The increasing automation of modern systems—across industry, healthcare, mobility, and beyond—has raised the demand for human reasoning and expertise, while alleviating the burden of repetitive tasks. This transformation is driving us toward Automation 5.0, a new paradigm aimed at unleashing human potential. Recently, the development of foundation models (FMs) has reinvigorated its realization, making it both urgent and critical to explore the concept of Automation 5.0 in this new era. In this article, we define Automation 5.0, discuss its significance, and emphasize its new world, thinking, and technology with the goal of achieving knowledge automation. A framework, based on business FMs, human-oriented operating systems, and scenarios engineering, is proposed, where biological, robotic, and digital humans work together in three modes: autonomous, parallel, and expert/emergency modes. Additionally, a diverse range of its scenarios and applications are summarized and discussed, such as Manufacturing 5.0, Healthcare 5.0, and Transportation 5.0. We believe that Automation 5.0 can drive the co-evolution of productivity and production relations across all domains, propelling society toward a “Safety, Security, Sustainability, Sensitivity, Service, Smartness (6S)” future.

Point cloud data labeling is considered a time-consuming and expensive task in autonomous driving, whereas annotation-free learning training can avoid it by learning point cloud representations from unannotated data. In this paper, we propose AFOV, a novel 3D Annotation-Free framework assisted by 2D Open-Vocabulary segmentation models. It consists of two stages: In the first stage, we innovatively integrate high-quality textual and image features of 2D open-vocabulary models and propose the Tri-Modal contrastive Pre-training (TMP). In the second stage, spatial mapping between point clouds and images is utilized to generate pseudo-labels, enabling cross-modal knowledge distillation. Besides, we introduce the Approximate Flat Interaction (AFI) to address the noise during alignment and label confusion. To validate the superiority of AFOV, extensive experiments are conducted on multiple related datasets. We achieved a record-breaking 47.73% mIoU on the annotation-free 3D segmentation task in nuScenes, surpassing the previous best model by 3.13% mIoU. Meanwhile, the performance of fine-tuning with 1% data on nuScenes and SemanticKITTI reached a remarkable 51.75% mIoU and 48.14% mIoU, outperforming all previous pre-trained models.

Parallel LiDAR emerges as an innovative framework for next-generation intelligent LiDAR systems in autonomous driving. In parallel LiDAR research, V2V (Vehicle-to-Vehicle) cooperative perception is a promising technology which can effectively enhance perception range and accuracy through inter-agent information exchange. Currently, sensor heterogeneity remains a critical challenge in V2V. Although some work has made initial attempts to address this issue, existing studies are primarily conducted under ideal clear-weather conditions, ignoring the impact of variable weather factors in real-world applications. In fact, adverse weather has been shown to significantly degrade the performance of LiDAR systems, with the risk of cumulative degradation in V2V. To address this challenge, we first introduce OPV2V-W and V2V4Real-W as new benchmarks to study sensor heterogeneity in V2V under adverse weather. Then we propose the HPLaw architecture (Heterogeneous Parallel LiDARs for Adverse Weather), a self-knowledge distillation method designed to enhance model robustness across varying weather scenarios. HPLaw employs an efficient PF network to facilitate heterogeneous feature fusion and incorporates an SAKD module to extract weather-invariant features. Extensive experiments demonstrate that the student model in HPLaw achieves outstanding performance under all weather conditions, exhibiting remarkable robustness.

Aiming to boost production efficiency and reduce human workload, human-centricity has emerged as the core concept of Industry 5.0 (I5.0). However, current works have not established a unified automation and autonomous framework for human-centric smart manufacturing across various real world applications. Addressing this gap, this research introduces an innovative automated framework, ParallelWorkforce, which integrates blockchain intelligence and decentralized autonomous organizations and operations (DAOs) to drive the evolution from digital twins to parallel intelligence. First, this research conducts a comprehensive investigation into smart manufacturing in I5.0, summarizing the ongoing evolution. Next, a detailed exploration of ParallelWorkforce is provided to offer customized strategies for managing different levels of out-of-distribution events, significantly alleviating the workload on biological workers and maximizing the potential of both digital and robotic workers. Finally, the development of ParallelWorkforce across various key applications of smart manufacturing is demonstrated, including autonomous transportation, task assignment, and worker management. This research provides a viable solution for the further development of human-centered smart manufacturing and paves the way for the realization of “6S” goals in I5.0.

Under the impetus of Industry 4.0, oil and gas is undergoing an unprecedented digital transformation, and many innovative ideas are proposed. However, the achieved higher efficiency comes at the expense of reduced social consideration, which necessitates more society-related research and calls for a technological paradigm shift. To meet this challenge, this article introduces parallel oil and gas within the framework of parallel intelligence-based Industry 5.0, providing a pivotal transition from cyber–physical systems (CPS) to cyber–physical–social systems (CPSS). A comprehensive review of oil and gas industrial chain that covers upstream, midstream, and downstream is first outlined. Grounded in Industry 5.0, the main principles of parallel oil and gas are then provided, where three kinds of workers (biological workers, digital workers, and robotic workers) and three operation modes (autonomous modes, parallel modes, and expert/emergency modes) collaborate to develop more human-oriented and resilient systems. To realize the desired vision, some enabling technologies, including blockchain, smart contracts, and industrial foundation models, are thereafter listed. Furthermore, computational experiments on fault diagnosis of sucker rod pumps are conducted to illustrate the feasibility and effectiveness of our proposed mechanism. Finally, the future trend toward imaginative intelligence is envisaged.

Since Manufacturing 4.0 faces various challenges, including the risks of data leakage and privacy violation, the struggle to meet the growing demand for personalization, and the limitations in harnessing human creativity, it has become crucial to embark on a transformation toward Manufacturing 5.0. To this end, we propose a DeFACT framework for parallel manufacturing and Manufacturing 5.0, which focuses on safe, efficient and personalized collaborative production. In DeFACT, different enterprises and parallel workers (i.e., digital, robotic and biological workers) are organized, coordinated and scheduled based on decentralized autonomous organizations and operations to promote mutual benefits among members, even in the context of low or zero trust. This contributes to providing customers with higher-quality personalized products and services while ensuring the confidentiality and safeguarding of data. Additionally, various advanced technologies, such as generative artificial intelligence, scenarios engineering, and blockchain, are leveraged to achieve trustworthy and adaptable decision making, user-friendly human–machine interaction, and the federated control and management of parallel workers. Finally, the effectiveness and efficiency of DeFACT are experimentally validated through the design and implementation of three case studies.

Aiming to provide a novel paradigm of oil fields, metaverses-based parallel oil fields are proposed in this article. Compared with the existing smart/intelligent oil fields in cyber–physical systems (CPS), parallel oil fields can take human factors into full consideration and expand the operation space to cyber–physical–social systems (CPSS), which can be regarded as the abstract and scientific explanation of metaverses. In the proposed parallel oil fields, there are three kinds of workers (human workers, digital workers, and robotic workers) coordinating to construct a more reliable and intelligent oil field. Furthermore, the framework and methodology of parallel oil fields are illustrated by parallel systems and the artificial systems, computational experiments, and parallel executions (ACP) approach. Based on the proposed framework, parallel oil fields are capable of generating a more trustworthy artificial system and guaranteeing the realization of the 6S (safety, security, sustainability, sensitivity, service, and smartness) goal. Finally, based on dynamometer cards, fault diagnosis of sucker rod pumping systems (SRPS) is investigated in parallel oil fields.

Industrial processes are typical cyber–physical–social systems (CPSSs), where the effective management of employees and the efficient control of machines play important roles. Traditional industries heavily rely on human labor and neglect the development of collection–utilization–transmission integrated information loops, thereby leading to high costs and low efficiency in operational procedures. To facilitate the natural interactions and smart operations for humans and machines, industrial foundation models (IFMs) based on metaverses are proposed in this article, serving as the operating systems of industrial parallel machines that provide sustainable data resources and scenarios for management and control experiments. On this basis, IFM comprised of vision foundation models, language foundation models, as well as operational foundation models, are constructed to manage resources in industrial parallel machines and provides comprehensive services for industrial procedures. On the one hand, IFM can efficiently manage various resources including computing power, digital assets, enterprise resources, and platform I/O via the proposed CPSS-based competing, sharing, scheduling, monitoring, allocating, and recovering mechanisms. On the other hand, imaginative intelligence, linguistic intelligence, and algorithmic intelligence can be achieved through vivid visualization of vision foundation models, natural conversations of language foundation models, and smart manipulation of operational foundation models. With the proposed IFM, cyber–physical–social intelligence (CPSI) can be achieved to enhance the efficient management and control of industrial processes.