Tracking Management Research Articles

Deep learning-based target tracking with X-ray images for radiotherapy: a narrative review.

As one of the main treatment modalities, radiotherapy (RT) (also known as radiation therapy) plays an increasingly important role in the treatment of cancer. RT could benefit greatly from the accurate localization of the gross tumor volume and circumambient organs at risk (OARs). Modern linear accelerators (LINACs) are typically equipped with either gantry-mounted or room-mounted X-ray imaging systems, which provide possibilities for marker-less tracking with two-dimensional (2D) kV X-ray images. However, due to organ overlapping and poor soft tissue contrast, it is challenging to track the target directly and precisely with 2D kV X-ray images. With the flourishing development of deep learning in the field of image processing, it is possible to achieve real-time marker-less tracking of targets with 2D kV X-ray images in RT using advanced deep-learning frameworks. This article sought to review the current development of deep learning-based target tracking with 2D kV X-ray images and discuss the existing limitations and potential solutions. Finally, it also discusses some common challenges and potential future developments. Manual searches of the Web of Science, and PubMed, and Google Scholar were carried out to retrieve English-language articles. The keywords used in the searches included "radiotherapy, radiation therapy, motion tracking, target tracking, motion estimation, motion monitoring, X-ray images, digitally reconstructed radiographs, deep learning, convolutional neural network, and deep neural network". Only articles that met the predetermined eligibility criteria were included in the review. Ultimately, 23 articles published between March 2019 and December 2023 were included in the review. In this article, we narratively reviewed deep learning-based target tracking with 2D kV X-ray images in RT. The existing limitations, common challenges, possible solutions, and future directions of deep learning-based target tracking were also discussed. The use of deep learning-based methods has been shown to be feasible in marker-less target tracking and real-time motion management. However, it is still quite challenging to directly locate tumor and OARs in real-time with 2D kV X-ray images, and more technical and clinical efforts are needed. Deep learning-based target tracking with 2D kV X-ray images is a promising method in motion management during RT. It has the potential to track the target in real time, recognize motion, reduce the extended margin, and better spare the normal tissue. However, it still has many issues that demand prompt attention, and further development before it can be put into clinical practice.

A rail fastener defect detection algorithm based on improved YOLOv5

Being a crucial component of railway tracks, monitoring the health condition of fasteners stands as a critical aspect within the realm of railroad track management, ensuring the normal passage of trains. However, traditional track fastener detection methods mainly use artificial checks, giving rise to challenges encompassing reduced efficiency, safety hazards, and poor detection accuracy. Consequently, we introduce an innovative model for the detection of track fastener defects, termed YOLOv5-CGBD. In this study, we first imbue the backbone network with the CBAM attention mechanism, which elevates the network’s emphasis on pertinent feature extraction within defective regions. Subsequently, we replace the standard convolutional blocks in the neck network with the GSConv convolutional module, achieving a delicate balance between the model’s accuracy and computational speed. Augmenting our model’s capacities for efficient feature map fusion and reorganization across diverse scales, we integrate the weighted bidirectional feature pyramid network (BiFPN). Ultimately, we manipulate a lightweight decoupled head structure, which improves both detection precision and model robustness. Concurrently, to enhance the model’s performance, a data augmentation strategy is employed. The experimental findings testify to the YOLOv5-CGBD model’s ability to conduct real-time detection, with mAP0.5 scores of 0.971 and 0.747 for mAP0.5:0.95, surpassing those of the original YOLOv5 model by 2.2% and 4.1%, respectively. Furthermore, we undertake a comparative assessment, contrasting the proposed methodology with alternative approaches. The experimental outcomes manifest that the YOLOv5-CGBD model exhibits the most exceptional comprehensive detection performance while concurrently maintaining a high processing speed.

Cross-Video Pedestrian Tracking Algorithm with a Coordinate Constraint

Pedestrian tracking in surveillance videos is crucial and challenging for precise personnel management. Due to the limited coverage of a single video, the integration of multiple surveillance videos is necessary in practical applications. In the realm of pedestrian management using multiple surveillance videos, continuous pedestrian tracking is quite important. However, prevailing cross-video pedestrian matching methods mainly rely on the appearance features of pedestrians, resulting in low matching accuracy and poor tracking robustness. To address these shortcomings, this paper presents a cross-video pedestrian tracking algorithm, which introduces spatial information. The proposed algorithm introduces the coordinate features of pedestrians in different videos and a linear weighting strategy focusing on the overlapping view of the tracking process. The experimental results show that, compared to traditional methods, the method in this paper improves the success rate of target pedestrian matching and enhances the robustness of continuous pedestrian tracking. This study provides a viable reference for pedestrian tracking and crowd management in video applications.

Resilient Chain: AI-Enhanced Supply Chain Security and Efficiency Integration

In most recent time, the global landscape of supply chain management has experienced unprecedented challenges during the COVID-19 pandemic, which has significantly impacted the routine and smooth operations of different firms in the United States. This paper explains about the importance of artificial intelligence (AI) and machine learning (ML) in the mitigation of these disruptions and possible ways of improving supply chain security and its efficiency. This research adopted a questionnaire survey involving 281 managers, with the aim to comprehensively examine the current state of AI integration across the U.S. supply chain sector, with focus on some key components like real-time tracking, cost optimization, and risk management. A mixed method approach was adopted for this research, utilizing both inferential and descriptive analyses to unravel insights and trends into the role of AI in enhancing supply chain security. The results indicate that integrating AI, most especially through cost optimization, real-time tracking, and risk management components, emerges as a significant determinant of supply chain security in the United States. Real-time tracking technologies are identified as crucial for monitoring shipments and assets, enabling quick responses to security incidents, and ensuring end-to-end visibility throughout the supply chain. Despite the potential benefits, the study highlights challenges that hinder the widespread integration of AI technologies in the U.S. supply chain. The high cost of AI adoption and the limited availability of skilled personnel are major obstacles. To address these challenges, the paper proposes practical recommendations. Firstly, real-time tracking technologies are recommended to monitor shipments and assets, facilitating rapid responses to security incidents, and ensuring visibility across the entire supply chain. Furthermore, the paper suggests optimizing costs by investing in cost-effective security solutions. This includes leveraging AI for automated monitoring systems and adopting secure packaging measures. These strategies aim to minimize vulnerabilities without compromising security standards, offering a balanced approach to enhancing supply chain security while mitigating the financial implications associated with AI adoption.

ВПЛИВ ЕКОНОМІКИ 4.0 НА ГЛОБАЛЬНІ ЛОГІСТИЧНІ ОПЕРАЦІЇ ТРАНСНАЦІОНАЛЬНИХ КОРПОРАЦІЙ

The article examines the transformation of logistics processes under the influence of the latest technologies that characterize Economy 4.0. The main goal of the study is to analyze the impact of the Economy 4.0 concept on the global logistics operations of multinational corporations. The authors analyze how the implementation of these technologies helps to optimize supply chains, reduce costs, increase transparency and efficiency of inventory management, and ensure seamless delivery. It studies how TNCs adapt to the changes caused by Economy 4.0 and how these changes affect their competitiveness in the world market. The article also reveals the challenges and opportunities that arise before TNCs in the context of the integration of Economy 4.0 technologies. In particular, it examines the impact of global crises such as the COVID-19 pandemic, military conflicts and climate change on logistics processes, as well as the strategies that companies can apply to minimize these risks. Based on the analysis, it is concluded that the integration of innovative technologies is key to improving the efficiency of logistics operations in TNCs, allowing them to achieve better results in a globalized world. A number of recommendations have been developed for TNCs regarding the implementation of Economy 4.0 technologies, which will help them optimize their logistics processes, increase the level of customer satisfaction, and strengthen their positions on the market. One of the key scientific innovations is the development of a theoretical model that integrates Economy 4.0 technologies in the context of global logistics, providing a new perspective on supply chain management in the digital economy. Economy 4.0 allows logistics operators to integrate advanced analytical tools and supply chain management systems, providing greater transparency and accuracy in cargo tracking, route optimization and inventory management. The study identifies and systematizes specific changes in logistics operations due to the introduction of these technologies, and determines how these changes contribute to the optimization of supply chains, increasing efficiency and competitiveness at the international level.

Reducing catheter-associated urinary tract infections in a large health system: a quality improvement approach using a fractal management system

Abstract Objective: Although preventable through established infection control practices, catheter-associated urinary tract infections (CAUTIs) remain prevalent in acute-care settings. Our goal was to improve the CAUTI rates through multiple hospitals through implementing sustainable practices, including enhancing communication, provider engagement, accountability, and transparency in reporting to achieve long-term improvements. Design: Quality improvement with multiple levels of interventions Setting: A health system in northern Ohio with 21 affiliated hospitals across 16 counties. Patients: Adult patients admitted to the hospital between June 2020 and June 2023. Methods: A broad set of quality improvement (QI) strategies was developed by an interdisciplinary team and guided by the Fractal Management System framework to ensure accountability, communication, and alignment across teams and facilities. Key drivers were indwelling urinary catheter (IUC) alternatives, insertion, maintenance, removal, and smart diagnostics. The main outcome measures were standardized infection ratios (SIR) and standardized utilization ratio (SUR), comparing period 1 (P1, June 2020 to December 2021) and period 2 (P2, January 2022 to June 2023). Results: Enhanced communication and management played crucial roles in minimizing IUC placement. Updated policies and protocols, coupled with clear guidelines and decision support tools, facilitated effective urinary management. Performance tracking and visual management boards provided real-time insights, while collaborative efforts, including staff huddles and multidisciplinary teamwork, ensured consistent adherence to best practices. Conclusions: A systemwide QI initiative focused on enhanced communication, management, and collaboration contributed to improved SIR and reduced CAUTI rates across multiple hospitals, highlighting the impact of strong communication and proactive management in healthcare settings.

Image segmentation method of rail head defects and area measurement of selected segments

The operation safety of railway transport, which is the most important economic and social factor, is largely determined by the technical condition of the rail track and measures to maintain the quality of its track management system. One of the system elements for ensuring the accident-free operation of the track is the technical diagnosis of rails using a method complex of non-destructive control of rails, such as acoustic (ultrasonic), magnetic, combined, etc., and monitoring of the track using methods of measuring the geometry of the rail track and its disturbances. When the wheel interacts with the rail, especially on high-speed and load-stressed sections, defects and damage inevitably occur in the rails. A rather large share of such defects are on the rolling surface of the rail head. Formed defects develop rapidly, which seriously complicates the safety of train traffic. Therefore, accurate and quick detection of defects on the rolling surface of the rail head is very important. However, it is quite difficult to detect defects on the rolling surface of the rail by the acoustic (ultrasound) method due to the violation of tight contact between the rolling surface of the rail head and the piezoelectric transducer. In this case, it is quite convenient to detect surface defects of the rail head using video control. The article provides a comparative analysis of segmentation methods. There has been presented the method of image segmentation of main rail defects based on general contour preparation and parallel-hierarchical (PH) transformation using their classification. The parallel-hierarchical transformation method allows to increase the segmentation accuracy of individual areas in the original image compared to similar ones. The algorithm of pyramidal generalized-contour preparation and the criterion system allows, by calculating the threshold for each level of the gray scale, to present the study of the image with the corresponding contour preparations at the segmentation level. Modeling of recursive generalized-contour preparation and PH transformation method for image segmentation problem of rail head defects shows that, compared to the segmentation method based on the increase of areas, the accuracy of image segmentation is better. A modified method of calculating the image contour area based on the coding of lines forming the boundaries of the black and white areas of the two-gradation image has been given.

Heterogeneity‐oriented ensemble learning for rail monitoring based on vehicle‐body vibration

AbstractIn this study, railway vehicle‐body vibration was applied to rail detection for convenient sensor deployment and cost‐effectiveness. However, the waveform is difficult to analyze due to damping and interference. Data‐driven methods can help concatenate multidimensional signals and complex rail‐surface irregularities but are impressionably uncertain. This study proposes a method in which a deep learning framework is coupled with heterogeneous factors at every link in its ensemble strategy. The instantiation, module foundation, and scenario description establish a concrete system for dealing with the dilemma that an insufficient database hinders feature extraction, causing unproductive capacity. The performance is quantitatively discussed by combining different levels of on‐site rail‐surface conditions, reaching prediction errors of 4.7% and 6.5% and classifier accuracies of 98.4% and 93.7% for irregularities and defect severities, respectively. This work describes a way to extend self‐learner applicability in industry and will facilitate new support for railway track management.

The speed-gene study: methods, study design and preliminary results

The Speed-Gene study aims to identify genetic variants influencing athletic performance and human locomotion using motion capture technology. Currently, 60 female participants have completed the testing protocol, and the overall aim is to recruit 283 moderately trained, healthy Southeast Asian individuals (18–45 y, BMI < 30). Participants will undergo biomechanical analysis and genetic testing. Several analyses will be performed, including (but not limited to) linear and angular kinematic analysis using motion capture technology, force plate dynamometry and genetic analyses to define novel power/torque related outcomes that would be more sensitive to allele-specific differences in athletic performance. Pretesting beverages will be provided, and activity history and current activity levels will be assessed by a questionnaire. The kinematic data will be obtained using a Qualisys Track Manager (QTM) system, and DNA will be extracted from white blood cells. The participants serve as their own controls. Although the Speed-Gene study is tightly controlled, our preliminary findings still indicate considerable individual variability. More participants and further genetic analysis are required to allow the investigation of potential underlying genetic mechanisms responsible for this individual variability.

Design of High Power Density MVDC Cables for Wide-Body All Electric Aircraft

Aircraft electrification yields the next generation of aircraft such as more electric aircraft (MEA) and all electric aircraft (AEA). These aircraft require high-power-density and low-system-mass electric power systems (EPS). To this end, the voltage of the system must be enhanced to reach medium voltage levels in a few kV ranges. However, using medium voltage (MV) EPS for aircraft exacerbates the challenges of designing aircraft cables such as arc and arc tracking, partial discharges (PD), and thermal management. In this paper, several novel multi-layer based insulation systems for MVDC power cables are proposed to resolve aircraft cables’ challenges while maintaining low weight and size. The proposed cables are thermally and electrically analyzed and compared to cable systems designed based on IEC60502 and AS50881 standards. We use a coupled electrical-thermal-fluid flow dynamic model, for simulations where all possible heat transfer approaches, including conduction, convection, and radiation, to transfer mainly the joule heating generated in the core conductor to ambient with a low pressure of 18.8 kPa which is the air pressure at cruising height of wide-body aircraft (12.2 km) are considered and modeled. To compare the proposed cables to the designs based on IEC 60502 and AS50881, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J</i> is introduced, which is the product of cables’ overall mass in a unit length and diameter. The results show that besides benefiting multi-layer, multi-function insulation systems to resolve aircraft cables’ challenges, the overall diameter and weight of the designed cables are lower than single-layer insulation system designs based on AS50881 and IEC60502.

Management of Tracking in Real Time on a Website-based Laundry Information System

Management of Tracking in Real Time on a Website-based Laundry Information System

Graph Attention Networks and Track Management for Multiple Object Tracking

Multiple object tracking (MOT) constitutes a critical research area within the field of computer vision. The creation of robust and efficient systems, which can approximate the mechanisms of human vision, is essential to enhance the efficacy of multiple object-tracking techniques. However, obstacles such as repetitive target appearances and frequent occlusions cause considerable inaccuracies or omissions in detection. Following the updating of these inaccurate observations into the tracklet, the effectiveness of the tracking model, employing appearance features, declines significantly. This paper introduces a novel method of multiple object tracking, employing graph attention networks and track management (GATM). Utilizing a graph attention network, an attention mechanism is employed to capture the relationships of nodes within the graph as well as node-to-node correlations across graphs. This mechanism allows selective focus on the features of advantageous nodes and enhances discriminability between node features, subsequently improving the performance and robustness of multiple object tracking. Simultaneously, we categorize distinct tracklet states and introduce an efficient track management method, which employs varying processing techniques for tracklets in diverse states. This method can manage occluded tracks in crowded scenes and improves tracking accuracy. Experiments conducted on three challenging public datasets (MOT16, MOT17, and MOT20) demonstrate that our method could deliver competitive performance.

Factors influencing decision-making on the implementation of BIM technologies in project management in construction

Subject. This article examines the factors influencing the decision-making to implement BIM technologies in project management in construction. Objectives. The article aims to show that in modern conditions, for the effective implementation of the project, it is important to widely use innovative technologies, namely, BIM technologies, that this technology is not just software for high-quality and fast modeling only, it is necessary to optimize management in the construction industry as a whole. Methods. For the study, I used a content analysis, comparative analysis, and statistics data. Results. The article considers BIM technologies not only as a design tool, but also as a technology that coordinates all departments of the construction organization. The basic principles of BIM technology implementation are also considered. The article shows that BIM technologies help track design, estimate work, organization and management of construction, the construction process and many other related processes. This technology also helps monitor the progress of construction processes in real time and make quick decisions if deviations from the planned indicators of cost, quality and construction time get detected. Conclusions. The article concludes that BIM technologies in companies of various sizes in Russia belong to the future. However, there are many difficulties to overcome in the construction industry and take into account the impact of a number of external and internal factors in the implementation of BIM technologies.

The Development of Track and Field Learning Management Model for High-Performance Athletics in Guangzhou

Background and Aim: In the construction of a sports powerhouse, the government has paid more and more attention to the training of track and field athletics, and learning management is one of the important components of the management of track and field athletics. The deviation of cultural achievements of Universities Track and Field Athletes exposes the inadequacy of the learning management model, optimizing the learning management model is an effective measure to improve the comprehensive quality of track and field athletics. This study was to construct a model of learning management of Universities Track and Field Athletes in Guangzhou. Materials and Methods: This study employs the methods of in-depth interviews, questionnaire surveys, the Delphi method, and mathematical statistics. Based on the POLC theory, this article analyzes the current situation of learning management modes for college track and field athletes in terms of enrollment management, program management, major management, learning process management, assessment and ranking management, and graduation management. To this end, the article summarizes the influencing factors through expert evaluations and constructs a new learning management mode. l. Results: after analysis was found as follows： (1) Planning: implement strict enrollment management, and improve the cultural foundation of track and field athletes from the source. (2) Organization is the main structure for learning and management of college track and field athletes in Guangzhou including five parts: government, schools, academic teachers, coaches, and high-level athletes themselves. (3) Leading has established a governance system that promotes multi-party co-governance, mutual benefit, and mutual promotion establishes a unified national enrollment deadline, and postpones professional exams. And (4) Controlling reforms should be implemented in various aspects such as the recruitment of high-level athletes, professional settings, and training time for track and field athletes. The training time for athletes should not exceed 4 hours per day, ensuring that they have sufficient time for academic studies. Academic performance should be linked to training and qualification for competitions. Conclusion: On the whole, the learning management model of university track and field athletes in Guangzhou presents the characteristics of "wide entry and wide exit", which reduces the admission requirements for cultural achievements to recruit athletes, exchange credits through sports training and competition results, implements incentive measures, and reduces course assessment requirements, etc. Ensuring that they complete cultural studies and can complete the required credits to meet graduation requirements, this "leisure" policy results in their academic level being lower than that of ordinary students.

Cyclic relationship of mechanical likelihood: Coupling perception-action states in extended haptic accuracy

The present study investigates the dynamic nature of haptic accuracy in racket sports, specifically focusing on self-produced movements in participants with different skill levels (novice vs. expert). The study examines performance accuracy using indicators such as absolute error size and the coefficient of restitution as measures of haptic accuracy. To collect and analyze the data, custom-made devices, including shock and vibration sensors and Qualisys Track Manager, were used. The results indicate that skilled participants demonstrated higher accuracy, reflected by smaller absolute error sizes, and exhibited reduced variability in impulse vibration during self-produced movements. Moreover, employing maximum likelihood estimation and differential equations, we reveal cyclic relationships among these mechanical features. These findings provide valuable insights into perception-action coupling within different haptic skill levels, contributing to a comprehensive understanding of expertise in racket sports. By shedding light on the intricate relationship between haptic accuracy and performance, this research offers a valuable framework for studying perception-action coupling in racket sports and can potentially guide future investigations.

Sharing imagery and analysis tools in a simulated submarine control room

We examined the impact of sharing periscope imagery and analysis tools in eighteen five-member teams (Sonar, Periscope, 2xTrack Motion Analysts, Track Manager) who undertook simulated submarine patrol tasks. Compared to a baseline condition, sharing imagery to team members increased perceived individual workload, with no improvement to team performance (tactical picture accuracy). When both imagery and analysis tools were shared, perceived workload increased and tactical picture compilation was more accurate. Despite this improved tactical picture for the imagery and analysis tools condition, there was no advantage to mission completion (rendezvous/close contact detection) or situation awareness. In contrast to the increased subjective workload, individuals in teams provided with shared imagery (with or without tools) had a lower physiological response (heart rate, electrodermal) to task demands compared to the baseline condition. Sharing imagery and analysis tools likely benefited tactical picture compilation by enabling dynamic task redistribution and multiple streams of concurrent data analysis.

Correlation between MOVA3D, a Monocular Movement Analysis System, and Qualisys Track Manager (QTM) during Lower Limb Movements in Healthy Adults: A Preliminary Study.

New technologies based on virtual reality and augmented reality offer promising perspectives in an attempt to increase the assessment of human kinematics. The aim of this work was to develop a markerless 3D motion analysis capture system (MOVA3D) and to test it versus Qualisys Track Manager (QTM). A digital camera was used to capture the data, and proprietary software capable of automatically inferring the joint centers in 3D and performing the angular kinematic calculations of interest was developed for such analysis. In the experiment, 10 subjects (22 to 50 years old), 5 men and 5 women, with a body mass index between 18.5 and 29.9 kg/m2, performed squatting, hip flexion, and abduction movements, and both systems measured the hip abduction/adduction angle and hip flexion/extension, simultaneously. The mean value of the difference between the QTM system and the MOVA3D system for all frames for each joint angle was analyzed with Pearson's correlation coefficient (r). The MOVA3D system reached good (above 0.75) or excellent (above 0.90) correlations in 6 out of 8 variables. The average error remained below 12° in only 20 out of 24 variables analyzed. The MOVA3D system is therefore promising for use in telerehabilitation or other applications where this level of error is acceptable. Future studies should continue to validate the MOVA3D as updated versions of their software are developed.

A Bayesian track management scheme for improved multi‐target tracking and classification in drone surveillance radar

AbstractIn this article, a simple, yet effective, Bayesian scheme for tracks maintenance, promotion, and deletion in drone surveillance radar is presented. It enables the simultaneous tracking of the target body and micro‐Doppler components that originate from the motion of rotors (if any) onboard an unmanned air system. This not only delivers more accurate multi‐target tracking, but also substantially improves the radar automatic target classification capability (e.g. discriminating between drone and non‐drone targets). Challenging and diverse real staring radar datasets are used here to demonstrate the efficacy and benefits of the proposed track management approach.

Development of Records Tracking Management System with QR Code

At Surigao del Norte State University, the process of keeping track of records is done manually by writing or logging document descriptions in the physical logbook. When the office staff received and outgoing the document, they need to log it in the physical logbook. The process relies on a physical logbook to keep track of each document’s movement. The problem here is some of the documents are missing during the process and no one can pinpoint a person since the system is not accurate and reliable. To expedite the implementation process, the researcher opted to utilize the Rapid Application Development (RAD) model for realizing the system. This approach enables the developer to receive ongoing suggestions and feedback while constructing the system, allowing them to make necessary changes to the code as needed. By incorporating this iterative feedback loop, the developer can ensure that the system meets the requirements and expectations of the stakeholders promptly. After implementing the Records Tracking Management System (RTMS) and analyzing the results, the system has performed exceptionally well in fulfilling its purpose of tracking and managing documents. It successfully tracks documents, verifies user authenticity, retrieves files when needed, and keeps a detailed audit trail. The functionalities of the system perfectly align with our organization's goals and effectively cater to the needs of our users. To sum it all up, the evaluators have provided positive feedback, consistently rating the RTMS highly across all quality characteristics. With an average weighted mean of 4.44 out of 5, it is evident that the system has either met or exceeded expectations in terms of functionality, reliability, performance, usability, security, compatibility, maintainability, and portability. These findings provide a strong foundation for designing and developing an efficient and user-friendly Records Tracking Management System that effectively caters to our organization's document management needs.

The effect of two-stage warning system on human performance along with different takeover strategies

Warning stages and takeover strategies are two key factors that affect the takeover process when highly automated systems fail. However, the interaction between these two factors remains unclear. This study empirically investigated the effect of warning types (two-stage warning vs. single-stage warning) and takeover strategies (interruption vs. multitasking) on human performance. Thirty-eight participants performed three tasks of the Multi-Attribute Task Battery (MATB), in which the system monitoring task was automated and adopted as the takeover task, while tracking and resource management tasks were employed as the initial tasks simultaneously. The results showed that compared with the single-stage warning system, the two-stage warning system enhanced situation awareness, reduced mental workload and improved takeover performance. However, tracking task performance declined when the two-stage warning system was used. Moreover, participants using the two-stage warning system reacted to gauge malfunctions much more quickly than participants using the single-stage warning system when the interruption strategy was adopted. However, there was no significant difference in tracking task performance between the two warning systems when the multitasking strategy was adopted. This study concluded that the two-stage warning system can benefit the takeover process but at the cost of the performance of initial tasks, and the takeover strategy plays an important role in realizing the benefits of the two-stage warning system and alleviating its costs. These findings could provide useful insights for automated system design and personnel training.