Influence of drivers’ visual and cognitive attention on their perception of changes in the traffic environment

Ojsteršek, Tina Cvahte; Topolšek, Darja

doi:10.1186/s12544-019-0384-2

Cited by 28 publications

(12 citation statements)

References 35 publications

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“…In this paper, we use the theory of RSM and the maximum stimulus hypothesis to describe driver selective attention in the dilemma zone as follows: When a driver approaches the signalized intersection at the onset of the yellow signal indication, the objects in the driving environment will stimulate the driver sensory organ [ 42 ]. When the stimulus exceeds a certain threshold value, the driver will keep his/her mind on the objects in the driving environment [ 43 , 44 ].…”

Section: Model Driver Behavior In the Dilemma Zonementioning

confidence: 99%

Modeling driver behavior in the dilemma zone based on stochastic model predictive control

Tan

Lin

2021

PLoS ONE

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Driver behavior is considered one of the most important factors in the genesis of dilemma zones and the safety of driver-vehicle-environment systems. An accurate driver behavior model can improve the traffic signal control efficiency and decrease traffic accidents in signalized intersections. This paper uses a mathematical modeling method to study driver behavior in a dilemma zone based on stochastic model predictive control (SMPC), along with considering the dynamic characteristics of human cognition and execution, aiming to provide a feasible solution for modeling driver behavior more accurately and potentially improving the understanding of driver-vehicle-environment systems in dilemma zones. This paper explores the modeling framework of driver behavior, including the perception module, decision-making module, and operation module. The perception module is proposed to stimulate the ability to perceive uncertainty and select attention in the dilemma zone. An SMPC-based driver control modeling method is proposed to stimulate decision-making behavior in the dilemma zone. The operation module is proposed to stimulate the execution ability of the driver. Finally, CarSim, the well-known vehicle dynamics analysis software package, is used to verify the proposed models of this paper. The simulation results show that the SMPC-based driver behavior model can effectively and accurately reflect the vehicle motion and dynamics under driving in the dilemma zone.

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Section: Model Driver Behavior In the Dilemma Zonementioning

confidence: 99%

Modeling driver behavior in the dilemma zone based on stochastic model predictive control

Tan

Lin

2021

PLoS ONE

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“…The studies that examine driver perception were conducted using different methodologies, by the experiments conducted in driving simulators (Ben-Bassat & Shinar, 2011;Martens & Brouwer, 2013;Pešić et al, 2019;Rudin-Brown, 2004;Wu et al, 2017;Zheng et al 2018), real traffic situations (Troscianko et al, 1999), tablet PCs and smart boards (Cicevic et al, 2017), by driver self-reports, i.e. questionnaire (Ojsteršek & Topolšek, 2019), and also in virtual reality (Trifunović et al, 2019). The sample ranges from 14 to 200 respondents, while statistical analysis was most often used for data processing.…”

Section: Perception and The Human Factor In Traffic Safetymentioning

confidence: 99%

A Bee Colony Optimization (BCO) and Type-2 Fuzzy Approach to Measuring the Impact of Speed Perception on Motor Vehicle Crash Involvement

Čubranić-Dobrodolac

Švadlenka

Čičević

et al. 2021

Preprint

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This paper examines how a driver’s perception of various speed levels, as well as driver’s speed perception from different positions, affect the propensity for motor vehicle crashes (MVCs). Data collection is performed in twelve experiments. 178 young drivers assessed the speed level from four positions; three of them relate to the speed perception of other vehicles on the road, while the remaining one represents the assessment of own speed. At each position, three speed levels were assessed: 30, 50, and 70 km/h. To process data, seven Type-2 fuzzy inference systems (T2FISs) are designed and tested in a sense of compliance with the empirical data. As a result, a relationship between the various forms of speed perception and participation in MVCs can be quantified. To examine the initial conclusions, the optimization of each of these T2FISs is performed by implementing the bee colony optimization (BCO) metaheuristic. The BCO based algorithm proposed in this paper achieved an average improvement of 21.17% in the performance of the initial T2FIS structures. The final results indicate that the drivers whose speed perception of the vehicle they are looking at from the rear side, as well as of the own vehicle, is poor have an elevated risk toward participation in MVCs compared to other forms of speed perception. The best-found T2FIS structures can be used as a decision-making tool that quantifies the driver propensity for MVCs, which can be useful in various educational and recruitment procedures in the field of transportation and traffic safety.

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“…erefore, it is extremely crucial to study distracted driving considering mixed traffic environments [8]. e National Highway Traffic Safety Administration roughly divides distraction into visual distraction [9], operational distraction [10], and cognitive distraction [11]. Visual distraction, such as distracted visual attention during driving while focusing on things not related to the driving area, can weaken driving safety, and visual distraction changes as the amount of time that the gaze point leaves the road ahead increases.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Distraction State Recognition of Drivers at a Nonsignalized Intersection in a Mixed Traffic Environment

Hua

Jin

Jiang

et al. 2021

Advances in Civil Engineering

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Distracted driving has become a growing traffic safety concern. With advances in autonomous driving and connected vehicle technology, a mixture of various types of intelligent vehicles will become normal in the near future, while more factors that may cause driver cognitive distraction are emerging. However, there are rarely studies on distracted driving in mixed traffic environments. To fill this gap, we conducted a natural driving experiment with three representative events at a nonsignalized intersection in a mixed traffic environment and proposed a novel method of identifying cognitive distraction based on bidirectional long short-term memory (Bi-LSTM) with attention mechanism. Forty participants were recruited for each event, who completed three different cognitive distraction experiments induced by three different secondary tasks in contrast with a normal driving process when passing a nonsignalized intersection. Related driving performance and eye movement data were collected to train and test the Bi-LSTM with attention mechanism model. Compared with the support vector machine (SVM) model, its recognition accuracy rate is 94.33%, which is 3.83% higher than that of the SVM in the total event, which has reasonable applicability for distraction recognition in a mixed traffic environment. Potential applications of this model include distraction alarm and autonomous driving assistance systems, which could avoid road traffic accidents.

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Influence of drivers’ visual and cognitive attention on their perception of changes in the traffic environment

Cited by 28 publications

References 35 publications

Modeling driver behavior in the dilemma zone based on stochastic model predictive control

Modeling driver behavior in the dilemma zone based on stochastic model predictive control

A Bee Colony Optimization (BCO) and Type-2 Fuzzy Approach to Measuring the Impact of Speed Perception on Motor Vehicle Crash Involvement

Cognitive Distraction State Recognition of Drivers at a Nonsignalized Intersection in a Mixed Traffic Environment

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