Video anomaly validation (VAV) serves as the final alarm validation step for false positive filtering in video anomaly detection (VAD), requiring both higher accuracy in anomaly identification and stronger justifications. Despite rapid VAD advancements, existing methods still lack sufficient interpretability and struggle with challenging cases. To address this, we propose VAV-R1, a multimodal reasoning model tailored for VAV. We first construct ThinkVAV, a dedicated benchmark with fine-grained reasoning annotations across diverse anomaly types. Furthermore, we introduce DA-GRPO, a difficulty-aware reinforcement learning strategy that prioritizes learning from more challenging cases. Extensive experiments demonstrate that VAV-R1 achieves SOTA performance across multiple tasks.

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.

Discrete data-based learning approaches have facilitated the wide applications of AI models, especially the notably favored foundation models. However, simply scaling the diversity and quantity of training data is still inadequate to achieve human-like thinking and action competency. A shift of learning paradigm from spatially–temporally discrete, weakly correlated, and noninteractive samples to spatially–temporally continuous, strongly correlated, and interactive scenarios is expected to go beyond the element-level understanding and promote the relation, trend, as well as situation awareness abilities of AI models. This article systematically structures the methodology of scenarios engineering (SE) and proposes a three-layer SE roadmap consisting of the scenarios development layer, scenarios organization layer, and scenarios cognition layer. This roadmap is designed to foster the flexible and efficient construction, organization, and utilization of scenarios. Building on this foundation and parallel intelligence, we introduce the framework of scenarios intelligence (SI) that leverages scenarios as next-generation data resources and microworld models to cultivate embodied AI agents, facilitating the development of descriptive, predictive, and prescriptive intelligence in tasks like perception, decision-making, and action. Experiments are conducted with unmanned aerial vehicles (UAVs) to illustrate the effectiveness of the proposed method in environmental understanding, risk assessment, and active perception.

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.

Isolated data islands are prevalent in intelligent automated optical inspection (AOI) systems, limiting the full utilization of data resources and impeding the potential of AOI systems. Establishing a collaborative ecology involving software providers, hardware manufacturers, and factories offers an encouraging solution to build a closed-loop data flow and achieve optimal data resource utilization. However, concerns about privacy issues, rights infringement, and threats from other participants present challenges in establishing an efficient and effective community. In this paper, we propose a novel framework, AOI-OPEN, which first creates a trustworthy AOI ecology to gather related entities with decentralized autonomous organization (DAO) mechanisms. Then, a parallel data pipeline is proposed to generate large-scale virtual samples from small-scale real data for AOI systems. Finally, federated learning (FL) is adopted to use the distributed data resources among multiple entities and build privacy-preserving big models. Experiments on defect classification tasks show that, with privacy preserved, AOI-OPEN greatly strengthens the utilization of distributed data resources and improves the accuracy of inspection models.

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.

Lane detection is a critical technology for autonomous driving, but current deep learning-based methods face significant challenges due to the lack of diverse datasets, especially for nighttime conditions. Most datasets are predominantly composed of daytime images, making it difficult to develop models that perform reliably around the clock. Inspired by the parallel system theory, we explore a novel approach to generate comprehensive 24-hour datasets from daytime images alone. In this paper, we propose the Parallel Scene Information Collaboration (PSIC) framework, designed to enhance 24-hour lane detection using only daytime data. The PSIC framework consists of three key components: artificial scene generation, information collaboration, and lane line detection. First, we address the limitations of existing datasets by proposing two generators—one that transforms daytime images into realistic nighttime scenes, and another that refines nighttime images by adding daytime characteristics. Next, to mitigate noise in the generated scenes, we propose a Multi-Spatial Feature Fusion (MSFF) module that effectively integrates features from both real and artificial scenes through spatial collaboration. Finally, the combined information is used by an anchor-based detection head to accurately identify lane positions. Our experiments on the TuSimple, Night TuSimple, and CULane datasets demonstrate that our method achieves state-of-the-art performance in 24-hour lane line detection, significantly improving reliability and robustness across varying conditions.

In natural disasters, the emergency rescue system is of utmost importance for the smart city development, but the search and rescue (SAR) leveraging unmanned aerial vehicles (UAVs) is under-explored. This study has established an Internet of UAVs architecture motivated by search and rescue activities, focusing on the path planning problem of UAV in three-dimensional urban disaster scenes. A mathematical model for UAV-based SAR missions has been built, aiming to use UAV platforms equipped with life-detection radars to search for survivors inside buildings. Our aim is to search for the most survivors in the shortest amount of time while ensuring coverage of the entire search area. Based on the mathematical model for SAR activities, the improved Multi-Verse Optimizer (IMVO) is used to solve the path of UAV. Finally, simulations were conducted in a photorealistic urban scenario, demonstrating the paths generated by the proposed method. The results indicate the potential of the generated path in terms of search time and the number of survivors discovered.

In this paper, we propose the innovative use of Intelligent Vehicle Carriers (IVCs) as a key solution to address the energy constraints of small-scale unmanned Intelligent Vehicles (IVs). IVCs function as both transporters and charging stations, significantly boosting the operational range and efficiency of IVs. Our research delves into the IV-IVC collaborative framework, highlighting the existing challenges, exploring potential solutions, and examining a range of applications. This study offers a visionary approach to revolutionizing intelligent transportation systems by leveraging the synergistic relationship between IVs and IVCs.

Replacing manual inspection, automated optical inspection (AOI) equipment is widely used in printed circuit board (PCB) factories for automatic PCB defect segmentation. However, parameter refinement of AOI devices has gradually become an efficiency bottleneck in AOI usage, posing a highly challenging task. Since a large number of AOI parameters and different types of inspected objects make timely proper parameter refinement for clear images quite difficult. Considering this, we propose the concept of parallel PCB inspection in cyber–physical–social systems (CPSSs). Based on artificial systems, computational experiments, and parallel execution (ACP) theory with automatic parameter identification and refinement, we perform descriptive intelligence to build an artificial imaging system, obtain knowledge about the mapping relationships of parameter settings and imaging results, and realize automatic parameter identification given image input; conduct predictive intelligence to obtain image quality assessment results and maximize quality score for refinement strategies; and carry out prescriptive intelligence to guide parameter refinement for better imaging. This system could guide engineers proactively with constructive suggestions on parameter refinement when imaging failures occur, greatly reducing the training cost of engineers while improving work efficiency and work quality. To validate that our parallel PCB inspection could perform automatic AOI results evaluation without human participation, we evaluate it on distortion-free and different distortion images and confirm image quality score is positively associated with segmentation accuracy.

Inspired by the term “social radar,” which collects and processes information about social behaviors, this article proposed “sociolinguistic radar,” which represents an emerging branch in social investigation and evaluation system aiming to explore the dynamic correlation between sociophonetic variants and macrosociological categories, such as age, gender, ethnicity, and socioeconomic status. The classic quantitative methods in this field include sociolinguistic surveys and interview, quantitative sociophonetics, and social network analysis. These methods have been proved to be effective in tracking the cognitive correlates of phonological variables. Under the emerging framework of sociolinguistic radar, speakers are no longer passive carriers, but active agents in transforming linguistic styles in the process of forming social differentiations, thus contributing to the construction of new social meaning. With the advancement in neuroscience and artificial intelligence (AI), the neurosociolinguistic and AI-based sociolinguistic radar research will thrive and empower the scope and strength of detecting linguistic variation. The working mechanism of this emerging model leverages neural and AI tool packages to radar and analyze linguistic variation, communication patterns, and diverse sociolinguistic phenomena. This interdisciplinary approach combines the principles of sociolinguistics, which will thoroughly examine the relationship between language and society.

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 the inception of Industry 5.0 in 2021, a growing number of researchers have begun to pay their attention to the revolutionary shift it brings. The principles of Industry 5.0, including human-centric, sustainability, and emphasis on ecological and social values, will become the new paradigm for future industrial development. In this transformative landscape, artificial intelligence (AI) plays a pivotal role, and foundation models based on ChatGPT are set to reshape the organizational structure of industries. In this article, we introduce a multimodal perception and decision-making system built upon a foundational model. This system integrates image and point cloud data to enhance perception accuracy and provide ample information for decision making. It is designed to achieve a deep integration of AI and human-centric autonomous driving within the context of Industry 5.0. We introduce a cross-domain learning approach in the system architecture, along with a model training method from foundation models to handle complex road conditions. The proposed method enables road drivable area segmentation on complex unstructured roads. To address the issue of increased variance caused by the residual structure employed in previous works, this article introduces a distribution correction module, which effectively mitigates this problem. Furthermore, to achieve high-performance perception systems in intricate road scenarios, we put forth a multimodal perception fusion method in this study. The experiments demonstrate the superiority of this approach over single-sensor perception. This work contributes to the ongoing discourse on the convergence of AI, human-centric values, and advanced driving systems within the framework of Industry 5.0.

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.

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.

The printed circuit boards (PCBs) industry is one of the fastest-growing industries in recent decades. The PCB manufacturing process is highly complicated and severely affected by social factors, which makes it very important to conduct integrated inspection, assuring and improving the production quality. In this article, we propose an artificial systems, computational experiments, and parallel execution-based integrated inspection method in cyber–physical–social systems (CPSS) to realize smart manufacturing. In this inspection system, rather than simply performing modeling, analysis, and control, we perform descriptive intelligence to construct production processes with limited multimodal information, perform predictive intelligence to conduct defect detection and defect prediction, and perform prescriptive intelligence to achieve defect diagnosis and defect management. In this way, our inspection system could offer a learning and training platform for workers to master professional inspection skills, provide an experimentation and evaluation platform for product defect monitoring and early warnings, and supply guidance about defect management and control to improve manufacturing processes. For technical implementation, we leverage a Transformer-based foundation model to achieve knowledge reasoning and human–computer interaction. As a result, we provide an innovative solution to cope with the challenges of quality inspection in current smart manufacturing, and expect its further applications in the PCB industry.

Recognizing and locating objects by algorithms are essential and challenging issues for Intelligent Transportation Systems. However, the increasing demand for much labeled data hinders the further application of deep learning-based object detection. One of the optimal solutions is to train the target model with an existing dataset and then adapt it to new scenes, namely Unsupervised Domain Adaptation (UDA). However, most of existing methods at the pixel level mainly focus on adapting the model from source domain to target domain and ignore the essence of UDA to learn domain-invariant feature learning. Meanwhile, almost all methods at the feature level ignore to make conditional distributions matched for UDA while conducting feature alignment between source and target domain. Considering these problems, this paper proposes the ParaUDA, a novel framework of learning invariant representations for UDA in two aspects: pixel level and feature level. At the pixel level, we adopt CycleGAN to conduct domain transfer and convert the problem of original unsupervised domain adaptation to supervised domain adaptation. At the feature level, we adopt an adversarial adaption model to learn domain-invariant representation by aligning the distributions of domains between different image pairs with same mixture distributions. We evaluate our proposed framework in different scenes, from synthetic scenes to real scenes, from normal weather to challenging weather, and from scenes across cameras. The results of all the above experiments show that ParaUDA is effective and robust for adapting object detection models from source scenes to target scenes.

Offline training and testing are playing an essential role in design and evaluation of intelligent vehicle vision algorithms. Nevertheless, long-term inconvenience concerning traditional image datasets is that manually collecting and annotating datasets from real scenes lack testing tasks and diverse environmental conditions. For that virtual datasets can make up for these regrets. In this paper, we propose to construct artificial scenes for evaluating the visual intelligence of intelligent vehicles and generate a new virtual dataset called “ParallelEye-CS”. First of all, the actual track map data is used to build 3D scene model of Chinese Flagship Intelligent Vehicle Proving Center Area, Changshu. Then, the computer graphics and virtual reality technologies are utilized to simulate the virtual testing tasks according to the Chinese Intelligent Vehicles Future Challenge (IVFC) tasks. Furthermore, the Unity3D platform is used to generate accurate ground-truth labels and change environmental conditions. As a result, we present a viable implementation method for constructing artificial scenes for traffic vision research. The experimental results show that our method is able to generate photorealistic virtual datasets with diverse testing tasks.