Assessing the effect of inattention-related error and anger in driving on road accidents among Iranian heavy vehicle drivers

Shams, Zhaleh; Naderi, Hossein; Nassiri, Habibollah

doi:10.1016/j.iatssr.2020.09.005

Cited by 13 publications

(9 citation statements)

References 51 publications

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“…The public dataset consists of 165 and 530 images, respectively for the two classes of neutral and non-neutral facial expressions, divided into training, validation, and testing subsets. After the first pre-training phase, the neural network model reaches a validation accuracy of 91%, similarly to the performance of state-of-the-art methods for monitoring a driver's emotions [37]. The private dataset consists of 404 frames, equally divided into the focused and inattentive classes, and the CNN has then been empirically tested on all the remaining ones, for a total of 61863.…”

Section: Resultsmentioning

confidence: 95%

“…First, there is a correlation between event occurrences and driver's inattention. Inattention progressively makes the user unable to realize what is happening in the environment and to predict, and thus to react to, other drivers' behavior [37], resulting in a higher probability of accidents. Nonetheless, there could be a second interpretation observed for this correlation; a user involved in a driving experience using the simulator is focused on his task as long as he is in control of the situation.…”

Section: Discussionmentioning

confidence: 99%

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Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

et al. 2021

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Driver inattention is the primary cause of vehicle accidents; hence, manufacturers have introduced systems to support the driver and improve safety; nonetheless, advanced driver assistance systems (ADAS) must be properly designed not to become a potential source of distraction for the driver due to the provided feedback. In the present study, an experiment involving auditory and haptic ADAS has been conducted involving 11 participants, whose attention has been monitored during their driving experience. An RGB-D camera has been used to acquire the drivers’ face data. Subsequently, these images have been analyzed using a deep learning-based approach, i.e., a convolutional neural network (CNN) specifically trained to perform facial expression recognition (FER). Analyses to assess possible relationships between these results and both ADAS activations and event occurrences, i.e., accidents, have been carried out. A correlation between attention and accidents emerged, whilst facial expressions and ADAS activations resulted to be not correlated, thus no evidence that the designed ADAS are a possible source of distraction has been found. In addition to the experimental results, the proposed approach has proved to be an effective tool to monitor the driver through the usage of non-invasive techniques.

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Section: Resultsmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

et al. 2021

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“…According to the results of some studies that have examined the effect of exposure, the more the drivers are exposed, the higher the number of their collisions [11,41]. Other studies show that there is no significant relationship between exposure and the number of collisions [8,12,42].…”

Section: Discussionmentioning

confidence: 99%

“…According to Radun et al and Shams et al studies [15,42], drivers' dissatisfaction with their sleep and its poor quality have led to an increase in the number of accidents. Unlike the results of these studies, no significant direct relationship between sleep quality and health with collision was discovered in this research.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors

Naderi

Nassiri

Zahedieh

2021

Journal of Advanced Transportation

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Traffic collisions are one of the most important challenges threatening the general health of the world. Iran’s crash statistics demonstrate that approximately 16,500 people lose their lives every year due to road collisions. According to the traffic police of Iran, heavy vehicles (including trailers, trucks, and panel trucks) contributed to 20.5% of the fatal road traffic collisions in the year 2013. This highlights the need for devoting special attention to heavy vehicle drivers to further explore their driving characteristics. In this research, the effect of heavy vehicle drivers’ behavior on at-fault collisions over three years has been investigated with an innovative approach of structural equation modeling (SEM) and Bayesian Network (BN). The database utilized in this research was collected using a questionnaire. For this purpose, 474 heavy vehicle drivers have been questioned in the Parviz Khan Border Market, located on the border of Iran and Iraq. The response rate of the survey was 80%. The participants answered the questions on Driver Behavior Questionnaire (DBQ) and a sleep assessing questionnaire named Global Dissatisfaction with Sleep (GSD). In this research, human factors affecting at-fault collisions of heavy vehicles were identified and their relationships with other variables were determined using the SEM approach. Then the descriptive model constructed by the SEM method was used as the basis of the BN, and the conditional probabilities of each node in the BN were calculated by the database collected by the field survey. SEM indicates that other attributes including GSD, mobile usage, daily fatigue, exposure, and education level have an indirect relation with heavy vehicle drivers’ at-fault collisions. According to the BN, if there is no information about the characteristics of a heavy vehicle driver, the driver will likely have at least one collision during the next three years with the probability of 0.17. Also, it was indicated that the minimum probability of the at-fault collision occurrence for a heavy vehicle is 0.08.

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“…However, this caused the potential for serious accidents to occur in road safety (Wen et al, 2022). Over-dimension and overloading truck accidents occurred due to disruption of the visibility of other drivers (Shams et al, 2021). The fatality rate of death was more dominant caused by over-dimension and overloading of trucks (Chang & Chien, 2013).…”

Section: Introductionmentioning

confidence: 99%

Over-Dimension and Overloading Truck Accident Model with Structural Equation Modeling Partial Least Square

Amir

Nugroho

Rijanto

et al. 2022

bpi

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Road accidents are an unavoidable problem, especially accidents caused by Over-dimension and Overloading trucks. This study involved environmental factors, driver factors, vehicle factors, and road factors which had previously been analyzed using the multinomial logistic regression method with questionnaire data of 194 respondents and resulted in 6 indicators including road surface conditions, traffic flow volume, accident history, vehicle load, vehicle dimension, and speed limit signs. The purpose of this study was to find out relationship between latent variables using the Structural Equation Modeling Partial Least Square (SEM PLS) method to obtain a model of the factors causing Over-dimension and Overloading truck accidents. The model results obtained indicate that weather conditions, road surface conditions, lighting condition, certified training, driver's physical condition, vehicle type, vehicle dimension, vehicle load, road geometry, accident location, traffic facilities, as well as vehicle dimension, and speed limit signs were the variables that had the greatest influence.The model obtained was good and fit (fit model) based on the R2 and Chi-Square values of 65.7% and 76.3%, respectively.Keywords: Over-dimension and Overloading Trucks; Road Accidents; SEM PLS; Factors Causing Accidents

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Assessing the effect of inattention-related error and anger in driving on road accidents among Iranian heavy vehicle drivers

Cited by 13 publications

References 51 publications

Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

Can ADAS Distract Driver’s Attention? An RGB-D Camera and Deep Learning-Based Analysis

Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors

Over-Dimension and Overloading Truck Accident Model with Structural Equation Modeling Partial Least Square

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