How Will Humans Cut Through Automated Vehicle Platoons in Mixed Traffic Environments? A Simulation Study of Drivers’ Gaze Behaviors Based on the Dynamic Areas of Interest

Guo, Xiang; Cui, Lian; Park, Brian; Ding, Wen; Lockhart, Marisa; Kim, Inki

doi:10.1007/978-3-030-00114-8_55

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…In [37], the combination of frame alignment, forward/backward difference and blob detection creates a UAV video event summarization, tracking salient objects. Deep architectures are used for real-time autonomous indoor navigation [21,11] and for monitoring wildfire [43], among others. Such approaches build upon basic deep-learning models, namely AlexNet [24] and CaffeNet [18].…”

Section: Introductionmentioning

confidence: 99%

How Well Current Saliency Prediction Models Perform on UAVs Videos?

Perrin

Zhang

Meur

2019

Computer Analysis of Images and Patterns

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It is exciting to witness the fast development of Unmanned Aerial Vehicle (UAV) imaging which opens the door to many new applications. In view of developing rich and efficient services, we wondered which strategy should be adopted to predict salience in UAV videos. To that end, we introduce here a benchmark of off-the-shelf state-of-theart models for saliency prediction. This benchmark studies comprehensively two challenging aspects related to salience, namely the peculiar characteristics of UAV contents and the temporal dimension of videos. This paper enables to identify the strengths and weaknesses of current static, dynamic, supervised and unsupervised models for drone videos. Eventually, we highlight several strategies for the development of visual attention in UAV videos.

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Section: Introductionmentioning

confidence: 99%

How Well Current Saliency Prediction Models Perform on UAVs Videos?

Perrin

Zhang

Meur

2019

Computer Analysis of Images and Patterns

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show abstract

“…With the increase of realism in IVE simulators and the development of low-cost ubiquitous sensors, IVE simulators have become promising tools for conducting highly realistic and immersive experimental studies [22]. In traffic safety studies, driving simulators have been widely applied to study drivers' behaviors, awareness [23], and psychophysiological states with multimodal data collection systems such as eye trackers, electroencephalogram (EEG), and electrocardiogram (ECG) [24][25][26][27]. Some of the driver-related studies are conducted in IVE [28].…”

Section: Introductionmentioning

confidence: 99%

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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“…With the increase of realism in IVE simulators and the development of low-cost ubiquitous sensors, IVE simulators have become promising tools for conducting highly realistic and immersive experimental studies (Ergan et al 2019). In traffic safety studies, driving simulators have been widely applied to study drivers' behaviors, awareness (Soares et al 2020), and psychophysiological states with multimodal data collection systems, such as eye trackers, electroencephalogram (EEG) and electrocardiogram (ECG) (Haufe et al 2011;Akbar et al 2017;Guo et al 2019;Baee et al 2021). Some of the driver-related studies are conducted in IVE (Eudave and Valencia 2017).…”

Section: Introductionmentioning

confidence: 99%

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

Guo,

Angulo,

Robartes

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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 conduct more studies to evaluate how bicyclist and pedestrian behavior and physiological states change in different roadway designs and contextual settings. Previous research highlights the advantages of Immersive Virtual Environment (IVE) in conducting bicyclist and pedestrian studies. These environments do not put participants at risk of getting injured, 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 ORCLSim, to support human sensing techniques within IVE to evaluate bicyclist and pedestrian physiological and behavioral changes in different contextual settings. To showcase this framework, we present two case studies where we collect and analyze pilot data from five participants' physiological and behavioral responses in an IVE setting, representing real-world roadway segments and traffic conditions. Results from these case studies indicate that physiological data is sensitive to road environment changes and real-time events, especially changes in heart rate and gaze behavior. Additionally, our preliminary data indicates participants may respond differently to various roadway settings (e.g., intersections with or without traffic signal). By analyzing these changes, we can identify how participants' stress levels and cognitive load is impacted by the simulated surrounding environment. The ORCLSim system architecture can be further utilized for future studies in users' behavioral and physiological responses in different virtual reality settings.

show abstract

How Will Humans Cut Through Automated Vehicle Platoons in Mixed Traffic Environments? A Simulation Study of Drivers’ Gaze Behaviors Based on the Dynamic Areas of Interest

Cited by 5 publications

References 19 publications

How Well Current Saliency Prediction Models Perform on UAVs Videos?

How Well Current Saliency Prediction Models Perform on UAVs Videos?

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

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

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