Drivence: Realistic Driving Sequence Synthesis for Testing Multi-Sensor Fusion Perception Systems

The competitive environment within which organizations operate is evolving faster than ever, prompting organizations to change to keep up with the environmental changes. The dynamic capabilities (DCs) view seeks to answer how organizations attain and sustain competitive advantage in changing environments. In the past two decades, there has been a proliferation of research in the DCs domain; yet much remains to be investigated regarding the antecedents and outcomes of DCs. The term “dynamic marketing capabilities” (DMCs) refers to a subset of DCs with the emphasis on customer value. This research studies four DMCs: proactive market orientation, agility, opportunity creation, and value innovation. The development of DCs may be triggered by the necessity to reconfigure resources in dynamic environments or by managers’ decisions to change the current way of doing things in an attempt to improve performance. Therefore, environmental dynamism and dynamic managerial capabilities are antecedents of DCs. To achieve competitive advantage, organizations can employ two distinct but complementary strategies. First, they can match environmental demands by reconfiguring and renewing their organizational resource base. Second, they can create market turbulence. Therefore, these two strategies act as mediators between DMCs and organizational outcomes. A conceptual framework is developed in order to examine the relationship between DMCs, their antecedents, and outcomes. A sample of 270 usable responses was obtained from top and senior managers working in Australia. After ensuring the constructs’ reliability and validity, the hypotheses were tested by means of hierarchical regression analysis and structural equation modeling. The results indicate that environmental dynamism and dynamic managerial capabilities positively affect the studied DMCs; however, the effect of dynamic managerial capabilities is stronger. Moreover, DMCs have a positive impact on operational marketing capabilities and induced market turbulence; these in turn positively affect organizational outcomes. The multi-mediation tests confirmed that operational marketing capabilities and induced market turbulence mediate the effect of DMCs on organizational outcomes. This study contributes to and extends the knowledge of DCs by proposing an integrative conceptual framework, explaining the antecedents of DMCs and the mechanism by which DMCs are linked to organizational outcomes. The findings re-emphasize the need for DCs in dynamic environments and highlight the critical role of dynamic managerial capabilities. Another contribution is that the study incorporates the element of induced market turbulence, hitherto missing in the literature pertaining to DMCs. This study also emphasizes that operational marketing capabilities mediate the relationship between DMCs and organizational performance. Moreover, four distinct DMCs (i.e., proactive market orientation, agility, opportunity creation, and value innovation) are identified, and scales are developed and validated to measure them. The findings suggest that these DMCs perform as anticipated. This research provides several insights for managers regarding DCs and how to apply them to organizations. The results of this study demonstrate that it is necessary for organizations to invest in developing DMCs, not to achieve performance improvement directly, but to optimize their operational marketing capability configuration. This, in turn, improves organizational outcomes. Moreover, managers are encouraged to invest in the development of DCs as a strategic rather than tactical approach. Another implication is that in ambiguous environments, organizations need to not only adapt to the environment, but also to employ strategies of environment constructing.

The application of unmanned aerial vehicle (UAV)-mounted base stations is emerging as an effective solution to provide wireless communication service for a target region containing some smart objects (SOs) in internet of things (IoT). This paper investigates the efficient deployment problem of multiple UAVs for IoT communication in dynamic environment. We first define a measurement of communication performance of UAV-to-SO in the target region which is regarded as the optimization objective. The state of one SO is active when it needs to transmit or receive the data; otherwise, silent. The switch of two different states is implemented with a certain probability that results in a dynamic communication environment. In the dynamic environment, the active states of SOs cannot be known by UAVs in advance and only neighbouring UAVs can communicate with each other. To overcome these challenges in the deployment, we leverage a game-theoretic learning approach to solve the position-selected problem. This problem is modeled a stochastic game, which is proven that it is an exact potential game and exists the best Nash equilibria (NE). Furthermore, a distributed position optimization algorithm is proposed, which can converge to a pure-strategy NE. Numerical results demonstrate the excellent performance of our proposed algorithm.

The proliferation of unmanned aerial vehicles (UAVs) has driven the emergence of urban air mobility (UAM) as a solution to alleviate the increasing demand for UAVs applications in various domains. The concept of UAM envisions a future where UAVs play a pivotal role in short-distance transportation, delivery services, surveillance, emergency rescue, and more. However, the rapid growth of UAVs poses significant challenges arising from the need to ensure the safe coexistence of UAVs with other manned/unmanned aerial vehicles. To ensure the safe integration of UAVs into low-altitude urban airspace and support high-density on-demand mobility, this research focuses on handling the safety-centric challenges related to UAV collision risk assessment, obstacle avoidance, and conflict resolution in complex urban environments.
\nOne of the main challenges is the intrusion of UAVs into airport runways. Airports serve as critical hubs for civil aircraft takeoff and landing operations. However, the increasing presence of UAVs has raised concerns about intruding into airports, leading to flight delays and temporary runway closures. To quantify the potential interference of UAVs on civil aircraft, a collision-course based trajectory planning model is proposed to evaluate the collision risk between manned civil aircraft and intruding UAVs. In this risk assessment model, the trajectory of the intruding UAV is formulated using a collision-course concept, taking into account the limited trajectory information available about intruding UAVs. The planned trajectory of the UAVs is then used to predict its relative position concerning civil aircraft. To account for the possible disturbances of the relative distance, a stochastic position prediction model based on Brownian motion is developed. The collision risk is assessed based on this stochastic kinematic model, providing a quantitative measure of the potential collisions. The proposed model is validated through extensive simulations involving different UAV initial positions, position updates, and different sizes of collision zone tests, as well as comparative tests with the Monte-Carlo algorithm, demonstrating its robustness in assessing the collision risk of the airport restricted area. To facilitate the application, the model is finally used to develop a platform to generate runway heatmaps, accounting for airport runway risk distributions. 
\nAnother safety-centric challenge is ensuring autonomous obstacle avoidance and conflict resolution of UAVs. To enable UAV autonomous safe operations in uncontrolled urban low-altitude airspace, a comprehensive collision-free path planning framework is developed to address the avoidance of both static obstacles and dynamic threats. Firstly, to handle static obstacles, a novel algorithm of 3D voxel jump point research is introduced to generate a global reference path for UAV navigation to avoid static obstacles, particularly for a free flight at different altitudes. Additionally, to optimize the reference path for specific mission requirements, techniques such as trajectory de-diagonalization, reconstruction, and smoothness are proposed to reduce the risk of static collisions, shorten the fly range, and enable UAVs to negotiate turns more efficiently. Secondly, real-time conflict resolution actions based on the Markov decision process are developed to avoid local dynamic threats encountered along the optimized desired path. This allows the host UAV to dynamically adjust its course to a resolution trajectory while maintaining its planned speed without hovering to avoid the potential collision with the approaching intruders. By avoiding near mid-air collisions (NMAC) and preventing the UAV from hovering, the proposed method ensures the timely completion of UAV missions. Simulation results demonstrate that the developed method successfully achieves UAV autonomous global path planning and local real-time conflict resolution in complex urban low-altitude environments, thereby avoiding both static obstacles and dynamic threats. 
\nMoreover, to bridge the research gap of UAV hard conflict resolution in mitigating NMAC risks arising from local dynamic threats, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. To improve UAV operating environments in urban airspace, a concept of tiered aerial passages is first designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity and optimizes the utilization of urban airspace. For UAV tiered aerial passage operations, a situational NMAC risk assessment module coupled with a conflict resolution decision-making module is formulated, enabling the host UAV to evaluate and respond to real-time conflicts, ensuring avoidance of potential collisions. To assess NMAC risks, a general risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the overtaking, approaching head-on, and converging UAV encounters are analyzed, and situational risk potential scores are created using the Gaussian kernel function in the CRF algorithm, considering advisory, caution, and warning risk alert levels. To resolve conflict, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed scheme is validated by simulating geometric encounter situations and comparative tests with the previous hard resolution technique. The results indicate that the proposed method outperforms UAV NMAC risk assessment and conflict resolution. Even in the most dangerous unacceptable resolution decision-making scenarios across three conflict encounters, the proposed algorithm achieves NMAC rates of 0.036, 0.070, and 0.043, which are lower than the corresponding NMAC rates associated with the hard resolution approach. Together with the acceptable and tolerable scenarios, the proposed negotiation resolution effectively tailors different risk requirements to accommodate diverse UAV missions. 
\nWith a primary focus on ensuring the safety of the UAM architecture, this research endeavors to assess UAV NMAC risks and efficiently manage potential conflicts. The proposed work are thoroughly validated to demonstrate their effectiveness in real-world scenarios. As a result, this study significantly contributes to the establishment of a robust UAM architecture, facilitating the realization of safe and reliable UAM.

Chapter 21 - Event-driven programming-based path planning and navigation of UAVs around a complex urban environment

A timely and complete test is an important factor to assure the functionality and safety of a railway signal system before it is put into service. With the development of rail transportation in China, the traditional semiautomatic test methods cannot satisfy the timely and complete test requirements any longer. This paper proposes an automated model-based test method. First, colored Petri nets are used as a formal language to describe the system specification. Second, the all paths covered optimal algorithm and the sequence priority selected algorithm are proposed to generate the test cases and sequences automatically. Third, taking a typical radio block center (RBC) handover scenario as an example, the generated test cases and sequences are applied into the RBC functionality test platform. The testing result validated the feasibility and efficiency of the proposed automated test method. Compared with the random-walk-based test sequence generation algorithm, the repeatability rate of the generated test sequences is reduced by 46%. The test sequences can cover all the generated test cases, and the cases can cover all the related criteria in the function requirements specification of the Chinese Train Control System Level 3.

Data fusion has been a trend in the field of imaging and signal/image analysis. Although multisensor data fusion is still not regarded as a formal professional discipline, tremendous progress has been made since the publication of the first edition of this book in 1992. With this second edition, the authors have been successful in updating us with state-of-the-art methods and techniques in multisensor data fusion. The book involves both algorithms and software tools, and also covers contemporary subjects like smart agents, cognitive aides, and so on. This book is written according to the Joint Directors of Laboratories (JDL) data fusion group model. There are five levels in the JDL model. The first level deals with association, correlation, estimation, and identification in the data domain. The second and third levels perform knowledge-based processing and utilize expert systems. The fourth level is focused on process monitoring and optimization. The fifth level is devoted to human computer interaction. Chapter 1 serves as an overview. Then, Chapter 2 introduces the JDL model and associated algorithms. Chapters 3–6 cover processing techniques at level one. Chapter 7 gives methods at levels two and three. Chapter 8 targets the control of sensor and information resources at level four. Chapter 9 is for data fusion at level five. Chapter 10 discusses implementation of fusion systems. Chapters 11 and 12 describe emerging applications and information management. The book contains 100 equations, 75 illustrations and key references. The typesetting quality is generally excellent but it would be better if some labels in the figures have been put in larger size. Note that the additons in this book include materials on fusion system control, DARPA's TRIP model, and applications in data warehousing, medical equipment, and defense systems. Hall (associate dean for research, Pennsylvania State University School of Information Sciences and Technology) and McMullen (captain, US Air Force) are well known experts in the field, and should be congratulated for accomplishing such an excellent job in summarizing the up-to-date essential ideas and results on multisensor fusion. Overall, the book is very informative and not difficult to read for electrical and computer engineers as well as technical managers. These types of practitioners can gain solid advice from the book regarding selection of data fusion methods, balance of trade-offs among commercial off-the-shelf tools, development of multisensor data fusion systems and their applications to solve real-world problems. However, to build a sophisticated data fusion system one may need other references for more comprehensive mathematical theories, more rigorous statistical treatments, and more technical details. The cited literature in the book represents appropriate pointers for that purpose. A good example for further reading is the Handbook [1]. As an engineer in biomedical imaging, I would like to underline the relevance of this book to research on systems biomedicine. Given the completion of the Human Genome Project [2] and the momentum of the Physiome Project [3], in 2003 the NIH defined its Roadmap to guide our efforts towards most important biomedical research, including studies on biological building blocks, pathways, and networks using molecular libraries, imaging, bioinformatics and computational biology. Consequently, multi-modality signal sensing and imaging plays a critical role, which often necessarily generates heterogeous sparse/huge datasets that are multi-dimensional, multi-spectral, dynamic, noisy and/or incomplete. The data fusion techniques discussed in this book are closely related to that for biomedical image registration/fusion. Major challenges and new opportunities are enormous ahead of us. In that context, I hope this book will have a significant impact on the biomedical imaging areas, and recommend it to biomedical imaging researchers in particular and other interested engineers in general.

In this paper, we study the cooperative autonomous inspection by Unmanned Aerial Vehicles (UAV) swarms in dynamic and uncertain environments. Facing the challenges of spatio-temporally random target events and constrained onboard resources, we propose a distributed online path planning algorithm based on a Hybrid Artificial Potential Field (HAPF). In contrast to existing works relying on a single probability map, we construct a hybrid artificial potential field to integrate a probability map and an exploration map. The probability map, generated via Kernel Density Estimation from historical and real-time data, learns the distribution pattern of events, while the exploration map encourages coverage of unvisited areas. Virtual attractive forces guide UAVs to prioritize high-probability events and explore unknown regions, whereas virtual repulsive forces ensure collision-free cooperation. Moreover, a Hungarian algorithm based dynamic task allocation method is proposed to prevent resource redundancy. To tackle the well-known local minimum problem in potential field, a sticking release mechanism is designed to escape local optima. Simulation results demonstrate that, in comparison to existing algorithms, our method improving the task completion rate by 20 % and shortening the average completion time by 13 %, offering a practical solution for resource-constrained UAV swarms to achieve efficient and safe autonomous inspection in complex and dynamic environments.

A compact infrared absorption spectrometer is developed and integrated with a light-duty unmanned aerial vehicle (UAV) to provide high-resolution, real-time spatial mapping of species concentrations in dynamic atmospheric environments. The multi-species optical sensor is contained within a 0.5 ft3 package that is mounted to a custom fabricated quadrotor UAV. The on-board optical diagnostics record CO and CO2 concentrations which are transmitted in real-time at a rate of 1.25 Hz to a remote monitoring station. The sensor results are synchronized with the UAV GPS system to generate time-histories and spatial maps. The aerial spectrometer was deployed in a series of prescribed forest burns at the UC Berkeley Blodgett Forest Research Station, where it successfully captured transient plume behavior and generated three-dimensional spatial profiles on 100 m spatial scales and altitudes up to 100 m. The measurements presented here aim to demonstrate the potential for high-resolution, UAV-borne optical sensors for atmospheric monitoring in harsh, dynamic environments, while serving as a guide for future technological improvements and applications.

Unmanned Aerial Vehicles (UAVs) have become essential tools across critical domains, including infrastructure inspection, public safety monitoring, traffic surveillance, environmental sensing, and target tracking, owing to their ability to collect high-resolution spatial data rapidly. However, maintaining stable and accurate flight trajectories remains a significant challenge, particularly during autonomous missions in dynamic or uncertain environments. This study presents a novel flight path prediction framework based on Gated Recurrent Units (GRUs), designed for both single-step and multi-step-ahead forecasting of four-dimensional UAV coordinates, Easting (X), Northing (Y), Altitude (Z), and Time (T), using historical sensor flight data. Model performance was systematically validated against traditional Recurrent Neural Network architectures. On unseen test data, the GRU model demonstrated enhanced predictive accuracy in single-step prediction, achieving a MAE of 0.0036, Root Mean Square Error (RMSE) of 0.0054, and a (R2) of 0.9923. Crucially, in multi-step-ahead forecasting designed to simulate real-world challenges such as GPS outages, the GRU model maintained exceptional stability and low error, confirming its resilience to error accumulation. The findings establish that the GRU-based model is a highly accurate, computationally efficient, and reliable solution for UAV trajectory forecasting. This framework enhances autonomous navigation and directly supports the data integrity required for high-fidelity photogrammetric mapping, ensuring reliable site assessment in complex and dynamic environments.

High-precision relative attitude measurement is a critical prerequisite for autonomous recovery of micro unmanned aerial vehicles (UAVs) in GNSS-restricted and dynamic environments. However, motion blur and drastic scale changes often lead to a surge in visual observation noise, causing significant drift in state estimation. To address this, this paper proposes a holistic visual metrology system that deeply fuses enhanced perception with uncertainty suppression. First, at the perception layer, an improved YOLO11n-Landing network serves as a high-fidelity instrument, utilizing multi-scale feature enhancement and anti-blur attention mechanisms to boost detection confidence in dynamic scenes. Second, a height-constrained Kalman filter was constructed at the fusion layer, explicitly managing uncertainty propagation through a physical covariance model. The validity of the optimal fusion interval was confirmed based on parameter sensitivity analysis. Crucially, at the system level, a perception-control coupled isolation mechanism has been introduced. Unlike traditional loosely coupled architectures, this mechanism uses the visual confidence factor as an information hub, synchronously mapping it to the filter covariance, switching gain K , and boundary layer thickness. This dynamically reconstructs the system’s response bandwidth, actively isolating invalid observations, and physically blocking the propagation of measurement outliers into the control loop. Experimental results demonstrate that compared to traditional benchmark methods, this integrated system significantly converges the root mean square error to 0.156 m, reduces the error variance by approximately 74.7%, and achieves a 99.0% landing success rate in Monte Carlo simulations. This work not only provides a highly system-integrity-preserving technical solution for robust autonomous landing of micro UAVs, but its core coupling paradigm also demonstrates broad prospects for generalization to high-risk tasks such as aerial docking and close-range inspection.

This thesis focuses on the use of multiple unmanned aerial vehicles(UAVs) in a distributed framework to synchronously perform shared tasks. These include aerial beamforming and coordinated mapping. It also focuses on use of multiple UAVs with the overall goal of enhancing the reliability of performing periodic (mapping) tasks at remote locations for long-term autonomous(LTA) missions. In the first part of the thesis, we focus on hardware architecture for UAVs to enable reliable LTA missions with minimal human intervention. We developed a Size, Weight, and Power(SWaP) constrained smart charging stack to minimize hotel loads seen during the recharging process and enable efficient charging of batteries. This leads to the design of a standalone, solar rechargeable quadcopter. Real-world applications such as reconstructing a dynamic scene from multiple viewpoints and distributed aerial beamforming require multiple robots(agents) to coordinate and synchronously act to accomplish shared tasks. These tasks require spatially distant, multiple UAVs to have time, phase, and frequency synchronization. We demonstrate a Synchronous UAV(S-UAV) architecture for wireless synchronization based on GPS disciplined oscillators and the associated software framework needed for temporal registration of data across multiple UAVs. We have built four S-UAVs and demonstrate the ability to 3D reconstruct a dynamic scene from overlapping viewpoints. Dynamic baseline camera arrays formed using multiple S-UAVs are used to synchronously capture a dynamic environment with people moving around. A single-time instance of synchronously captured images of the scene is used to 3D reconstruct the dynamic environment while preserving static scene assumptions of Structure from Motion(SFM). In the second part of the thesis, we focus on a multi UAV autonomy framework for real-world applications of UAVs in perception, wireless communications, and reliable LTA missions. We present 'Simplenav,' a navigation stack for heterogeneous, multiple UAVs. We demonstrate this framework with novel applications of end-to-end autonomy pipelines developed for a coordinated swarm of UAVs.--Author's abstract

Traditional bulky and complex control devices such as remote control and ground station cannot meet the requirement of fast and flexible control of unmanned aerial vehicles (UAVs) in complex environments. Therefore, a data glove based on multi-sensor fusion is designed in this paper. In order to achieve the goal of gesture control of UAVs, the method can accurately recognize various gestures and convert them into corresponding UAV control commands. First, the wireless data glove fuses flexible fiber optic sensors and inertial sensors to construct a gesture dataset. Then, the trained neural network model is deployed to the STM32 microcontroller-based data glove for real-time gesture recognition, in which the convolutional neural network-Attention mechanism (CNN-Attention) network is used for static gesture recognition, and the convolutional neural network-bidirectional long and short-term memory (CNN-Bi-LSTM) network is used for dynamic gesture recognition. Finally, the gestures are converted into control commands and sent to the vehicle terminal to control the UAV. Through the UAV simulation test on the simulation platform, the average recognition accuracy of 32 static gestures reaches 99.7%, and the average recognition accuracy of 13 dynamic gestures reaches 99.9%, which indicates that the system’s gesture recognition effect is perfect. The task test in the scene constructed in the real environment shows that the UAV can respond to the gestures quickly, and the method proposed in this paper can realize the real-time stable control of the UAV on the terminal side.

Task allocation for Unmanned Aerial Vehicle (UAV) swarms is a complex challenge. Effective distribution of tasks among multiple UAVs necessitates the optimization of conflicting objectives, such as task balance, flight distance, cost, and target benefits. This multi-objective optimization problem is inherently intricate due to strong interdependencies, nonlinear relationships, and dynamic environments. Traditional methods, including heuristic algorithms, supervised machine learning, and basic reinforcement learning mechanisms, often suffer from low efficiency and strict requirements for data sources or supervision mechanisms. They also struggle to adapt to problems with large discrete action spaces. To address this challenge, this paper proposes a novel approach that integrates swarm intelligence simulation with reinforcement learning. The core of this approach is a reinforcement learning algorithm based on the Deep Deterministic Policy Gradient (DDPG) framework. A key innovation is a customized DDPG variant specifically designed to handle the large, discrete action spaces inherent in UAV task allocation. Additionally, a behavior tree model is employed to simulate UAV swarm interactions, providing a realistic and verifiable environment for evaluating task allocation strategies. This simulation generates a rich dataset for training and testing reinforcement learning algorithms. The combined approach efficiently addresses the multi-objective optimization problem, enabling optimal task distribution for numerous UAVs. Compared to existing heuristic methods, the proposed approach demonstrates superior computational efficiency and solution quality, while maintaining reasonable convergence stability and environmental adaptability. Simulation-based validation ensures the approach’s robustness in dynamic environments, a critical factor for practical applications. Overall, the integration of simulation and reinforcement learning provides a powerful framework for tackling the complex task allocation problem in UAV swarms.

The aim of this study is to investigate the role of environmental dynamism in relation to dynamic capabilities and competitive advantage. This study uses a quantitative approach and type of explanatory research. The survey was conducted at 130 MSMEs in Malang Raya. Hypothesis testing using SEM-PLS. The findings of the study prove that environmental dynamism is a driver for dynamic capability, while providing evidence that environmental dynamism acts as a negative moderator for achieving competitive advantage. Another finding is that dynamic capabilities have a major effect on competitive advantage while simultaneously acting as fully mediating environmental dynamism with competitive advantage. The implication is important for MSMEs to develop dynamic capabilities in order to deal with environmental dynamism so that MSMEs can increase competitive advantage because if they do not develop dynamic capabilities, environmental dynamism will reduce MSME's competitive advantage.

Purpose Through the theory of dynamic capability, this study aims to make theoretical hypotheses and empirical explorations on the relationship between supply chain quality management (SCQM) practices, dynamic supply chain capability (DSCC), supply chain resilience (SCR) and environmental dynamics. Design/methodology/approach In this study, DSCC is used as a mediating variable in the relationship between SCQM practices and SCR. To enhance the resilience of the supply chain, the moderating effect of environmental dynamics was tested. About 426 completed questionnaires were obtained from a cohort of Chinese manufacturing enterprises, and the proposed hypotheses were tested using the structural equation model method. Findings Empirical evidence shows that SCQM practices have a significantly positive impact on SCR at the overall level. Achieving better DSCC plays an important mediating role between SCQM practices and resilience. In addition, environmental dynamics have a moderating effect on the relationship between SCQM practices and DSCC. Originality/value This study constructs the influencing mechanism of SCQM practices on SCR through dynamic capability theory and enriches the relevant literature on SCR research. This study also provides management guidance for firms to enhance SCR through SCQM practices in an uncertain environment.