Visual Human–Computer Interactions for Intelligent Vehicles and Intelligent Transportation Systems: The State of the Art and Future Directions

Wang, Xumeng; Zheng, Xinhu; Chen, Wei; Wang, Fei‐Yue

doi:10.1109/tsmc.2020.3040262

Cited by 57 publications

(18 citation statements)

References 153 publications

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“…When the driver completes the secondary task, t(i) and T need to satisfy the relationship shown in Equation (13).…”

Section: Evaluation Model Of Secondary Task Carrying Capacity Under A...mentioning

confidence: 99%

“…The second approach is the adaptive human-computer interaction system based on spatial three-dimensional interaction. This system uses a cooperative intelligent transportation system to collect outside information from various sensors and uses the onboard augmented reality (AR) of the windshield display screen to present the dynamic traffic information, realized through a visual display of 360 • 3D virtual space around the vehicle [13][14][15][16][17][18]. As regards the design of adaptive human-computer interaction systems, researchers believe that drivers' cognitive state and the form in which information is presented will influence the interaction effect.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

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The Effects of Dynamic Complexity on Drivers’ Secondary Task Scanning Behavior under a Car-Following Scenario

Wang

Guo

et al. 2022

IJERPH

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The user interface of vehicle interaction systems has become increasingly complex in recent years, which makes these devices important factors that contribute to accidents. Therefore, it is necessary to study the impact of dynamic complexity on the carrying capacity of secondary tasks under different traffic scenarios. First, we selected vehicle speed and vehicle spacing as influencing factors in carrying out secondary tasks. Then, the average single scanning time, total scanning time, and scanning times were selected as evaluation criteria, based on the theories of cognitive psychology. Lastly, we used a driving simulator to conduct an experiment under a car-following scenario and collect data on scanning behavior by an eye tracker, to evaluate the performance of the secondary task. The results show that the relationship between the total scanning time, scanning times, and the vehicle speed can be expressed by an exponential model, the relationship between the above two indicators and the vehicle spacing can be expressed by a logarithmic model, and the relationship with the total number of icons can be expressed by a linear model. Combined with the above relationships and the evaluation criteria for driving secondary tasks, the maximum number of icons at different vehicle speeds and vehicle spacings can be calculated to reduce the likelihood of accidents caused by attention overload.

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“…When the driver completes the secondary task, t(i) and T need to satisfy the relationship shown in Equation (13).…”

Section: Evaluation Model Of Secondary Task Carrying Capacity Under A...mentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

The Effects of Dynamic Complexity on Drivers’ Secondary Task Scanning Behavior under a Car-Following Scenario

Wang

Guo

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…AR and MR are currently considered two of the most promising technologies for providing interfaces to visualize and explore information [9] and they present an opportunity to redefine the way in which people collaborate in fields like telemedicine [10] or intelligent transportation [11].…”

Section: State Of the Art 21 Collaborative Augmented And Mixed Realmentioning

confidence: 99%

Developing the Next Generation of Augmented Reality Games for Pediatric Healthcare: An Open-Source Collaborative Framework Based on ARCore for Implementing Teaching, Training and Monitoring Applications

Vidal-Balea

Blanco-Novoa

Fraga‐Lamas

et al. 2021

Sensors

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Augmented Reality (AR) provides an alternative to the traditional forms of interaction between humans and machines, and facilitates the access to certain technologies to groups of people with special needs like children. For instance, in pediatric healthcare, it is important to help children to feel comfortable during medical procedures and tests that may be performed on them. To tackle such an issue with the help of AR-based solutions, this article presents the design, implementation and evaluation of a novel open-source collaborative framework that enables to develop teaching, training, and monitoring pediatric healthcare applications. Specifically, such a framework allows for building collaborative applications and shared experiences for AR devices, providing functionalities for connecting with other AR devices and enabling real-time visualization and simultaneous interaction with virtual objects. Since all the communications involved in AR interactions are handled by AR devices, the proposed collaborative framework is able to operate autonomously through a Local Area Network (LAN), thus requiring no cloud or external servers. In order to demonstrate the potential of the proposed framework, a practical use case application is presented. Such an application has been designed to motivate pediatric patients and to encourage them to increase their physical activity through AR games. The presented games do not require any previous configuration, as they use ARCore automatic surface detection technology. Moreover, the AR mobile gaming framework allows multiple players to engage in the same AR experience, so children can interact and collaborate among them sharing the same AR content. In addition, the proposed AR system provides a remote web application that is able to collect and to visualize data on patient use, aiming to provide healthcare professionals with qualified data about the mobility and mood of their patients through an intuitive and user-friendly web tool. Finally, to determine the performance of the proposed AR system, this article presents its evaluation in terms of latency and processing time. The results show that both times are low enough to provide a good user experience.

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“…However, when it comes to an intersection between humans and CAVs, the crossing efficiency is still a challenge. Many research works focus on pedestrian detection [5] and cooperative collision warning systems, where CAVs help one another to detect hidden pedestrian obstacles (e.g., in turning movements). This cooperation enhances the safety level of the pedestrian crossing.…”

Section: Introductionmentioning

confidence: 99%

Coordination Between Connected Automated Vehicles and Pedestrians to Improve Traffic Safety and Efficiency at Industrial Sites

et al. 2022

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Transportation in controlled industrial sites provides a conducive environment for technologies of Connected Automated Vehicles (CAV). Recent studies show that safe and efficient road sharing between CAVs and pedestrians is challenging. Besides safety issues, a significant loss of time occurs when pedestrians cross a stream of CAVs. Currently, many techniques have been employed to improve the coordination between CAVs and pedestrians. They focus on pedestrian detection, display of the intention of CAVs, and cooperative collision avoidance. However, one of the most significant sources of information that the pedestrian uses for her/his decision-making is the speed profile of CAVs. This paper aims to provide a safe and efficient pedestrian crossing at industrial sites through communicative crossing behavior. To this end, a suitable speed profile of the CAV is designed by assuming that pedestrians and CAV play a cooperative game to move as close as possible to their desired speed. First, a system analysis is proposed to derive the optimal decision and trajectory for each agent. Then, Deep Reinforcement Learning (DRL) is used to control the longitudinal speed of the CAVs. Compared with Model Predictive Control approach, DRL allows coping with unforeseeable pedestrian behaviors (e.g. long reaction time, varying ideal speed, stop in the middle, etc.). Simulations and experiments with real human testers based on immersive hamlet are performed. Results show that the proposed speed profile outperforms significantly the collision avoidance approach.

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Visual Human–Computer Interactions for Intelligent Vehicles and Intelligent Transportation Systems: The State of the Art and Future Directions

Cited by 57 publications

References 153 publications

The Effects of Dynamic Complexity on Drivers’ Secondary Task Scanning Behavior under a Car-Following Scenario

The Effects of Dynamic Complexity on Drivers’ Secondary Task Scanning Behavior under a Car-Following Scenario

Developing the Next Generation of Augmented Reality Games for Pediatric Healthcare: An Open-Source Collaborative Framework Based on ARCore for Implementing Teaching, Training and Monitoring Applications

Coordination Between Connected Automated Vehicles and Pedestrians to Improve Traffic Safety and Efficiency at Industrial Sites

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