Search Results (51)

Human-oriented smart cities and landscape systems increasingly depend on reusable, high-resolution geospatial observations, yet unmanned aerial vehicle (UAV) imagery is commonly acquired for a single task and retained by separate organizations. This study diagnoses the resulting reuse bottlenecks using an improved combinatorial weighting multi-criteria decision method (ICW-MCDM). Seven experts assessed four dimensions and 17 criteria. Data Rights (0.0912), Data Security (0.0823), Share Policy (0.0819), Data Description (0.0804), and Incentives (0.0706) received the highest integrated weights. A transparent raw-score comparator recovered the same five-item set, while bootstrap and leave-one-out checks supported a governance-oriented leading set with panel dependence for some criteria. After C1–C6 were excluded, Data Description, Reliable Data, Access Permissions, Service Facilities, Search and Discovery, and Data Citation and Provenance became the leading post-entry requirements. By 11 August 2026, a national directory platform had recorded 336,753 visits, fewer than ten formal applications, and two completed university research deliveries. The cases demonstrate small-scale matching, cross-institutional aggregation, and controlled delivery, but not general platform effectiveness or downstream GeoAI outcomes. The study separates governance entry from technical readiness and identifies governance and technical prerequisites for GeoAI-ready UAV data infrastructure. 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

Continuous shared control provides an effective approach for intelligent vehicles to balance driving autonomy and system safety boundaries in complex human–machine interaction scenarios. However, existing shared control methods fail to dynamically adapt to the complex and time-varying states of the driver. To address this limitation, a hierarchical shared steering control strategy based on driver states is proposed in this paper. First, an in-vehicle eye tracker is utilized to collect data, and recognition features are extracted based on real-world datasets. Subsequently, a CNN-TCN deep learning algorithm is employed to train a model for identifying five-dimensional driver states. To mitigate excessive intervention and driving experience degradation caused by model misclassifications, a total probability weighting mechanism is developed. This mechanism integrates the real-time confidence distribution output by the neural network with the established baseline safety weights for each driving state, enabling the dynamic and continuous computation of the initial machine control authority. Furthermore, to eliminate high-frequency confidence spikes at the state perception end, a weight-smoothing strategy is designed using an adaptive nonlinear tracking differentiator based on Active Disturbance Rejection Control (ADRC). An autonomous driving controller is then constructed using the Linear Quadratic Regulator (LQR) method to ensure vehicle stability. Finally, Hardware-in-the-Loop (HIL) experiments conducted on a human–machine driving platform with hardware feedback verify the feasibility and superiority of the proposed method. Full article

Fine particulate matter with a diameter ≤2.5 μm (PM_2.5) pollution poses a global public health crisis, demonstrating significant threats to human health. This study focused on the strategically important Chengdu-Chongqing Economic Circle in western China, systematically comparing the toxic effects of urban and rural PM_2.5 across five levels. PMF and regression analysis were used to identify source contributions, dual-omics to pinpoint key molecules, and epidemiological data with a GAM model to assess health risks. Findings demonstrate that rural PM_2.5 possesses greater biotoxicity than its urban counterpart. Cytotoxicity in urban and rural PM_2.5 originated from road dust/vehicle emissions and biomass burning, respectively. Subsequently, integrated omics and molecular biology analyses identify kinesin family member 20A (KIF20A) as a shared key target, which mediates toxicity induced by both urban and rural PM_2.5. Finally, epidemiological analysis reveals that females and ≥65 years old exhibit relatively high sensitivity to urban PM_2.5 exposure trends, with rhinitis showing a comparatively higher impact among various related diseases. The novelty of this work lies in its pioneering application of a multi-tiered investigative approach. This approach spans “environmental samples-cellular mechanisms-population health” within the Chengdu-Chongqing economic circle context, systematically elucidating common and distinct respiratory health risk of urban and rural PM_2.5. This work offers a vital scientific foundation for advancing region-specific, precise air pollution prevention and control measures. Full article

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg game, and the steering control problem is formulated as an MPC optimization problem. The optimal control sequences of the driver and the Advanced Driver Assistance System (ADAS) under game equilibrium are then derived through backward induction. Subsequently, driver behaviour is classified as aggressive, moderate, or conservative according to lateral preview error and lateral acceleration, and the driver state is quantified using parametric indicators. Furthermore, by integrating potential field-based driving risk assessment with human–machine conflict intensity, a fuzzy logic-based dynamic weight adjustment mechanism is constructed. Simulation results show that when the steering intentions of the driver and the ADAS are highly consistent, the proposed strategy can effectively reduce driver workload and improve driving safety. In high-risk driving situations, the strategy automatically transfers more steering authority to the ADAS to enhance safety, whereas under low-risk conditions with strong human–machine steering conflict, greater driver authority is preserved to ensure that the vehicle follows the intended path. Hardware-in-the-loop experiments in lane-changing assistance scenarios further verify the effectiveness of the proposed strategy under different driving styles. Quantitative results show that, compared with manual driving, the proposed strategy reduces the maximum lateral overshoot by 98.75%, 85.54%, and 98.58% for aggressive, moderate, and conservative drivers, respectively. In addition, the peak yaw rate and driver control effort are significantly reduced, indicating smoother vehicle dynamic response and lower steering workload. These results demonstrate that the proposed strategy can effectively improve lane-change stability, reduce driver burden, and maintain safe and coordinated human–machine shared control. Full article

To address collision risks in teleoperated mobile robotic vehicles, this study proposes a Human–Machine Interaction-based Advanced Smooth Shared Control (ASSC) system aimed at enhancing obstacle avoidance and achieving smooth shared control between human operators and the automation system. The ASSC system integrates a novel approach using predictive vectors to represent the vehicle’s heading position, automatically adjusting the steering position upon obstacle detection to ensure smooth collision avoidance without changing the driver’s perception. Feedback forces applied to the steering wheel are calculated through an artificial potential field algorithm. Twenty participants were invited to operate the vehicle, providing feedback on the ASSC system’s performance relative to conventional obstacle avoidance methods. Performance metrics such as the effects of communication delays, Time to Complete the Task (TTC), ASSC effectiveness, performance of the delay impact on the ASSC system, and the Number of Obstacle Collisions (NOC) are analyzed. The results demonstrate that the ASSC system significantly outperforms traditional obstacle avoidance methods, providing more precise control in teleoperation. Statistical analysis indicates that the ASSC system improves safety, comfort and operational performance by 12.8%. This research highlights the ASSC system as a promising solution for enhancing automation, safety, and human–machine interaction in teleoperated mobile robotic vehicles. Full article

Autonomous vehicles (AVs) are expected to significantly influence road safety, traffic efficiency, and urban mobility. However, their real-world impacts depend not only on vehicle-level automation but also on interactions within the broader traffic ecosystem, including human-driven vehicles, vulnerable road users, infrastructure, and governance frameworks. This review provides a system-level synthesis of recent research on the integration of autonomous and connected autonomous vehicles in mixed traffic environments. Following PRISMA 2020 guidelines, 51 peer-reviewed studies published between 2016 and 2025 were systematically reviewed and thematically analyzed. The review addresses technological foundations, safety impacts, traffic flow and network performance, mixed traffic dynamics, infrastructure and urban systems, and policy and governance challenges. The findings indicate that AV impacts are highly non-linear and sensitive to market penetration rates, control strategies, and human behavioral adaptation. While high levels of automation and connectivity can improve safety, capacity, and traffic stability, early-stage deployment may temporarily increase delays and traffic conflicts. Policy measures—such as pricing, shared mobility integration, and regulatory oversight—are therefore critical to ensuring that AV deployment delivers sustainable and equitable mobility outcomes. Full article

Despite decades of research, autonomous forklifts remain deployed at a small scale (2–50 vehicles), while industrial warehouses require coordinating hundreds of vehicles in environments shared with human workers. This systematic review analyzes forklift-specific autonomous technologies published between 2010 and 2025 across major robotics databases (including IEEE Xplore, ACM, Elsevier, and related venues) to identify deployment barriers. Following the PRISMA guidelines, we systematically selected 122 peer-reviewed papers addressing forklift-specific challenges across eight subsystems: vehicle modeling, localization, planning, control, vision-based manipulation, multi-vehicle coordination, and safety. We synthesized 80 methods through 8 standardized comparison tables with quality assessment based on validation rigor. State-of-the-art approaches demonstrate strong laboratory performance: localization achieving ±1.4 mm accuracy, control enabling sub-centimeter manipulation, planning reducing mission times by 2–55%, vision reaching 98%+ recognition, and safety frameworks cutting rollover risk by 53–59%. However, validation predominantly occurs at laboratory scale, revealing a critical deployment gap. These achievements do not scale to industrial environments due to fleet coordination complexity, payload variability, and unpredictable human behavior. Our contributions include the following: (1) performance rankings with technology selection guidance, (2) systematic gap characterization, and (3) research priorities addressing mixed-fleet coordination, learning-enhanced control, and human-aware safety. This review was not prospectively registered. Full article

Human–machine shared control has been widely adopted to enhance driving performance and facilitate smooth transitions between manual and fully autonomous driving. However, existing authority allocation strategies often neglect real-time assessment of scenario complexity and driver workload. To address this gap, we leverage non-invasive eye-tracking devices and the 3D virtual driving simulator Car Learning to Act (CARLA) to collect multimodal data—including physiological measures and vehicle dynamics—for the real-time classification of scenario complexity and cognitive workload. Feature importance is quantified using the SHAP (SHapley Additive exPlanations) values derived from Random Forest classifiers, enabling robust feature selection. Building upon a Hidden Markov Model (HMM) for workload inference and a Model Predictive Control (MPC) framework, we propose a novel human–machine shared control architecture with adaptive authority allocation. Human-in-the-loop validation experiments under both high- and low-workload conditions demonstrate that the proposed strategy significantly improves driving safety, stability, and overall performance. Notably, under high-workload scenarios, it achieves substantially greater reductions in Time to Collision (TTC) and Time to Lane Crossing (TLC) compared to low-workload conditions. Moreover, the adaptive approach yields lower controller load than alternative authority allocation methods, thereby minimizing human–machine conflict. Full article

This article proposes a multi-agent path planning and decision-making solution for high-tempo field robotic operations, such as search-and-rescue, in large-scale unstructured environments. As a representative example, the subterranean environments can span many kilometers and are loaded with challenges such as limited to no communication, hazardous terrain, blocked passages due to collapses, and vertical structures. The time-sensitive nature of these operations inherently requires solutions that are reliably deployable in practice. Moreover, a human-supervised multi-robot team is required to ensure that mobility and cognitive capabilities of various agents are leveraged for efficiency of the mission. Therefore, this article attempts to propose a solution that is suited for both air and ground vehicles and is adapted well for information sharing between different agents. This article first details a sampling-based autonomous exploration solution that brings significant improvements with respect to the current state of the art. These improvements include relying on an occupancy grid-based sample-and-project solution to terrain assessment and formulating the solution-search problem as a constraint-satisfaction problem to further enhance the computational efficiency of the planner. In addition, the demonstration of the exploration planner by team MARBLE at the DARPA Subterranean Challenge finals is presented. The inevitable interaction of heterogeneous autonomous robots with human operators demands the use of common semantics for reasoning across the robot and human teams making use of different geometric map capabilities suited for their mobility and computational resources. To this end, the path planner is further extended to include semantic mapping and decision-making into the framework. Firstly, the proposed solution generates a semantic map of the exploration environment by labeling position history of a robot in the form of probability distributions of observations. The semantic reasoning solution uses higher-level cues from a semantic map in order to bias exploration behaviors toward a semantic of interest. This objective is achieved by using a particle filter to localize a robot on a given semantic map followed by a Partially Observable Markov Decision Process (POMDP)-based controller to guide the exploration direction of the sampling-based exploration planner. Hence, this article aims to bridge an understanding gap between human and a heterogeneous robotic team not just through a common-sense semantic map transfer among the agents but by also enabling a robot to make use of such information to guide its lower-level reasoning in case such abstract information is transferred to it. Full article

The transition from combustion vehicles to electric vehicles (EVs) is critical for mitigating climate change. As the global leader in the EV market, China has been propelled by government policies, market dynamics, and public awareness. As sentiment is fundamental to human communication, however, existing research lacks a systematic examination of the extent to which public environmental affect influences EV adoption at the macro level, particularly in the presence of government interventions and market strategies. To address this gap, we construct a novel affect index using a CNN deep learning model to extract environmental affect (positive, negative, and neutral) from Weibo posts between 2014 and 2023. Employing monthly EV market share as the dependent variable, we incorporate affect indices as key independent variables, alongside control variables such as government subsidy reductions, price levels, and other marketing strategies. A time-series cointegration model is applied to assess the long-term impact of environmental affect on EV sales. Empirical results reveal that only positive environmental affect has a significant and positive impact on EV adoption, whereas key industry factors, including subsidies, charger availability, patent activity, and price disparities, also play crucial roles. These findings highlight the growing influence of public awareness in shaping the EV market transition from government-driven to market-driven growth. Our study reconciles conflicting findings in prior research and provides actionable insights for policymakers and marketers seeking to foster sustainable EV adoption. Full article

Transportation 5.0 is an advanced and sophisticated system combining technologies with a focus on human-centered design and inclusivity. Its various components integrate intelligent infrastructure, autonomous vehicles, shared mobility services, green energy solutions, and data-driven systems to create an efficient and sustainable transportation network to tackle modern urban challenges. However, this evolution of transportation is also intended to improve accessibility by creating environmentally benign substitutes for traditional fuel-based mobility solutions, even when addressing traffic management and control issues. Consequently, to promote synergy for sustainability, the diversified nature of the Transportation 5.0 components ought to be efficiently and effectively managed. Thus, this study aims to reveal the involvement of Transportation 5.0 core component prediction in the sustainable transportation system through a systematic literature review. This study also contemplates the causal model under system dynamics modeling in order to address sustainable solutions and the movement toward sustainability in the context of Transportation 5.0. From this review, in addition to the developed causal model, it is identified that every core component management method in the sustainable Transportation 5.0 system reduces environmental impact while increasing passenger convenience and the overall efficiency and accessibility of the transport network, with greater improvements for developing nations. As the variety of transportation options, including electric vehicles, is successfully integrated, this evolution will eventually enable shared mobility, green infrastructure, and multimodal transit options. Full article

The shared control system has been a key technology framework and trend, with its advantages in overcoming the performance shortage of safety and comfort in automated vehicles. Understanding human drivers’ driving capabilities and styles is the key to improving system performance, in particular, the acceptance by and adaption of shared control vehicles to human drivers. In this research, personalized shared control considering drivers’ main human factors is proposed. A simulated scenario generation method for human factors was established. Drivers’ driving capabilities were defined and evaluated to improve the rationality of the driving authority allocation. Drivers’ driving styles were analyzed, characterized, and evaluated in a field test for the intention-aware personalized automated subsystem. A personalized shared control framework is proposed based on the driving capabilities and styles, and its evaluation criteria were established, including driving safety, comfort, and workload. The personalized shared control system was evaluated in a human-in-the-loop simulation platform and a field test based on an automated vehicle. The results show that the proposed system could achieve better performances in terms of different driving capabilities, styles, and complex scenarios than those only driven by human drivers or automated systems. Full article

The existing signal control methods for mixed traffic related to connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs) at intersections fail to tap the traffic potential of CAV-dedicated lanes. Accordingly, a dynamic allocation method of CAV-shared lanes is proposed, and the method of traffic flow scheduling and CAV trajectory optimization for multilane intersections with CAV-shared lanes is constructed to improve the traffic performance. The simulation results show that the optimization strategy proposed in this study can reduce the average delay at the intersection to varying degrees compared with the control strategy, using (a) the dynamic CAV-dedicated lane allocation method and (b) the shared-phase dedicated-lane method. Although the stops of CAVs will increase, the time utilization rate of most approach lanes is considerably improved, particularly CAV-shared lanes that can effectively improve the intersection performance. Further analysis shows that the number of CAV-shared lanes is closely dependent on the CAV penetration rate. The method proposed in this study is suitable for multilane intersections with a high CAV penetration rate. Full article

In this paper, we introduce the preview-follower theory for modeling the trajectory-tracking controller of an automated system using model predictive control (MPC). The primary contribution of this research lies in enhancing tracking accuracy and driving safety when the driver and automated system share similar driving intentions, while also enabling a rapid transfer of driving authority to the human driver in cases of differing intentions. To verify the effectiveness of the proposed driving authority optimization framework, both simulation and driver-in-the-loop experiments were conducted under conditions of consistent and inconsistent driving intentions between the human driver and the autonomous driving system. The results of both experiments demonstrated that the proposed shared steering cooperative control and driving authority optimization framework not only significantly improves vehicle tracking accuracy but also promptly aligns with the driver’s intentions. Full article