Electrophysiology-based detection of emergency braking intention in real-world driving

Haufe, Stefan; Kim, Jeong-Woo; Kim, Il-Hwa; Sonnleitner, Andreas; Schrauf, Michael; Curio, Gabriel; Blankertz, Benjamin

doi:10.1088/1741-2560/11/5/056011

Cited by 121 publications

(93 citation statements)

References 21 publications

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“…Concluding, the current findings complement recent studies that have identified correlates of other cognitive processes in realistic driving, including drowsiness [6], [21], [22], [23], emergency braking [9], [24], error-awareness [25], anticipation of self-motivated steering [8] and braking actions [7], as well as visual attention [26]. We purport that future driving assistive systems can exploit information derived from these signals -decoded through a brain-machine interface system-, in combination with information from in-car sensors to tailor the support they provide both to the perceived conditions of the environment as well as the internal state of the driver [27].…”

Section: Discussionsupporting

confidence: 84%

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Brain Correlates of Lane Changing Reaction Time in Simulated Driving

Zhang

Chavarriaga

Gheorghe

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-Psychophysical studies have reported correlation between neural activity in frontal and parietal areas and subject's reaction time in simple tasks. Here we study whether similar correlates can also be identified in driver's electroencephalography (EEG) activity when they perform steering actions triggered by exogenous stimuli (e.g. obstacles along the road). We report analysis of the EEG signals of fifteen subjects while they drive in a realistic car simulator. We found that the peak latency of the event-related potentials in frontal and parietal areas significantly correlates with the onset of the steering behavior. Similarly, modulations of the power in the theta band (4-8 Hz) prior to the action also correlates with the reaction times. These results provide evidence of reliable neural markers of the driver's response variability.

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Section: Discussionsupporting

confidence: 84%

“…after 400 ms). A decrease in the beta power (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) is also clear in these two electrodes, appearing at about 300 ms, continuing during lane change behavior. In addition, a late increase at about 500 ms can be observed in the low frequency (1-4 Hz) activity in FCz.…”

Section: Power Spectral Densitymentioning

confidence: 98%

Brain Correlates of Lane Changing Reaction Time in Simulated Driving

Zhang

Chavarriaga

Gheorghe

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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“…Currently, driving assistant systems are mainly based on monitoring the vehicle conditions, e.g., the parameters of the car (steering, braking and accelerating), vehicle's location, complexity of the environment, and distance from other automobiles. Furthermore, it has been proposed that these systems can also monitor the driver's condition through the recording of physiological signals such as electroencephalography (EEG), electrocardiography (ECG) or electrooculography (EOG) (Chuang et al, 2010;Haufe et al, 2011;Haufe et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…These BCI systems would decode driver's brain activity to estimate his/her cognitive states or action intentions. For instance, the system can verify whether the driver is paying attention to the driving behavior (Simon et al, 2011), estimate mental workload (Dijksterhuis et al, 2013), or predict driver's intention of action (e.g., braking, traffic lights, and lane changes) (Haufe et al, 2011;Gheorghe et al, 2013;Haufe et al, 2014;Sonnleitner et al, 2014;Kim et al, 2015;Khaliliardali et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

EEG-based decoding of error-related brain activity in a real-world driving task

et al. 2015

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Abstract.Objectives. Recent studies have started to explore the implementation of braincomputer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be used, e.g., to predict driver's intended turning direction before reaching road intersections. Approach. We executed experiments in a car simulator (N = 22) and a real car (N = 8). While subject was driving, a directional cue was shown before reaching an intersection, and we classified the presence or not of an error-related potentials from EEG to infer whether the cued direction coincided with the subject's intention. In this protocol, the directional cue can correspond to an estimation of the driving direction provided by a driving assistance system. We analyze ERPs elicited during normal driving and evaluated the classification performance in both offline and online tests.Results. An average classification accuracy of 0.698 ± 0.065 was obtained in offline experiments in the car simulator, while tests in the real car yielded a performance of 0.682 ± 0.059. The results were significantly higher than chance level for all cases. Online experiments led to equivalent performances in both simulated and real car driving experiments. These results support the feasibility of decoding these signals to help estimating whether the driver's intention coincides with the advice provided by the driving assistant in a real car. Significance. The study demonstrates a BCI system in real-world driving, extending the work from previous simulated studies. As far as we know, this is the first online study in real car decoding driver's error-related brain activity. Given the encouraging results, the paradigm could be further improved by use of more sophisticated machine learning approaches and possibly be combined with applications in intelligent vehicles.

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“…The modern research on brake-assisting typically considers the brain and the myoelectric activities measured by electroencephalography (EEG) and electromyography (EMG), respectively [7][8][9]. These approaches demonstrate the high feasibility of predicting the emergency situation on the road.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles

Podusenko¹,

Nikulin²,

Tanev³

et al. 2017

Algorithms

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Abstract:We investigate the feasibility of classifying (inferring) the emergency braking situations in road vehicles from the motion pattern of the accelerator pedal. We trained and compared several classifiers and employed genetic algorithms to tune their associated hyperparameters. Using offline time series data of the dynamics of the accelerator pedal as the test set, the experimental results suggest that the evolved classifiers detect the emergency braking situation with at least 93% accuracy. The best performing classifier could be integrated into the agent that perceives the dynamics of the accelerator pedal in real time and-if emergency braking is detected-acts by applying full brakes well before the driver would have been able to apply them.

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Electrophysiology-based detection of emergency braking intention in real-world driving

Abstract: Our study serves as a real-world verification of the feasibility of electrophysiology-based detection of emergency braking intention as proposed in Haufe et al (2011 J. Neural Eng. 8 056001).

Cited by 121 publications

References 21 publications

Brain Correlates of Lane Changing Reaction Time in Simulated Driving

Brain Correlates of Lane Changing Reaction Time in Simulated Driving

EEG-based decoding of error-related brain activity in a real-world driving task

Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles

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