Detection of emergency braking intention using driver's electroencephalographic signals

Hernandez-Rojas, Luis Guillermo; Martínez, Efraín; Antelis, Javier M.

doi:10.1109/tla.2019.8826702

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…In addition to the external driver state information, the vehicle needs to understand the driver's intention to generate appropriate assistance and collaborative control strategies. Current studies focus on specific tasks or scenarios such as lane change intention [136][137][138], braking intention [139][140][141], and acceleration intention [142], and they can be summarized as shown in Fig. 6.…”

Section: Driver Intention Inference and Predictionmentioning

confidence: 99%

“…[139] presented a novel braking intention identification model based on the LSTM network to recognize three levels of braking: slight, normal, and hard, in which braking-related data obtained from the speed sensor, gyroscope, and pedal force sensor, were utilized to achieve a predictive accuracy greater than 95%. Further, the driver's cognitive signal was investigated to detect the intention to perform emergency braking [140,141], with an experimental accuracy greater than 90%. [157] investigated the mechanism of intentional behavior and proposed a psychological perception-action (P-A) model that enables the intentions of drivers to be characterized at each level of the P-A hierarchy in terms of a variety of driver signals.…”

Section: Driver Intention Inference and Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Lou

Xing

et al. 2022

IEEE Trans. Intell. Veh.

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Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system.

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Section: Driver Intention Inference and Predictionmentioning

confidence: 99%

Section: Driver Intention Inference and Predictionmentioning

confidence: 99%

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Lou

Xing

et al. 2022

IEEE Trans. Intell. Veh.

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“…Conditions such as temperature, humidity, oxygen content in the air, noise, and vibration of car seats are much worse than indoor environments. Unfavorable conditions driving fatigue usually occurs in driving environments [12,13].…”

Section: Definition Of Fatigue Drivingmentioning

confidence: 99%

Heterogeneous Signal Fusion Method in Driving Fatigue Detection Signals

Wang

Zhang

2021

Journal of Advanced Transportation

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Driving fatigue is a physiological phenomenon that often occurs during driving. When the driver enters a fatigue state, they will become distracted and unresponsive, which can easily lead to traffic accidents. The driving fatigue detection method based on a single information source has poor stability in a specific driving environment and has great limitations. This work helps with being able to judge the fatigue state of the driver more comprehensively and achieving a higher accuracy rate of driving fatigue detection. This work mainly introduces research into different signal fusion methods to detect fatigue drive. This work will take the normal driver’s breathing signal, eye signals, and steering wheel signal as research objects and collect and isolate the characteristics of the fatigue detection signal. Research was then conducted on different signal fusion methods for the detected depth of breath. Change of steering angle, eyelid closure, and blinking marks and the fatigue driving experiment was designed to evaluate the results of different data fusion methods. Experimental results show that the detection accuracy of the heterogeneous signal fusion method in fatigue detection is as high as 80%.

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“…Past studies associated with measuring the impact of distracted driving on safety can be categorized into three types, including data analysis (Peng and Boyle, 2012; Qin et al , 2019; Jamil et al , 2021), driving simulator studies (Nowosielski et al , 2018; Xu and Lin, 2018; Arkonac et al , 2019; Zhang et al , 2019; Baldo et al , 2020) and field studies (Dingus et al , 2016; Ma et al , 2018; Arvin et al , 2019; Hernández-Rojas et al , 2019; Arvin and Khattak, 2020; Gao et al , 2021). Due to driving simulation providing a low-cost method for driver behavior data collection, most distracted driving studies were conducted in a simulator environment.…”

Section: Introductionmentioning

confidence: 99%

Revealing driver psychophysiological response to emergency braking in distracted driving based on field experiments

Zhao

Gao

et al. 2022

JICV

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Purpose The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driver’s physiological states. Design/methodology/approach Field tests with 17 participants were conducted in the connected and automated vehicle test field. All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed. Demographic data, vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit, such as electrocardiograph for heart rate, electromyography for muscle strength, electrodermal activity for skin conductance and force-sensing resistor for braking pressure. Findings This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs. The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driver’s take-over maneuver is needed. Originality/value The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time. Therefore, the driver states in distracted driving are significantly different than in regular driving, the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics. To the best of the authors’ knowledge, this is the first study using psychophysiological measures to quantify a driver’s response to an emergency stop during distracted driving.

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Detection of emergency braking intention using driver's electroencephalographic signals

Cited by 8 publications

References 29 publications

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Heterogeneous Signal Fusion Method in Driving Fatigue Detection Signals

Revealing driver psychophysiological response to emergency braking in distracted driving based on field experiments

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