Existing studies on traffic flow prediction primarily rely on on-board devices to collect vehicle trajectory data , which can potentially infringe upon the privacy of users and limit the applicability of the method. Additionally, traffic flow prediction remains challenging due to the complex spatial and temporal dependencies within real-world traffic networks. To address these limitations, this paper introduces a framework for analyzing discrete vehicle trajectory data at urban intersections. By incorporating various external physical factors into traffic flow prediction, this framework derives embedding vectors from vehicle trajectory sequences and road network topology , modeling their spatio-temporal dependencies using Skip-Gram and GraphSAGE, respectively. Additionally, the intersection similarity is introduced to capture and integrate traffic flow patterns between the target intersection and similar intersections. A Spatio-Temporal Graph Convolutional Neural Network (ST-GCN) algorithm, which combines Graph Convolutional Networks (GCN) with Long Short-Term Memory (LSTM), is developed to achieve precise traffic flow prediction. Extensive experiments on a real-world traffic flow dataset from Qingdao, China, validate that the proposed method outperforms state-of-the-art baseline methods .

Industry 5.0 heralds a new wave of the industrial revolution, placing a spotlight on human-centric intelligent manufacturing. At the core of Industry 5.0 lies the human-cyber-physical system (HCPS), a composite intelligent system where interactions among humans, cyberspace, and physical assets are orchestrated across diverse manufacturing levels and phases. Understanding the pivotal roles played by humans in these advanced systems is of paramount importance. Nonetheless, the exploration of HCPS within the context of Industry 5.0 remains in its infancy. This paper presents a holistic literature review of industrial HCPS from a human-centric perspective. A united architecture is employed to encompass the aspects of cognitive-to-technology integration and human-to-human interaction in HCPS, highlighting human-in-the-loop, human-on-the-loop, and human-in-the-society paradigms. The mechanisms of these paradigms and their effects on design, production, and service are investigated to expand the research landscape of intelligent manufacturing in Industry 5.0. Key enabling technologies that facilitate harmonious tri-space integration are introduced, and the future challenges of industrial HCPS are discussed. This work is expected to attract more open discussions and in-depth research on HCPS in the new industrial revolution era.Note to Practitioners—This paper is motivated by the emergence of Industry 5.0 that integrates humans into cyber-physical systems to offset drawbacks on both sides. It presents an overview of HCPS-related works to identify the state-of-the-art and open problems in the Industry 5.0 era. The review of HCPS applications in the design, production, and service phases can benefit engineers in the intelligent manufacturing area. Key enabling technologies on human ability augmentation, human-robot interaction, digital twin, human-cyber-physical data fusion, crowdsourcing, and system modeling, are analyzed to attract researchers in broader research fields to join in the development of industrial HCPS.

Autonomous driving (AD) will rely, either fully or partially, on data-driven approaches. As such, being aware of the algorithm limitation is crucial when implementing learning-based methods in such safety-critical contexts. A comprehensive AD monitor allows control authority to be transferred promptly to a contingency backup solution when the vehicle is recognized in impasses. To address this challenge, we propose MonitorGAN, a Bayesian discriminator trained within an adversarial framework, designed to recognize unknown traffic scenarios and monitor planning quality in open-world autonomous driving. Additionally, it is designed to be aware of its own limitations using a Bayesian approach. Unlike previous epistemic uncertainty estimation algorithms for self-driving, MonitorGAN is independent and planner-agnostic, capable of monitoring various types of planners without requiring real outlier exposure. MonitorGAN is trained exclusively on Argoverse 2 and tested through extensive cross-dataset experiments, including NGISM, HighD, RounD, and NuScenes, across three common planning schemes: learning-based, polynomial-based, and optimization-based, all of which use the same training dataset for interaction-aware planning. Both quantitative results and qualitative comparisons with other epistemic uncertainty estimation algorithms indicate that our approach can estimate the feasibility of the AD’s planning in a planner-agnostic manner and ensure safety.

Advancements in autonomous driving technologies continue to revolutionize transportation, yet the full realization of self-driving vehicles remains hampered by several critical challenges. Automated vehicles continue to encounter significant challenges in perception, prediction, and decision-making, while their low-level modules are relatively mature and robust. Conversely, humans surpass machines in terms of high-level intelligence but may suffer from control performance degradation. To coalesce the strength of the human and machine, a novel collaboration scheme is proposed to compensate for the prediction-decision uncertainty via human guidance while providing low-level control feedback to the driver. This approach generates multiple decision candidates and corresponding predictions for other road users using a transformer-based socially compliant generative adversarial network (SCGAN). The driver can assist in choosing the appropriate candidate using the context-understanding capability, while concurrently, the control projection of this chosen decision guides the driver to achieve the desired objective via haptic steering feedback. The haptic feedback can reflect the decision uncertainties enabled by the decision-control projection of the intention estimation of the ego vehicle. A Type-II fuzzy controller is utilized to determine the control authority to account for the complexity of the future movement. We verify the effectiveness of the proposed algorithm through a real-time human-in-the-loop experiment, including an ablation study and comparisons with other human-machine collaboration schemes. The results demonstrate that the proposed scheme can minimize human-machine conflicts while increasing system safety.

While existing Learning from intervention (LfI) methods within the human-in-the-loop reinforcement learning (HiL-RL) paradigm mainly operate on the assumption that human policies are homogeneous and deterministic with low variance, natural human driving behaviors are multimodal with intrinsic uncertainties, and hence, accommodating diverse human capabilities is significant for its practical applications. This work proposes an enhanced LfI approach for learning the optimal RL policy by leveraging multimodal human behaviors in the setting of N-driver concurrent interventions. Specifically, we first learn the N number of human digital drivers from the multi-human demonstration dataset, wherein each driver possesses its own policy distribution. Then, the post-trained drivers will be kept in the training loop of the RL algorithms, providing diverse driving guidance whenever the intervention is required. Additionally, to better utilize the provided guidance, we augment the RL regarding the fundamental architecture and optimization objectives to facilitate the proposed uncertainty-aware reinforcement learning (UnaRL) algorithm. The proposed approach, which won 2ndplace in the Alibaba Future Car Innovation Challenge 2022, is solidly compared in two challenging autonomous driving scenarios against state-of-the-art (SOTA) LfI baselines, and results of both simulation and real-world experiment confirm the superiority of our method in terms of learning robustness and driving performance. Videos and source code are provided.1

Human–robot hybrid disassembly cells provide a shared workspace that synergizes the strength of both humans and robots. These cells are characterized by adaptability and reconfigurability to accommodate the frequent changes stemming from diverse products, championing the mass personalization paradigm in Industry 5.0. Disassembly sequence planning assumes paramount importance within hybrid cells but proves to be a formidable challenge. Conventional methods prioritize the fulfillment of product-related constraints while neglecting the ergonomics considerations of operators. This oversight runs counter to the human-centric ethos central to Industry 5.0. This article proposes a personalized disassembly sequence planning approach for a human–robot hybrid disassembly cell. It presents a biobjective disassembly sequence planning model that concurrently addresses sequence scheduling and task allocation. Personal ergonomics are automatically assessed by analyzing joint angles within the operator's body skeleton. To yield disassembly plans that optimize both benefit and efficiency, a hybrid multiobjective ant lion optimizer is proposed featuring improved encoding/decoding mechanisms, updating strategies, and constraint satisfaction strategies. It adeptly addresses the discrete nature of disassembly sequences and the binary attributes associated with task execution and assignment. Personalized disassembly experiments are carried out to illustrate the feasibility and practicability of the proposed approach.

Interacting with the surrounding road users is crucial for autonomous vehicles (AV). However, the inherent multimodality and uncertainties associated with traffic participants (TP) pose challenges in AVs’ prediction and decision-making (PnD). A primary challenge is adapting predictors trained on static offline datasets to the dynamic, diverse data streams encountered in reality. Secondly, utilizing one single forecast trajectory with the highest probability for decision-making contains potential risks as it neglects that even a small probability represents a subset of TP behaviors. Based on the existing prediction backbone, we propose an online learning approach incorporating pseudo-labels inferred from partial feedback as compensation for conventional methodologies, considering both the commonsense and personalization facets of driving. Drawing inspiration from the second law of thermodynamics, we propose to minimize microscopic traffic entropy as an additional objective in decision-making. This objective aims to reduce the chaos of traffic scenes, thus achieving more predictable future interactions and, conversely, making future decisions easier. Through real-time human-in-the-loop experiments, we quantifiably and comparably reveal that adopting one single trajectory without online learning in PnD is risky. However, this reliability is verified to be significantly improved by our proposed techniques, and the efficacy is further analyzed in a subsequent qualitative study. A static experiment transferring the prediction algorithm trained exclusively on Argoverse 2 to datasets including NGSIM, HighD, RounD, and NuScenes is also conducted, demonstrating that the proposed correction can effectively mitigate the gap between the datasets and real-world scenarios.

Successful product development relies on the enterprise system (ES) to manage product lifecycle data and support decision-making in various levels. Since the amount of data generated in the industrial Internet of Things is increasing dramatically, new paradigms of the ES are required to realize the distributed intelligence. Recent progress in edge computing has enabled advances in decentralized decision support systems. Although the product design is a key stage of new product development, none of the recent studies in edge-based ES have shed light on this stage. Therefore, an edge-based ES framework for design scheme evaluation is proposed in this article. It not only accomplishes most of the decision-making tasks within the edge mesh of an enterprise, but also solves the information island of multiple decision makers from different geographical regions. Moreover, a multigroup decision-making algorithm is proposed to enable collaborative design scheme evaluation. The evaluation processes of designers, experts, and customers are analyzed systematically in this article. For one advantage, the trapezium cloud model is applied to convert the qualitative evaluation information given by designers and experts into qualitative values. It decreases the cognitive discrepancy and solves the information distortion. For another, EEG data are utilized to explore the implicit psychological states of customers during the product operation. All the evaluation results of multiple groups of decision makers are integrated by the fuzzy measure and Choquet integral to determine the optimal design scheme. A case study is conducted to illustrate the feasibility of the method proposed in this article.