Search Results (28)

Autonomous driving systems (ADSs) are reliable only when heterogeneous sensors, estimation algorithms, and safety mechanisms are engineered as a single coherent safety-critical measurement system rather than as loosely coupled modules. Production stacks integrate cameras, LiDAR, automotive radar, and GNSS/IMU, yet deployment remains constrained by modality-specific failure modes, calibration and synchronization drift, and out-of-distribution (OOD) conditions that violate modeling assumptions. These limitations induce overconfidence and downstream decision errors whenever planning assumes certainty sharper than sensing can justify. This survey introduces a sensor-centric framework linking measurement physics, uncertainty propagation, fusion integrity, safety assurance, and risk-aware planning and control. We formalize what each modality physically measures; unify probabilistic, evidential, and conformal uncertainty representations; analyze filtering, factor-graph, BEV, transformer, and state-space fusion architectures with an emphasis on robustness and graceful degradation; and generalize aviation-style integrity concepts (RAIM/ARAIM) to multi-modal autonomy. The distinctive contribution is a single sensor-to-assurance throughline in which every uncertainty representation is tied to its measurement physics, every fusion architecture is evaluated against an explicit integrity-monitoring requirement generalized from RAIM/ARAIM, and every safety-standard clause is mapped to a concrete architectural mechanism. We map these mechanisms onto ISO 26262, ISO 21448 (SOTIF), ISO/PAS 8800, ANSI/UL 4600, and the UNECE framework, and connect perception uncertainty to decision-making through chance-constrained MPC and formal safety filters (RSS, CBF). Industry case studies and emerging V2X and generative-simulation approaches close the loop to deployable safety arguments. Full article

This paper proposes a conceptual framework for strengthening school quality assurance through knowledge management to support sustainable education. Drawing on the international priorities of the OECD and UNESCO, the study positions school self-evaluation as a central quality-assurance mechanism that can promote continuous improvement, accountability, equity, and better learning outcomes. Methodologically, the study adopts a quantitative research design to collect data from 978 teachers across 20 schools in Hong Kong. Exploratory factor analysis and structural equation modelling were employed to identify the latent variables and validate the conceptual framework. Results show that effective quality assurance depends on both formal procedures and the school’s capacity to create, share, and use knowledge. Key knowledge management enablers include visionary leadership, professional autonomy, bureaucratic control, information technology infrastructure, and a collaborative culture of knowledge sharing. Within this model, professional autonomy and knowledge sharing link management conditions to evidence-informed reflection, planning, and improvement. The framework is situated in the context of Hong Kong schools while offering broader relevance for education systems seeking sustainable development. The study concludes that sustainable school self-evaluation is driven primarily by teacher professional autonomy (β = 0.738, total effect = 0.795), with knowledge sharing functioning as a critical mediating mechanism that transmits the effects of visionary leadership (indirect β = 0.343) and enabling bureaucratic control (indirect β = 0.103) into evaluation quality. IT infrastructure does not exert a significant direct effect on SSE (β = 0.056, p = 0.098), indicating that technological provision is a necessary but insufficient condition for evaluation effectiveness in the Hong Kong context. Full article

Anthropic’s April 2026 Claude Mythos Preview release established a new operational threat category: frontier AI systems whose extended-context reasoning, recursive self-correction, native system-tool integration, and agentic scaffolding render dominant AI safety paradigms—RLHF, output filtering, contractual access vetting, human-in-the-loop supervision—insufficient as sole controls. This paper develops a defense-in-depth reference architecture against that category, structured around four named contributions: a five-indicator operational definition of the Mythos-class (capability conjoined with scaffold, access pattern, autonomy depth, and persistence); the Mythos-Class Posture Rubric (MCPR), a three-tier detection framework spanning evaluation, deployment, and runtime with explicit routing to mitigation layers; a four-layer mitigation stack comprising the Vetted-Access Operational Pattern (VAOP), Authority-Bound Output Release (ABOR) cryptographically grounded in FIPS 203/204/205 post-quantum primitives, and the Compute-Plane Isolation Profile (CPIP); and an integrated architecture that crosswalks to the NIST AI Risk Management Framework, NIST Cybersecurity Framework 2.0, and CISA Zero Trust Maturity Model 2.0. The architecture is applied to three deployment surfaces—post-quantum cryptography migration, federal AI supply-chain assurance, and critical-infrastructure operational technology defense—demonstrating that the four contributions generalize across heterogeneous operational contexts. The contribution is a reference design rather than a deployed system; limitations, falsifiability criteria, and a research agenda for empirical refinement are developed. Full article

Vision–Language–Action (VLA) policies promise flexible long-horizon manipulation, but deployment under domain shift requires both reliable uncertainty estimates and a workable runtime-assurance policy. We study a model-agnostic uncertainty-calibrated safety-gating wrapper that estimates online failure risk and routes control among policy execution, pause-and-reobserve, and a fallback planner. Using a cleaned and consistently aggregated benchmark pipeline, we evaluate two long-horizon manipulation tasks in NVIDIA Isaac Sim 5.0 under lighting, texture, occlusion, sensor, and combined shifts. Relative to an ungated VLA baseline, calibrated gating improves mean shifted success from 57.5% to 77.2% and reduces aggregate expected calibration error from 0.303 to 0.100. The largest success gains occur under occlusion and combined shift, including improvements from 48.3% to 85.2% on the drawer task and from 59.4% to 87.8% on clutter sort. The results also expose a systems trade-off: an aggressive uncalibrated threshold baseline attains stronger raw success and collision metrics, but requires nearly twice as many interventions per shifted episode (21.6 vs. 11.5). The main contribution is, therefore, an empirical characterization of the reliability–intervention trade-off created by calibrated supervision, not a claim that the calibrated supervisor is universally the best terminal controller. We frame calibrated gating as a better-calibrated, lower-intervention supervisor that materially improves robustness relative to an ungated VLA while revealing the open problem of mapping calibrated risk into efficient intervention policies. Additional threshold-sensitivity, signal-diagnostic, overhead, and residual-failure analyses show that the selected operating point is meaningful but not universal: the calibrated risk threshold captures most shifted failures in retrospective logs, yet residual contacts still arise during pause and fallback states. These findings provide controlled simulation evidence for trustworthy VLA supervision under distribution shift and clarify the reliability–intervention frontier that future embodied-control systems must navigate. Full article

Supervising tactile-first robotic traversal in confined, uncertain spaces poses a challenge: operators must be able to intervene without continuous micromanagement. We present a human–machine interface (HMI) that blends operator commands with safety-constrained autonomy and surfaces risk through synthesized predictive haptic alerts. Using offline, log-driven replay of 660 trials, we counterfactually evaluate this HMI without new user studies. Results show consistent improvements: predicted collisions decrease, minimum clearance increases, traversal time and path length improve, and the traversability certificate margin rises. Operator–autonomy disagreement is reduced, with smoother control and fewer heading reversals, particularly under algorithms M2 and M3. Importantly, the synthesized haptic alerts anticipate safety-critical events with positive lead time, achieving high precision and recall as objective measures of informativeness. Together, these findings indicate that shared-control blending with tactile-first autonomy can enhance safety, efficiency, and assurance while reducing conflict between operator intent and autonomy. Contributions include the method (counterfactual shared control with safety projection), metrics for safety/efficiency/assurance/conflict, empirical results across 660 trials, and release of replay and haptic-synthesis artifacts. This positions tactile-first HMI as a practical pathway for safe, low-overhead operator supervision in vision-denied, contact-rich environments. Full article

Robotics is increasingly positioned as an enabling technology for precision agriculture, where management actions must be spatially and temporally targeted under constraints on labour, input use, safety, and environmental impact. This review synthesises studies on agricultural field robotics and organises the literature along four complementary axes: task (monitoring, weeding, spraying, and harvesting), platform (UGV, UAV, gantry/fixed-structure, greenhouse robot, and hybrid systems), autonomy-stack module (perception, localisation, planning, control, actuation, safety, and human–robot interaction), and evaluation setting (lab, greenhouse, open-field single season, and open-field multi-season/multi-site). Across these dimensions, this review analyses how platform constraints shape sensing geometry, actuation capability, localisation reliability, energy/endurance, supervision burden, and safety requirements. It further examines enabling technologies that recur across tasks, including vision and multimodal perception under occlusion and illumination variability, localisation and mapping under weak or denied GNSS, uncertainty-aware planning in deformable and partially observed environments, and compliant end-effectors for contact-rich operations. Beyond cataloguing systems, this paper emphasises evaluation practice by synthesising core task-relevant metrics, comparing laboratory and field validation settings, and proposing a reporting checklist and benchmark ladder to improve reproducibility and cross-study comparability. This review identifies recurring bottlenecks in domain shift, long-term autonomy, calibration robustness, crop-safe actuation, and safety assurance near humans, and it concludes with a staged research roadmap linking near-term evaluation reform to longer-term credible multi-site autonomy. Overall, this paper provides a structured framework for interpreting agricultural robotic systems not only by application but also by deployment context, system maturity, and evaluation credibility. Full article

Agentic AI is increasingly framed as enabling consumers to delegate commerce decisions and actions to digital assistants, yet consumer-facing evidence still centers on assistive chatbots and recommender-like systems, with scarce evaluation of execution-level delegation. This study provides an evidence-mapping review of empirical work on agentic commerce and synthesizes determinants and outcomes of delegation across three questions: (RQ1) how systems are operationalized (autonomy, task scope, interaction mode, and transaction capability/evidence realism), (RQ2) what facilitates or inhibits delegation, and (RQ3) what downstream outcomes follow for marketing performance and consumer experience. We searched Scopus and Web of Science for English-language, peer-reviewed primary studies (2015–2026) and applied conservative coding rules that distinguish claimed capability from simulated or demonstrated execution. The mapped literature is concentrated in text-based, low-autonomy assistants focused on recommendation and post-purchase support; coverage drops sharply for workflow-level autonomy, cart building, checkout/payment execution, and negotiation. Across studies, findings cluster into two motifs: a utility/assurance pathway in which performance cues and interaction quality increase perceived usefulness, satisfaction, and trust, and a governance pathway in which autonomy cues and system-initiated control trigger reactance/powerlessness and reduce acceptance unless mitigated by safeguards; urgency can attenuate governance resistance. Because most outcomes are intention- or vignette-based, calibration, verification, and error-recovery behaviors remain under-measured. Overall, delegation appears to depend less on maximizing autonomy than on coupling capability with user governance (consent, oversight, recourse, accountability), and we outline measurement priorities for evaluating execution-capable agents. Full article

Teacher education systems globally experience a gap in implementation between policy aspirations and everyday enactment, with implications for initial teacher education (ITE), the quality of practicums, professional identity, and teacher recruitment and retention. Situated in Malta’s superdiverse context and informed by international debates on professional capital, care ethics, inclusion, and ecological conceptions of agency, this article introduces the Responsive Teacher Formation Framework (RTFF). This original, theoretically integrated, and empirically grounded framework foregrounds four interdependent pillars of professional formation: belonging, wellbeing, autonomy and agency. Drawing on a two-year, multi-strand national inquiry synthesising perspectives from children, families, newly qualified teachers, learning support educators, and school leaders, we integrated artefact-elicitation, focus groups, interviews, and questionnaires using reflexive thematic analysis and cross-strand configurational synthesis. Through a meta-synthesis convergence of the different strands of the study, recurrent tensions surface, including procedural versus lived belonging; attention versus neglect of wellbeing; nominal autonomy versus fragile system supports and policy endorsement versus constrained agency. The findings demonstrate how these complexities are experienced across the ITE–school interface. We argue that the RTFF offers a coherent and tractable syntax for ITE programme (re)design that is both theoretically robust and practically adaptable, diagnostically sensitive to local context, and implementable at scale. The model contributes to international discourse by linking fragmented debates on these four pillars into a responsive framework of, and for, teacher formation. Beyond the Maltese case, the RTFF offers an adaptable orientation for superdiverse settings seeking to transition from compliance-driven quality assurance to formation-centred professional excellence. The article concludes by outlining how the RTFF can anchor more integrated and sustainable policy, as well as nurture professional learning communities, thereby advancing the transformation of teacher education for academic excellence. Full article

Trust assurance will be a cornerstone of the security and autonomy of sixth-generation (6G) networks. Traditional trust models focus on data authenticity and integrity; however, 6G systems increasingly rely on autonomous decision-making. This article presents blockchain as a dual function in 6G: first, as a decentralized and immutable ledger, and second, as a trustor, an entity that guarantees the transparency, traceability, and immutability of the trust assessment process itself. A conceptual framework is proposed in which blockchain houses both trust metrics and the logic that governs their assessment using smart contracts. Full article

This study critically examines the technological feasibility, regulatory challenges, and societal acceptance of Pilotless Passenger Aircraft (PPAs) in commercial aviation. A mixed-methods design integrated quantitative passenger surveys (n = 312) and qualitative pilot interviews (n = 15), analyzed using SPSS and NVivo to capture both statistical and thematic perspectives. Results show moderate public awareness (58%) but limited willingness to fly (23%), driven by safety (72%), cybersecurity (64%), and human judgement (60%) concerns. Among pilots, 93% agreed automation improves safety, yet 80% opposed removing human pilots entirely, underscoring reliance on human adaptability in emergencies. Both groups identified regulatory assurance, demonstrable reliability, and human oversight as prerequisites for acceptance. Technologically, this paper synthesizes advances in AI-driven flight management, multi-sensor navigation, and high-integrity control systems, including Airbus’s ATTOL and NASA’s ICAROUS, demonstrating that pilotless flight is technically viable but has yet to achieve the airline-grade reliability target of 10⁻⁹ failures per flight hour. Regulatory analysis of FAA, EASA, and ICAO frameworks reveals maturing but fragmented approaches to certifying learning-enabled systems. Ethical and economic evaluations indicate unresolved accountability, job displacement, and liability issues, with potential 10–15% operational cost savings offset by certification, cybersecurity, and infrastructure expenditures. Integrated findings confirm that PPAs represent a socio-technical challenge rather than a purely engineering problem. This study recommends a phased implementation roadmap: (1) initial deployment in cargo and low-risk missions to accumulate safety data; (2) hybrid human–AI flight models combining automation with continuous human supervision; and (3) harmonized international certification standards enabling eventual passenger operations. Policy implications emphasize explainable-AI integration, workforce reskilling, and transparent public engagement to bridge the trust gap. This study concludes that pilotless aviation will not eliminate the human element but redefine it, achieving autonomy through partnership between human judgement and machine precision to sustain aviation’s uncompromising safety culture. Full article

This qualitative study explored physically fit women’s physical, psychological, and societal reasons for engaging in exercise through the lenses of objectification theory and self-determination theory. A phenomenological approach enabled an in-depth examination of the complexity of women’s experiences, including the challenges and successes associated with physical exercise. The participants’ narratives ranged from accounts of perseverance in exercising, despite societal expectations and gendered stereotypes, to expressions of a desire to live long, happy, and healthy lives. Eight key themes were identified: initial motivators for physical exercise, benefits of physical exercise, perseverance, definition of a successful workout, gendered barriers to physical fitness, physical fitness identity, cost of physical fitness pursuit, and appearance motives. The findings highlight how physical exercise fosters empowerment and psychological well-being, with benefits such as self-assurance and self-love supporting sustained motivation over time. This study deepens understanding of how women navigate fitness within a broader sociocultural context and illustrates how external motivators can evolve into intrinsic motivation centered on autonomy, competence, and personal well-being. Full article

The growing reliance on autonomy in uncrewed aircraft systems (UASs) necessitates a real-time solution for assured contingency landing management during in-flight emergencies. This paper presents a novel gradient-guided search algorithm for risk-aware emergency landing trajectory generation with a wing-lift UAS loss-of-thrust use case. This framework integrates a compact four-dimensional discrete search space with aircraft kinematic and ground-risk cost. A multi-objective cost function is employed, combining flight envelope feasibility, optimal descent, and overflown population risk terms. To ensure discrete search convergence, a constrained hypervolume definition is introduced around the destination. A holding pattern identification algorithm is defined to minimize risk during the necessary flight path angle-constrained descent to final approach. Planner effectiveness is validated through randomly generated case studies over a region of Long Island, NY, under steady wind conditions. Benchmark comparisons with a 3D Dubins solver demonstrate the approach’s improved risk mitigation and acceptable real-time computation overhead. Future development will focus on integrating collision avoidance into the discrete search-based landing planner. Full article

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction. Full article

The development of the Autonomous vehicle industry contributes to achieving the environmental, economic, and social sustainability goals. Autonomous vehicles (AVs) involve highly autonomous and complex intelligent driving technology, and their large-scale commercial application depends on the level of public trust in their safety and reliability. Therefore, how to establish and enhance public trust in AVs has become the key to the development of the AV industry. This study discusses the impact of technical structural assurance, social structural assurance, and individual cognitive factors on trust in AVs. This study uses a structural equation model to analyze a valid sample of 548 participants from China. The results show that autonomy has a negative impact on trust, and currently, personal cognitive factors exert a greater influence on trust compared to technical and social structural assurance factors in China. In theory, this study combines the trust-in-automation three-factor model with the concept of structural assurance to reveal subjective controllable factors that can promote public trust. In practice, this study reveals the important role of structural assurance factors in enhancing trust before fully automatic driving technology is officially launched. Full article

While there is evidence of substantial improvement in efficiency and cost reduction from the integration of Robotics, Artificial Intelligence, and Drones (RAID) in solar installations; it is observed that there is limited oversight by international standards such as the International Electrotechnical Commission (IEC) in terms of the hazards and untapped potentials. This is partly because it is an emerging application and generally burdened with social acceptability issues. Thus, the safety regulations applied are adaptations of device-specific regulations as deemed fit by individual companies. Also, due to the fast-paced technological development of these platforms, there is huge potential for applications that are not currently supported by the device-specific regulations. This creates a multi-faceted demand for the establishment of standardized, industry-wide polices and guidelines on the use of RAID platforms for Solar PV integrations. This work aims to address critical safety concerns by conducting a comprehensive high-level system examination applicable to the monitoring and maintenance of Solar PV systems. Standard safety assurance models and approaches are examined to provide a safe autonomy perspective for Solar PVs. It is considered that, as RAID applications continue to evolve and become more prevalent in the Solar PV industry, standardized protocols or policies would be established to ensure safe and reliable operations. Full article