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PREDICTING THE MENTAL STRESS LEVEL OF DRIVERS IN A BRAKING CAR PROCESS USING ARTIFICIAL INTELLIGENCE

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Sugiono S., Prasetya R. P., Fanani A. A., Cahyawati A. N.

Acta Neuropsychologica

2022

vol. 20(1)

1-15

Reducing the physical and mental weariness of drivers is significant in improving healthy and safe driving. This paper is aim to predict the stress level of drivers while braking in various conditions of the track. By discovering the drivers’ mental stress level, we are able to safely and comfortably adjust the distance in relation to the vehicle ahead. The initial step used was a study related to Artificial Intelligence (AI), Electroencephalogram (EEG), safe distance in braking, and the theory of mental stress. The data was collected by doing a direct measurement of drivers’stress levels using the EEG tool. The respondents were 5 parties around 30-50 years old who had experience in driving for> 5 years. The research asembled 400 pieces of data about braking including the data of the velocity before braking, track varieties (cityroad, rural road, residential road, and toll road), braking distance, stress level (EEG), and focus (EEG). The database constructed was used to input the machine learning (AI) – Back Propagation Neural Network (BPNN) in order to predict the drivers’ mental stress level. Referring to the data collection, each road type gave a different value of metal stress and focus. City road drivers used an average velocity of 23.24 Km/h with an average braking distance of 11.17 m which generated an average stress level of 53.44 and a focus value of 45.76.Under other conditions, city road drivers generated a 52.11 stress level, the rural road = 48.65, and 50.23 for the toll road. BPNN Training with 1 hidden layer, neuron = 17, ground transfer function, sigmoid linear, and optimation using Genetic Algorithm (GA) obtained the Mean Square Error (MSE) value = 0.00537. The road infrastructure, driving behavior, and emerging hazards in driving took part in increasing the stress level and concentration needs of the drivers. The conclusion may be drawn that the available data and the chosen BPNN structure were appropriate to be used in training and be utilized to predict drivers’ focus and mental stress level. This AI module is beneficial in inputting the data to the braking car safety system by considering those mental factors completing the existing technical factor considerations.

Investigating the influence of distance between cars on the driver psychophysiology during braking using EEG: a case study on driving in Indonesia

100%

Sugiono S., Widhayanuriyawan D., Andriani D. P., Prasetya R. P.

vol. 17(3)

329-339

The multiple crashes in Indonesia are categorized into a frequently occurring accident, which often causes death. The aim of this paper is to examine the driver psychophysiology during braking in response to the vehicle in the front, which is varied. The research was initiated with a literature review regarding the electrooculography (EEG), safe braking distance, Emotive Epoc+, and Central Nervous System (CNS). The research was initiated with a literature review regarding the Electroencephalography (EEG), safe braking distance, Emotive Epoc+, and Central Nervous System (CNS). Research design with direct driving experiments on the road is used to analyze what happens to the driver's brain when braking at a certain distance (psychophysiology factor). The collected sampling data are from 4 male healthy drivers with the age between 20 - 40 years and average driving experience of more than 5 years. The measurement of brain activities into a spectrum of colors and Emotive BCI 16 electrodes through the performance matrices was conducted for the existing condition and condition suitable with the safety distance permitted. Experiments have been tested in 4 different road conditions of residential road (speed <30Km/h), city road (speed <50Km/h), rural road (speed <80Km/h) and motorway (speed <100Km/h). Safety distance measurement used standard data with residences road = 10m, city road = 29m, rural road 73m, and motorways = 115m. Results of brainwave signal have been recorded by Emotive Epoc Brain Activity map and Emotive BCI matrix and have been used to analyse the driver’s psychophysical. The findings show that the level of stress in the existing condition is very wherein for the braking in the densely populated residence = 87, urban areas = 83, intercity = 76, and motorways = 60. In contrast, following the safety distance rules have successfully reduced mental stress to average 47 as proofed by lower beta signal especially on occipital lobe (vision function) and on frontal lobe (attention function). Improper infrastructure such as narrow road at heavy residential damaged driver relaxes and increased stress level as indicated by increasing brain signal significantly. Meanwhile, driving while concerning the safety braking distance psychophysiologically through the identification of brain activity will be able to lower the driver’s stress and fatigue level.

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2 Acta Neuropsychologica

1 2022

1 2019

2 Prasetya R. P.

2 Sugiono S.

1 Andriani D. P.

1 Cahyawati A. N.

1 Fanani A. A.

1 Widhayanuriyawan D.

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PREDICTING THE MENTAL STRESS LEVEL OF DRIVERS IN A BRAKING CAR PROCESS USING ARTIFICIAL INTELLIGENCE

Investigating the influence of distance between cars on the driver psychophysiology during braking using EEG: a case study on driving in Indonesia