A Jensen-Shannon Divergence Driven Metric of Visual Scanning Efficiency Indicates Performance of Virtual Driving

Lv, Zezhong; Xu, Qing; Schoeffmann, Klaus; Parkinson, Simon

doi:10.1109/icme51207.2021.9428109

Cited by 4 publications

(9 citation statements)

References 15 publications

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“…The recent works support that fixation distribution composed of gaze points constrained time and special scope provides an indicator of observers’ state, the same as an internal or human state [9]. For another metric, optical flow distribution, is an external or environmental factor that reflects observation variation due to the motor action [17, 29]. Nevertheless, a problem worth explaining is that the internal and external information produced is not isolated or absolute.…”

Section: Resultsmentioning

confidence: 99%

“…It is now well understood that information flow reflects cognitive processes associated with various information, expanding such knowledge to assess behavioral performance in virtual reality settings requires an integrated model of conceptualizing as shown in Fig 4, which is based on the square root of Jensen-Shannon divergence (SRJSD) between two distributions to extract efficiency [29]. The two probability distribution is normalized by studies of [57, 58].…”

Section: Methodsmentioning

confidence: 99%

“…In addition, the optical flow resulted from a dynamic environment always happen in driving situations [41], representing a motion variation [42] and the optical flow distribution embedded fixation distribution represents the observation variation on retina (environmental state) [17]. The fixations are calculated using identification by dispersion threshold algorithm(I-DT) [43], and the algorithm of identification of optical flow (see Fig 2 for schematic depiction of fixations and optical flow) and the detail of probability distribution function established are used in [29].…”

Section: Fixation and Optical Flowmentioning

confidence: 99%

“…Instead of segregating it into high coupling components as shown in partial information decomposition (PID) [28]. In fact, the square root of Jensen-Shannon divergence (SRJSD) is a true system-level metric to measure the dissimilarity between two probability distributions [29], which is fully used and explained in our study to compute information flow gain.…”

Section: Introductionmentioning

confidence: 99%

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Eye movement based information system indicates human behavior in virtual driving

Peng

Zhang

et al. 2022

Preprint

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Humans modulate the behavior flexibly after timely receiving and processing information from the environment. To better understand and measure human behavior in the driving process, we integrate humans and the environment as a system. The eye-movement methodologies are used to provide a bridge between humans and environment. Thus, we conduct a goal-directed task in virtual driving to investigate the law of eye-movement that could characterize the humans (internal) and environmental (external) state measured by fixation distribution and optical flows distribution. The analysis of eye-movement data combined with the information-theoretic tool, transfer entropy, active information storage, quantify the humans’ cognitive effort and receiving information, and in fact, there is a balance (optimal) range between two, because of the mutual synergy and inhibition, whose quantified value is named balance of information processing. Subsequently, we update a system-level model, finding that those information measurements, transfer entropy, active information storage, and balance of information processing, all are included. This information set is information flow, which is quantified by the square root of Jensen-Shannon divergence (SRJSD), named information flow gain. What’s more, results also demonstrate that the influence of system-level information flow correlated with behavioral performance stronger than the separate measurements. In conclusion, we research humans’ eye-movement based on information theory to analyze behavioral performance. Besides driving, these measurements may be a predictor for other behaviors such as walking, running, etc. Still, the limitation is that the information flow may be a proxy of determinants of behavior.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Fixation and Optical Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Eye movement based information system indicates human behavior in virtual driving

Peng

Zhang

et al. 2022

Preprint

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“…In addition, more physiological responses may be included in the system with off-the-shelf sensors such as electrodermal activity and electromyography. Furthermore, other measurements of eye tracking, such as the Jensen-Shannon divergence can be applied to calculate the visual scanning efficiency, which has been reported to be a better indicator than the SGE/GTE used in this study [97]. Future IVE can be improved to increase immersion and tackle more complex research problems.…”

Section: Limitations and Future Workmentioning

confidence: 95%

ORCLSim: A System Architecture for Studying Bicyclist and Pedestrian Physiological Behavior through Immersive Virtual Environments

Guo

Angulo

Robartes

et al. 2022

Journal of Advanced Transportation

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Injuries and fatalities for vulnerable road users, especially bicyclists and pedestrians, are on the rise. To better inform design for vulnerable road users, we need to evaluate how bicyclist and pedestrian behavior and physiological states change in different roadway design and contextual settings. Previous research highlights the advantages of using immersive virtual environments (IVEs) in conducting bicyclist and pedestrian studies. These environments do not put participants at risk of injury, are low cost compared to on-road or naturalistic studies, and allow researchers to fully control variables of interest. In this paper, we propose a framework, Omni-Reality and Cognition Lab Simulator (ORCLSim), to support human sensing techniques within IVEs to evaluate bicyclist and pedestrian physiological and behavioral changes in different contextual settings. To showcase this framework, we present two case studies, where pilot data from five participants’ physiological and behavioral responses in an IVE setting are collected and analyzed, representing real-world roadway segments and traffic conditions. Results from these case studies indicate that physiological data are sensitive to road environment changes and real-time events in the IVE, especially changes in heart rate and gaze behavior. In addition, our preliminary data indicate participants may respond differently to various roadway settings (e.g., signalized vs. unsignalized intersections). By analyzing these changes, future studies can identify how participants’ stress level and cognitive load are impacted by the surrounding environment. The ORCLSim system architecture is a prototype that can be customized for future studies in understanding users’ behavioral and physiological responses in virtual reality settings.

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Transfer Entropy Based Causality from Head Motion to Eye Movement for Visual Scanning in Virtual Driving

Zhang

Peng

et al. 2022

Preprint

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Visual scanning is achieved by eye movement control for visual information acquisition and cognitive processing, which plays a critical role in undertaking common sensorimotor tasks such as driving. The specific coordination of the head and eyes, with head motions temporally preceding eye movements, is an important human behavior to make a key contribution to goal-directed visual scanning and sensorimotor driving. In this paper, we put forward a proposal of philosophy that this specific coordination of the head and eyes essentially indicates a unidirectional causality from head motion to eye movement. We propose to investigate transfer entropy for defining a quantitative measure of this unidirectional head-eye causality. A normalized version of the proposed causality measure is introduced for taking a role as an assessment proxy of driving. The plain transfer entropy-based definition has shown its statistical significance as the measure of causality and, the normalized version has demonstrated its good effectiveness for the evaluation of driving performance, with the verification in virtual reality-based psychophysical studies. This paper successfully suggests that the quantitative exploitation of causality based on the specific coordination of the head and eyes offers an effective approach to behaviometrics of visual scanning and sensorimotor activity.

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A Jensen-Shannon Divergence Driven Metric of Visual Scanning Efficiency Indicates Performance of Virtual Driving

Cited by 4 publications

References 15 publications

Eye movement based information system indicates human behavior in virtual driving

Eye movement based information system indicates human behavior in virtual driving

ORCLSim: A System Architecture for Studying Bicyclist and Pedestrian Physiological Behavior through Immersive Virtual Environments

Transfer Entropy Based Causality from Head Motion to Eye Movement for Visual Scanning in Virtual Driving

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