Human–Machine Telecollaboration Accelerates the Safe Deployment of Large-Scale Autonomous Robots During the COVID-19 Pandemic

Hu, Zhongxu; Zhang, Yiran; Li, Qinghua; Lv, Chen

doi:10.3389/frobt.2022.853828

Cited by 6 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is both the data generator and the recipient of system services. Whether in a semi-autonomous driving system or a fully autonomous driving system, humans will always play an important role and coexist with vehicles [ 43 , 44 ]. Digital driver is a virtual replica of the human driver.…”

Section: Parallel Driving Architecture Under Mixed Traffic and Its Ch...mentioning

confidence: 99%

Parallel Driving with Big Models and Foundation Intelligence in Cyber–Physical–Social Spaces

Wang,

Huang,

Tian

et al. 2024

Research

Self Cite

View full text Add to dashboard Cite

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs); on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the “6S” goals of parallel driving.

show abstract

Section: Parallel Driving Architecture Under Mixed Traffic and Its Ch...mentioning

confidence: 99%

Parallel Driving with Big Models and Foundation Intelligence in Cyber–Physical–Social Spaces

Wang,

Huang,

Tian

et al. 2024

Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…The DDT can be implemented to address the challenges of a growing shortage of skilled drivers in specific sectors and assist aging drivers in improving the safety, smoothness, and efficiency of operation. Meanwhile, humans will always play an important role and coexist with vehicles whether the vehicle is partially or fully autonomous [70][71][72]. Thus, futuristic vehicles should meet human needs and recognize individual preferences to form a human-centric intelligent driving system that provides a safe, efficient, and personalized user experience.…”

Section: A Human Driver In Real Worldmentioning

confidence: 99%

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Lou

Xing

et al. 2022

IEEE Trans. Intell. Veh.

Self Cite

View full text Add to dashboard Cite

Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system.

show abstract

“…Therefore, the smartness, safety, and efficiency of interactions and collaborations between humans and agents should continue to improve [ 4 , 5 , 6 , 7 , 8 , 9 ]. In particular, autonomous driving technology could allow humans to share control with intelligent vehicles [ 10 , 11 , 12 , 13 , 14 , 15 ]. A well-designed co-pilot system requires the vehicle to understand the behavior and state of the driver [ 16 , 17 , 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Framework for Multi-State Driver Monitoring Using Heterogeneous Loss and Attention-Based Feature Decoupling

Zhang

Xing

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Multi-state driver monitoring is a key technique in building human-centric intelligent driving systems. This paper presents an integrated visual-based multi-state driver monitoring framework that incorporates head rotation, gaze, blinking, and yawning. To solve the challenge of head pose and gaze estimation, this paper proposes a unified network architecture that tackles these estimations as soft classification tasks. A feature decoupling module was developed to decouple the extracted features from different axis domains. Furthermore, a cascade cross-entropy was designed to restrict large deviations during the training phase, which was combined with the other features to form a heterogeneous loss function. In addition, gaze consistency was used to optimize its estimation, which also informed the model architecture design of the gaze estimation task. Finally, the proposed method was verified on several widely used benchmark datasets. Comprehensive experiments were conducted to evaluate the proposed method and the experimental results showed that the proposed method could achieve a state-of-the-art performance compared to other methods.

show abstract

Human–Machine Telecollaboration Accelerates the Safe Deployment of Large-Scale Autonomous Robots During the COVID-19 Pandemic

Cited by 6 publications

References 14 publications

Parallel Driving with Big Models and Foundation Intelligence in Cyber–Physical–Social Spaces

Parallel Driving with Big Models and Foundation Intelligence in Cyber–Physical–Social Spaces

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

An Integrated Framework for Multi-State Driver Monitoring Using Heterogeneous Loss and Attention-Based Feature Decoupling

Contact Info

Product

Resources

About