Brain Correlates of Lane Changing Reaction Time in Simulated Driving

Zhang, Huaijian; Chavarriaga, Ricardo; Gheorghe, Lucian; Millán, José del R.

doi:10.1109/smc.2015.548

Cited by 10 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To this end, we performed an EEG study on the neural basis of stimulus driven behavior during car driving. In particular, we addressed the neural markers of the response variability in an obstacle avoidance driving task [44]. We explored how EEG activity, elicited by the appearance of obstacles which required lane changes, relate to the steering reaction.…”

Section: Driver's Response Variabilitymentioning

confidence: 99%

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

Chavarriaga

Uscumlic

Zhang

et al. 2018

IEEE Trans. Emerg. Top. Comput. Intell.

Self Cite

View full text Add to dashboard Cite

Modern cars can support their drivers by assessing and performing autonomously different driving maneuvers, based on information gathered by in-car sensors. We propose that brain machine interfaces (BMIs) can provide complementary information that can ease the interaction with intelligent cars in order to enhance the driving experience. In our approach, the human remains in control, while a BMI is used to monitor the driver's cognitive state and use that information to modulate the assistance provided by the intelligent car. In this review, we gather our proof-of-concept studies demonstrating the feasibility of decoding electroencephalography (EEG) correlates of upcoming actions and those reflecting whether the decisions of driving assistant systems are in-line with the driver intentions. Experimental results while driving both simulated and real cars consistently showed neural signatures of anticipation, movement preparation and error processing. Remarkably, despite the increased noise inherent to real scenarios, these signals can be decoded on a single-trial basis, reflecting some of the cognitive processes that take place while driving. However, moderate decoding performance compared to the controlled experimental BMI paradigms indicate there exists room for improvement of the machine learning methods typically used in the state-ofthe-art BMIs. We foresee that fusion of neural correlates with information extracted from other physiological measures; e.g. eye movements or electromyography (EMG) as well as contextual information gathered by in-car sensors will allow intelligent cars to provide timely and tailored assistance only if it is required; thus keeping the user in the loop and allowing him to fully enjoy the driving experience.

show abstract

Section: Driver's Response Variabilitymentioning

confidence: 99%

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

Chavarriaga

Uscumlic

Zhang

et al. 2018

IEEE Trans. Emerg. Top. Comput. Intell.

Self Cite

View full text Add to dashboard Cite

show abstract

“…And this Wireless Communications and Mobile Computing paper verified that the drivers' sensitivity and judgment ability would weaken on a long road under fatigue state, and it would take longer time to make driving decisions. Visual distraction can further cause drivers to lose their attention on the road and affect their reaction time [25,26]. Ru et al [27] confirm that the subjective conditions that interfere with the drivers' response include driving experience, mental and physical conditions, and adaptability.…”

Section: Drivers' Reaction Timementioning

confidence: 99%

DeepCF: A Deep Feature Learning-Based Car-Following Model Using Online Ride-Hailing Trajectory Data

Xie

Alfarraj

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The car-following model describes the microscopic behavior of the vehicle. However, the existing car-following models set the drivers’ reaction time to a fixed value without considering its dynamics. In order to improve the accuracy of car-following model, this paper proposes Deep Feature Learning-based Car-Following Model (DeepCF), a car-following model based on fatigue driving and Generative Adversarial Networks (GAN). The model is composed of the drivers’ reaction time model and the car-following decision algorithm. First, we regard driving fatigue as the starting point to study the influence of driving time and the acceleration of the preceding vehicle on the drivers’ reaction time, and develop a coarse-grained drivers’ reaction time model. Secondly, considering the impact of fatigue driving on car-following decisions, we utilize GAN to generate a driving decision database based on reaction time and use Euclidean distance as a decision search indicator. Finally, we conduct experiments on a real data set, and the results indicate that our DeepCF model is superior to baseline models.

show abstract

“…Kohlmorgen et al (2007) and Dijksterhuis et al (2013) study EEG correlates of mental workload during real-world and simulated driving, while (Kecklund and Åkerstedt, 1993; Papadelis et al, 2007; Schmidt et al, 2007, 2009; Gugler et al, 2010; Simon et al, 2011; Sonnleitner et al, 2012) study fatigue and attention during monotonous real-world driving. Reaction time in lane changing tasks has been investigated with EEG (Zhang et al, 2015a). Further studies demonstrate the detection of error and anticipatory potentials that could potentially be harnessed to increase driving safety (Khaliliardali et al, 2015; Zhang et al, 2015b).…”

Section: Detection Of Emergency Braking Intention During Drivingmentioning

confidence: 99%

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

et al. 2016

View full text Add to dashboard Cite

The combined effect of fundamental results about neurocognitive processes and advancements in decoding mental states from ongoing brain signals has brought forth a whole range of potential neurotechnological applications. In this article, we review our developments in this area and put them into perspective. These examples cover a wide range of maturity levels with respect to their applicability. While we assume we are still a long way away from integrating Brain-Computer Interface (BCI) technology in general interaction with computers, or from implementing neurotechnological measures in safety-critical workplaces, results have already now been obtained involving a BCI as research tool. In this article, we discuss the reasons why, in some of the prospective application domains, considerable effort is still required to make the systems ready to deal with the full complexity of the real world.

show abstract

Brain Correlates of Lane Changing Reaction Time in Simulated Driving

Cited by 10 publications

References 26 publications

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

Decoding Neural Correlates of Cognitive States to Enhance Driving Experience

DeepCF: A Deep Feature Learning-Based Car-Following Model Using Online Ride-Hailing Trajectory Data

The Berlin Brain-Computer Interface: Progress Beyond Communication and Control

Contact Info

Product

Resources

About