

doi:10.1016/j.trf.2016.09.016

Cited by 15 publications

(13 citation statements)

References 41 publications

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“…A summary of the study characteristics is listed in Table 1 . The risk factors involved in the included articles can be summarized in five categories, including distracted driving ( N = 5) [ 12 , 27 , 32 , 33 , 34 ], reduced attention ( N = 5) [ 19 , 35 , 36 , 37 , 38 ], increased difficulty in driving ( N = 2) [ 27 , 28 ], alcohol ( N = 2) [ 32 , 39 ], and negative emotion ( N = 2) [ 40 , 41 ]. Distracted driving is a major cause of traffic accidents, which is related to driving behaviors such as using a cell phone and talking to passengers while driving.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, reaction time is a primary measure of the subjects’ performances in the task. In addition to the oddball paradigm, a driving task provides a variety of performance measures such as mean speed, speed variability, lane deviation, and driving errors [ 27 , 28 ]. Because reaction time and driving behaviors are essential to driving safety, the current review conducted separate statistical analyses on the reaction time of the oddball paradigm and performance of the driving task.…”

Section: Methodsmentioning

confidence: 99%

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P300 Measures and Drive-Related Risks: A Systematic Review and Meta-Analysis

Fang

Zhang

et al. 2020

IJERPH

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Detecting signs for an increased level of risk during driving are critical for the effective prevention of road traffic accidents. The current study searched for literature through major databases such as PubMed, EBSCO, IEEE, and ScienceDirect. A total of 14 articles that measured P300 components in relation to driving tasks were included for a systematic review and meta-analysis. The risk factors investigated in the reviewed articles were summarized in five categories, including reduced attention, distraction, alcohol, challenging situations on the road, and negative emotion. A meta-analysis was conducted at both behavioral and neural levels. Behavioral performance was measured by the reaction time and driving performance, while the neural response was measured by P300 amplitude and latency. A significant increase in reaction time was identified when drivers were exposed to the risk factors. In addition, the significant effects of a reduced P300 amplitude and prolonged P300 latency indicated a reduced capacity for cognitive information processing. There was a tendency of driving performance decrement in relation to the risk factors, however, the effect was non-significant due to considerable variations and heterogeneity across the included studies. The results led to the conclusion that the P300 amplitude and latency are reliable indicators and predictors of the increased risk in driving. Future applications of the P300-based brain–computer interface (BCI) system may make considerable contributions toward preventing road traffic accidents.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

P300 Measures and Drive-Related Risks: A Systematic Review and Meta-Analysis

Fang

Zhang

et al. 2020

IJERPH

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show abstract

“…Now we introduce how to estimate the importance of each input variable to the variation of the system output. Initially, set values , and calculate (11) For , set and then calculate (12) where . Next calculate (13) and 14The ERR value for each team , as a criterion to select the model structure, is defined as…”

Section: Identification Of Important Variables Using Errcmentioning

confidence: 99%

“…Measuring driver workload is of great significance for improving the understanding of driver behaviours, and worthwhile investigating for the purposes of enhancing drivervehicle interactions [12], [13]. The workload indicates the proportion of an operator's limited capacity that is needed to conduct a specific task [14].…”

Section: Introductionmentioning

confidence: 99%

Driver workload estimation using a novel hybrid method of error reduction ratio causality and support vector machine

et al. 2018

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Measuring driver workload is of great significance for improving the understanding of driver behaviours and supporting the improvement of advanced driver assistance systems technologies. In this paper, a novel hybrid method for measuring driver workload estimation for real-world driving data is proposed. Error reduction ratio causality, a new nonlinear causality detection approach, is being proposed in order to assess the correlation of each measured variable to the variation of workload. A full model describing the relationship between the workload and the selected important measurements is then trained via a support vector regression model. Real driving data of 10 participants, comprising 15 measured physiological and vehicle-state variables are used for the purpose of validation. Test results show that the developed error reduction ratio causality method can effectively identify the important variables that relate to the variation of driver workload, and the support vector regression based model can successfully and robustly estimate workload.

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“…Another way to study circumstances that strongly affect driving performance is by carrying out driving tasks in a driving simulator. The concept was applied by Chan et al [10], who said that more focus is needed in case of the presence of a company in the vehicle. Their investigations were based on results provided by examinations performed in a driving simulator.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Approach for In-Car Sound Quality Prediction

Lukács¹

2020

ACTA POLYTECH HUNG

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Numerous methods exist to characterize product quality. Nowadays, in the case of road vehicles, one of the most important issues is the acoustic comfort of the interior. However, the detection of the traffic environment is a further key question. In the case of minor vehicle collisions, the perceptibility is to analyze. Within the framework of the current study, the results of airborne noise measurements are presented. Experimental data were used to design predictive fuzzy models to estimate cabin noise level, which is in connection with the audibility of outer sourcing sounds. Two concepts of inference systems were investigated by examining accuracy, conformity and 0 residuals: Mamdani and Sugeno type ones. It was finally concluded that for estimating interior noise, Sugeno type fuzzy model is the better choice, as the accuracy and conformity are higher. In addition, the range of residuals is a magnitude lower: Mamdani type FIS provided-2.30 ~ 2.30 dB (-3.84 ~ 3.30%), Sugeno type one resulted-0.40 ~ 0.20 dB (-0.57 ~ 0.33%). Furthermore, the residuals follow a Gaussian distribution, in the case of the Sugeno predictive fuzzy model.

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Effects of a front-seat passenger on driver attention: An electrophysiological approach

Cited by 15 publications

References 41 publications

P300 Measures and Drive-Related Risks: A Systematic Review and Meta-Analysis

P300 Measures and Drive-Related Risks: A Systematic Review and Meta-Analysis

Driver workload estimation using a novel hybrid method of error reduction ratio causality and support vector machine

A Fuzzy Approach for In-Car Sound Quality Prediction

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