Search Results (166)

Level 3 automated driving necessitates rapid driver reengagement following a takeover request, making human–machine interface design critical for safety. As a sensor-based in-vehicle interface, the augmented reality head-up display (AR-HUD) integrates real-time environmental sensing with visual information delivery, yet how different information presentation strategies influence driver visual attention during takeover remains underexplored. We examined the effects of presentation form (static/dynamic) and presentation timing (concurrent/progressive) using a 2 × 2 within-subject design. In the experiment, twenty-one licensed drivers viewed prerecorded automated driving takeover scenarios. Eye tracking measured mean fixation duration, mean saccade amplitude, time to first fixation, and fixation count, while ratings assessed usability, acceptance, and intention comprehension. Progressive presentation significantly reduced all eye-tracking measures and improved perceived usability ratings compared with concurrent presentation. This pattern indicates more concentrated and orderly gaze allocation and is consistent with lower visual-search and information-integration demands. However, the lower fixation count may partly reflect reduced information exposure. Static presentation reduced mean fixation duration but showed no consistent overall advantage on the remaining measures. Significant form-by-timing interactions showed that progressive presentation reduced saccade amplitude and time to first fixation and improved intention comprehension under static, but not dynamic, presentation. Among the four combinations, static-progressive presentation yielded the most favorable overall pattern. Overall, presentation timing affected more measured outcomes than presentation form. These findings provide preliminary evidence that presentation form and presentation timing shape gaze behavior and subjective evaluations of AR-HUD takeover displays. Full article

Highly automated vehicles often rely on takeover requests (TORs) that lack contextual transparency, treat drivers as passive fallbacks, and lead to poor situational awareness. To address this challenge, this study presents the Empowering Language Interaction (ELI) framework, a conversational interface powered by a large language model that supports bidirectional negotiation and collaborative human–AI teamwork. Using the CARLA driving simulator, 28 participants compared ELI with a conventional TOR baseline in both urban and peri-urban driving scenarios. The study employed a multidimensional evaluation approach, integrating telemetry data on driving performance with continuous monitoring of physiological indicators (electrocardiogram and electrodermal activity) and subjective questionnaires to assess driver trust and engagement. Results indicated that ELI sustained continuous driver engagement and improved the subjective comprehension of the vehicle’s state. Physiologically, the conversational interface maintained active cognitive load, preventing the abrupt autonomic spikes characteristic of traditional takeover requests. Furthermore, ELI outperformed the TOR baseline in safety metrics by reducing out-of-lane events and maintaining greater safety margins. Conversational interaction has shown potential to transform drivers from passive supervisors into active teammates, improving joint decision-making without inducing over-reliance on the automated system. Full article

In level-3 automated driving, control shifts between the system and the human driver, making timely and effective communication crucial in conflict situations. To explore how different communication modalities affect the driver’s understanding, trust, and performance in such situations, we conducted two complementary studies: a focus group and an interactive user study. The focus group revealed a preference for multimodal, tailored feedback, with visual information most frequently favored. The interactive user study tested these findings in practice by asking participants to confirm the system suggestion or take over. The results from the interactive study showed the importance of a pre-explanation signal that draws attention to the situation, and a clear visual marking of the proposed resolution. While most participants stated a preference for speech-augmented feedback, spoken and written explanations were frequently overlooked or reported as distracting during active conflict scenarios, suggesting a gap between stated preference and in-task utility. These results indicate that in urgent conflict situations, concise visual cues are more effective than detailed verbal explanations, offering guidance for the design of future level-3 vehicle interfaces. Full article

This study examines the relatively underexplored phenomenon of hostile takeovers within the insurance industry, a sector that played a significant role in the events surrounding the 2008 global financial crisis. Using a sample of 754 insurance merger and acquisition (M&A) transactions completed between 2008 and 2021, we investigate the determinants of hostile takeover activity. The dataset comprises acquiring and target firms from 46 countries and special administrative regions (SARs), providing a broad international perspective on post-crisis insurance-sector M&A dynamics. The findings reveal that several transaction- and firm-specific factors significantly affect the likelihood of hostile takeovers. In particular, a target firm’s prior M&A experience, increases in target book value, higher bidder research and development expenditures, bid revisions, and acquisition premia are positively associated with takeover hostility. While descriptive analyses document notable variation across jurisdictions, the primary empirical evidence is derived from pooled regression models incorporating country fixed effects. The results contribute to the literature on insurance-sector consolidation by identifying industry-specific factors associated with hostile acquisition activity and enhancing understanding of how information asymmetries, strategic considerations, and governance mechanisms shape takeover outcomes. These findings offer valuable implications for corporate managers, investors, and policymakers operating within a globally interconnected and highly regulated insurance industry. Full article

Human–machine interaction (HMI) has become a central issue in autonomous driving because partial automation can degrade, rather than improve, human performance during takeover, handover, and out-of-the-loop transitions. Similar interaction risks are emerging in manned–unmanned vehicle teaming (MUM-T), where operators must supervise multiple autonomous or remotely controlled assets under higher mission complexity and safety-critical constraints. However, existing effectiveness analyses of unmanned and MUM-T systems often treat the level of autonomy (LOA) as a fixed system attribute or assume the highest autonomy level, thereby obscuring the human–automation bottlenecks that arise during partial autonomy. This study proposes a function-specific HMI effectiveness framework in which autonomy is represented as a vector across surveillance, maneuver, fire or neutralization, command and control, and human–machine teaming functions. Measures of performance (MOPs) are modeled as conditional performances jointly shaped by function-specific LOA, operational environment, and intrinsic system capability, and are propagated through a five-layer LOA–MOP–MOE structure to a mission-level measure of effectiveness (MOE). The framework is demonstrated using a notional mine countermeasure scenario in which manned minehunters cooperate with unmanned underwater and surface vehicles. Four autonomy progression stages, from manned-centric operation to advanced cooperative autonomy, are evaluated for timely route opening. The case illustrates a non-monotonic partial-autonomy trap, or LOA-2 valley: remotely controlled unmanned assets may temporarily reduce mission effectiveness when teleoperation workload and HMI bottlenecks outweigh equipment gains, before cooperative and supervisory autonomy restore and exceed baseline performance. The contribution of this study lies not in the notional numerical results but in providing an explicit diagnostic structure for identifying where and why function-specific autonomy, control sharing, and HMI bottlenecks shape mission effectiveness. The framework thereby extends human–machine interaction analysis from autonomous driving to the broader, higher-risk setting of manned–unmanned vehicle teaming. Full article

To improve autonomous path execution of a wheeled mower in standardized orchards, an ROS-based navigation control system integrating dual-antenna RTK-GNSS positioning and heading, an industrial computer, CAN-based drive control, and wireless manual takeover was developed. Parameterized reference paths consisting of straight sections and U-shaped turns were generated for inter-row operation, and an adaptive Pure Pursuit controller was implemented within the ROS framework. Three repeated field trials were conducted on a U-shaped path at a nominal speed of 0.8 m/s, with conventional Pure Pursuit using a fixed 2.0 m look-ahead distance as a reference. The mower completed the planned path under both controller settings. For the adaptive controller, the mean absolute lateral errors in the curved, straight, and overall sections were 0.124, 0.035, and 0.047 m, respectively. The corresponding overall error for PP-2m was 0.115 m. These results support the feasibility of the developed system for RTK-GNSS-guided closed-loop path tracking under the tested standardized-orchard conditions. Full article

Autonomous driving systems relieve drivers from continuous vehicle operation and constant monitoring, allowing them to engage in non-driving-related tasks (NDRTs). However, immersion in such tasks can impair drivers’ perception of both the takeover situation and the surrounding environment. To quantitatively assess drivers’ risk perception capability during takeover, a driving simulation platform was used to design autonomous takeover scenarios involving three types of NDRTs, three takeover request times (TOR), and two obstacle avoidance conditions. A total of forty participants were recruited to complete the driving experiment. Drivers’ eye movement data were collected, and visual metrics—including fixation, saccade, and pupil diameter—were extracted by defining areas of interest (AOIs). A subjective risk perception scale was developed and administered to measure drivers’ subjective evaluations. Together with takeover reaction time, the K-means clustering method was applied to classify drivers’ risk perception levels into three categories: high, medium, and low. The LightGBM algorithm was selected to construct a baseline classification model for assessing drivers’ risk perception levels. Subsequently, the Whale Optimization Algorithm (WOA) was employed to optimize the hyperparameters of LightGBM, resulting in the WOA-LightGBM model. This optimized model demonstrated improved recall, accuracy, precision, and F1-score, reaching 0.9210, 0.9253, 0.9261, and 0.9201, respectively. Furthermore, SHapley Additive exPlanations (SHAP) analysis was conducted to quantify the contribution of eye movement indicators to risk perception assessment. The results revealed that saccade duration in the NDRT areas significantly reduced drivers’ risk perception levels (SHAP value = −0.71), whereas increased saccade duration in the forward road area effectively restored drivers’ risk perception capability (SHAP value = 0.71). In addition, higher risk perception levels were found to enhance drivers’ takeover performance in terms of vehicle control. These findings provide valuable insights for the management of NDRTs and the optimization of autonomous vehicle takeover systems. Full article

With the development of space-based cloud computing and on-orbit intelligent processing, higher requirements have been imposed on standardized network interconnection for spaceborne platforms. However, constrained by size, power consumption, thermal design, and structural layout, some spaceborne platforms lack physical Ethernet interfaces and therefore cannot directly support standard Internet Protocol version 6 (IPv6) communications. In addition, harsh spaceborne operating conditions, including thermal-vacuum stress and potential radiation-induced disturbances, increase the risk of link anomalies, state inconsistency, and service interruption. To address these issues, this paper proposes a reliability-enhanced IPv6 access and transmission method for spaceborne platforms without physical Ethernet interfaces. On the processor side, a network TAP interface is established to reconstruct the semantics of a standard Layer-2 network device. Combined with a cooperative central processing unit–field-programmable gate array (CPU–FPGA) link-carrying mechanism, the proposed method enables transparent IPv6 access without modifying the native Linux protocol stack. To satisfy both standard spacecraft onboard network services and high-throughput engineering data transmission, a dual-channel architecture is designed, in which the service network channel is separated from the engineering data channel. In addition, a hierarchical reliability-oriented mechanism is constructed, consisting of hardware-level fault-tolerance design, reliable link interaction, status monitoring, and redundancy takeover. Experimental validation is conducted on a CPU-FPGA prototype platform under a thermal-vacuum environment and representative abnormal operating scenarios. The results show that the proposed method can stably support IPv6 address configuration, neighbor discovery, and end-to-end communication. Under zero-packet-loss conditions, the service network channel achieves an average stable throughput of 173.8 Mb/s, while the engineering data channel achieves a stable throughput of approximately 3.4 Gb/s. The system also demonstrates good service continuity during long-duration operation and under typical abnormal scenarios. The proposed method provides a verifiable system-level solution for realizing standardized IPv6 network access and reliability-enhanced data transmission on interface-constrained spaceborne platforms. Full article

The increasing adoption of cloud computing has expanded organizational attack surfaces and created additional opportunities for identity abuse, ransomware operations, data exposure, and configuration-related security incidents. Conventional monitoring environments based primarily on static rules, fragmented telemetry, and manual triage often struggle to prioritize high-severity incidents in real time. This study evaluates the operational impact of an integrated AI-augmented cloud-native SIEM/XDR/SOAR architecture for cloud threat detection and automated incident response. A sequential mixed-methods comparative case study was conducted across two enterprise-style security environments: an AI-augmented architecture combining cloud-native SIEM, XDR telemetry unification, behavioral analytics, AI-assisted correlation, generative-AI analyst support, and SOAR automation, and a conventional baseline environment based on manual triage and signature-based controls. Three attack scenarios were analyzed: phishing-led account takeover, multi-stage ransomware, and shadow-IT data exfiltration. The AI-augmented architecture reduced mean time to triage from 17.4 h in the conventional baseline to 10.7 min and enabled ransomware containment in under five minutes through pre-configured automated response playbooks. The results also showed improved prioritization of high-severity incidents, reduced analyst review burden, and a high automated closure rate. The findings provide operational evidence for the evaluated security architecture. Limitations include single-vendor dependency, non-equivalent false-positive classification mechanisms, proprietary model internals, calibration requirements, and detection gaps involving legitimate third-party services and password-protected content. Full article

Real-time kinematic (RTK) navigation, which enables centimeter-level positioning accuracy through carrier-phase differential correction, provides high-accuracy positioning for orchard robots, but short-term outages caused by canopy occlusion and signal interference may interrupt path guidance and increase lateral drift. To address this issue, this study proposes a vision-based takeover navigation method for orchard robots under short-term RTK failure conditions. First, an improved YOLOv11-based road segmentation and completion model, termed YOLOv11-VF, was developed. By introducing a Squeeze-and-Excitation (SE) channel attention mechanism, the model jointly perceives visible road regions and occluded road completion regions, thereby producing continuous and complete road semantic representations. Second, a structured geometric road representation was constructed from the segmentation results to extract the navigation reference line, and a joint direction-position constraint mechanism was established by integrating the reference line with the robot reference view axis. A hierarchical constraint strategy based on a travel corridor and a deadband region was further designed to jointly determine heading deviation and lateral drift. Finally, road segmentation, navigation-line extraction, parameter analysis, and vision-based takeover experiments were conducted in a standardized orchard environment. The results showed that YOLOv11-VF achieved Precision, Recall, AP₅₀, mAP_@0.5:0.95, and F1 values of 92.31%, 88.56%, 94.40%, 67.41%, and 90.40, respectively, showing the best overall segmentation performance among all compared models while maintaining good real-time performance. The proposed method also demonstrated high consistency in navigation-line extraction and maintained mean absolute deviations of 0.0176 ± 0.0041 m to 0.0718 ± 0.0138 m during RTK outage intervals over 10 repeated trials, indicating good path-following capability and operational stability. Full article

Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty. This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery. Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery. The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty. Full article

Under weak-grid conditions, grid-following (GFL) control of doubly fed induction generators (DFIGs) suffers from reduced stability margins, deteriorated dynamic performance, and intensified oscillations near the stability boundary. To address these issues, a pre-synchronized switching strategy between GFL and grid-forming (GFM) modes, triggered by locally measured operating variables, is proposed. Based on the GFL control model, the evolution of system dynamics with decreasing short-circuit ratio is analyzed, thereby elucidating how reduced grid strength progressively weakens robustness and disturbance rejection and eventually leads to instability. To characterize this deterioration, a set of normalized indices is constructed to quantify the oscillation levels of active power, phase-locked loop frequency, and point of common coupling voltage, enabling reliable identification of control-performance deterioration. A pre-synchronization scheme based on a virtual power closed loop is then developed, allowing the target mode to converge to the current operating point prior to takeover and enabling smooth bidirectional switching between GFL and GFM modes. Hardware-in-the-loop results demonstrate that the proposed strategy accurately detects GFL performance deterioration and effectively suppresses boundary oscillations while mitigating switching transients, thereby enhancing the adaptability of DFIGs to variations in grid strength. Full article

The transition to automated vehicles (AVs) introduces complex human factors and system-level challenges across Society of Automotive Engineers (SAE) Levels 0–5, with profound implications for the long-term viability of future transport infrastructure. Drawing on a synthesis of socio-technical, cognitive, and behavioural adaptation theories, this study develops an integrated framework to analyse the evolving relationships among driving automation, human behaviour, system risks, and urban sustainability. The findings demonstrate that human-factor risks are inherently nonlinear, meaning they do not decrease proportionally as technology advances; instead, risk profiles peak significantly at intermediate automation levels (SAE 2–3) due to supervisory fatigue and delayed takeovers, introducing severe traffic flow volatility and localised micro-congestion that directly compromise the environmental efficiency of sustainable transport systems. As these risks reconfigure into institutional and digital infrastructure dependencies at higher levels (SAE 4–5), the primary constraint shifts toward network readiness. Through an analysis of real-world AV deployment case studies and a structured narrative literature review, this paper identifies critical operational discontinuities and mixed-traffic complexities that threaten urban grid resilience. This study proposes a conceptual framework that translates these cross-level socio-technical insights into actionable deployment pathways, providing policymakers with adaptive governance models, transportation planners with mixed-traffic management strategies aimed at preserving network efficiency, infrastructure agencies with physical and digital readiness criteria for long-term asset sustainability, and AV developers with human–machine interface optimisation frameworks to secure human-centric safety within sustainable smart city networks. Full article

The biofuel supply chain is a complex value chain spanning from production to consumption. Manipulating information such as geographical origin, raw material type, and quantity at the production stage can disrupt refinery production plans and cause supply–demand imbalances. Therefore, a transparent traceability system is essential. The existing centralized database architecture poses a high risk of supply chain service suspension due to even a temporary fault in the central server, and it lacks resilience. Furthermore, it is vulnerable to data forgery, making it urgent to secure information integrity. To resolve these issues, this study proposes a blockchain-based biofuel supply chain information protection system. This system utilizes Shamir’s Secret Sharing algorithm to distribute data location information across all nodes and introduces the R-snowball consensus algorithm, which combines the reputation score of nodes with the random sampling of Snowball. The system aims to secure resilience in the event of a failure, achieve reputation-based security, and provide preliminary evidence of robustness against internal and external threats under the tested conditions. Experimental results demonstrated that the proposed system achieved an average recovery time of within 0.03 s, regardless of the load volume. Furthermore, preliminary evidence under the tested conditions suggests that the security and robustness of the system were supported through the exclusion of internal malicious nodes via a reputation-based penalty logic, the defense against main chain takeover attempts in external attack scenarios involving multiple fake nodes (Sybil nodes), and the maintenance of consistent consensus times. Full article

Takeover safety remains a critical human-factors issue in conditionally automated driving because drivers must resume manual control under limited time and varying traffic conditions. This study examined how auditory alert urgency influences takeover safety under different weather and scenario conditions using a controlled driving-simulator experiment. Forty female licensed drivers completed a 3 × 2 × 2 within-subject design in OpenDS v.4.5 4.5, with three alert urgency levels, two weather conditions, and two representative takeover scenarios. Driving behavior and safety indicators were treated as the primary outcomes, while limited supplementary measures were retained as supporting evidence. Across the matched takeover tasks, alert urgency and scenario condition were associated with differences in takeover-related behavior and safety outcomes, whereas weather effects were more evident in subjective difficulty appraisal than in the main objective indicators. High-urgency alerts were perceived as the most urgent and the most helpful, whereas medium-urgency alerts showed the highest overall acceptability. Rainy weather and cut-in scenarios were consistently perceived as more demanding. Limited supplementary evidence provided selective support for interpreting the observed primary outcome patterns. These findings provide controlled simulator-based evidence for context-sensitive auditory warning design and takeover-support evaluation under combined environmental and scenario demands. Full article