Search Results (240)

To address the challenges in unstructured orchard environments, including high geometric similarity between fruit trees (with the measured average Euclidean distance difference between point cloud descriptors of adjacent trees being less than 0.5 m), significant dynamic interference (e.g., interference from pedestrians or moving equipment can occur every 5 min), and uneven terrain, this paper proposes an improved mapping algorithm named OSC-LIO (Orchard Scan Context Lidar Inertial Odometry via Smoothing and Mapping). The algorithm designs a dynamic point filtering strategy based on Euclidean clustering and spatiotemporal consistency within a 5-frame sliding window to reduce the interference of dynamic objects in point cloud registration. By integrating local semantic features such as fruit tree trunk diameter and canopy height difference, a two-tier verification mechanism combining “global and local information” is constructed to enhance the distinctiveness and robustness of loop closure detection. Motion compensation is achieved by fusing data from an Inertial Measurement Unit (IMU) and a wheel odometer to correct point cloud distortion. A three-level hierarchical indexing structure—”path partitioning, time window, KD-Tree (K-Dimension Tree)”—is built to reduce the time required for loop closure retrieval and improve the system’s real-time performance. Experimental results show that the improved OSC-LIO system reduces the Absolute Trajectory Error (ATE) by approximately 23.5% compared to the original LIO-SAM (Tightly coupled Lidar Inertial Odometry via Smoothing and Mapping) in a simulated orchard environment, while enabling stable and reliable path planning and autonomous navigation. This study provides a high-precision, lightweight technical solution for autonomous navigation in orchard scenarios. Full article

Interest in the adoption of autonomous vehicles (AVs) continues to grow. It is essential to design new software architectures that meet stringent real-time, safety, and scalability requirements while integrating heterogeneous hardware and software solutions from different vendors and developers. This paper presents a lightweight, modular, and scalable architecture grounded in Service-Oriented Architecture (SOA) principles and implemented in ROS 2 (Robot Operating System 2). The proposed design leverages ROS 2’s Data Distribution System-based Quality-of-Service model to provide reliable communication, structured lifecycle management, and fault containment across distributed compute nodes. The architecture is organized into Perception, Planning, and Control layers with decoupled sensor access paths to satisfy heterogeneous frequency and hardware constraints. The decision-making core follows an event-driven policy that prioritizes fresh updates without enforcing global synchronization, applying zero-order hold where inputs are not refreshed. The architecture was validated on a 1:10-scale autonomous vehicle operating on a city-like track. The test environment covered canonical urban scenarios (lane-keeping, obstacle avoidance, traffic-sign recognition, intersections, overtaking, parking, and pedestrian interaction), with absolute positioning provided by an indoor GPS (Global Positioning System) localization setup. This work shows that the end-to-end Perception–Planning pipeline consistently met worst-case deadlines, yielding deterministic behaviour even under stress. The proposed architecture can be deemed compliant with real-time application standards for our use case on the 1:10 test vehicle, providing a robust foundation for deployment and further refinement. Full article

Pedestrian evacuation efficiency is paramount to public safety and sustainable urban resilience. This study utilizes an agent-based model simulating evacuation dynamics in a built environment to assess the impact of route familiarity, interpersonal interactions, and storage layout on evacuation efficiency. The model incorporates an evolutionary game theory framework to capture strategic decision-making, featuring both symmetric and asymmetric interactions among evacuees with varying levels of exit information (complete, partial, or none). Results show that familiarity with exit location is the most decisive element for evacuation, significantly outweighing the influence of crowd interactions, imitation behaviors, group composition, or storage layout. Furthermore, the crowd composition exerts a significant moderating effect, so that asymmetric group structures yield superior evacuation performance compared to symmetric ones. The optimal storage layout for evacuation is contingent upon the availability of exit information. An orderly layout is superior when information is known, whereas a random layout proves more effective in the absence of information by preventing misleading paths. Thus, providing clear information, adaptable spatial designs and consciously constructing a heterogeneous population structure are more critical for evacuation. This work provides actionable insights for architects and safety planners, contributing directly to the development of safer, more sustainable built environments and supporting Sustainable Development Goal (SDG) 11, particularly Target 11.5. Full article

Predicting multiple future pedestrian trajectories is a challenging task for real-world applications like autonomous driving and robotic motion planning. Existing methods primarily focus on immediate spatial interactions among pedestrians, often overlooking the impact of distant spatial environments on their future trajectory choices. Additionally, aligning trajectory smoothness and temporal consistency remains challenging. We propose a multimodal trajectory prediction model that utilizes spatio-temporal graphical attention networks for crowd scenarios. Our method begins by generating simulated multiview pedestrian trajectory data using CARLA. It then combines original and selected multiview trajectories using a convex function to create augmented adversarial trajectories. This is followed by encoding pedestrian historical data with a multitarget detection and tracking algorithm. Using the augmented trajectories and encoded historical information as inputs, our spatio-temporal graph Transformer models scaled spatial interactions among pedestrians. We also integrate a trajectory smoothing method with a Memory Storage Module to predict multiple future paths based on historical crowd movement patterns. Extensive experiments demonstrate that our proposed MTP-STG model achieves state-of-the-art performance in predicting multiple future trajectories in crowds. Full article

Accurate, reliable and infrastructure-free indoor positioning using a smartphone is considered an essential topic for applications such as indoor emergency response and indoor path planning. While the inertial measurement units (IMU) offer continuous and high-frequency motion data, pedestrian dead reckoning (PDR) based on IMU data suffers from significant and accumulative errors. Map-aided particle filters (PFs) are important pose estimation frameworks that have exhibited capabilities to eliminate drifts by incorporating additional constraints from a pre-built floor map, without relying on other wireless or perception-based infrastructures. However, despite the recent approaches, a key challenging issue remains: existing map-aided PF-PDR solutions are computationally demanding, as they typically rely on a large number of particles and require map boundaries to eliminate non-matching particles. This process introduces substantial computational overhead, limiting efficiency and real-time performance on resource-constrained platforms such as smartphones. To address this key issue, this work proposes a novel map-aided PF-PDR framework that leverages a smartphone’s IMU data and a pre-built vectorized floor plan map. The proposed method introduces an adaptive PF-PDR solution that detects particle convergence using a cross-entropy distance of the particles and a Gaussian distribution. The number of particles is reduced significantly after a convergence is detected. Further, in order to reduce the computational cost, only the heading is included in particle attitude sampling. The heading is estimated accurately by levelling gyroscope measurements to a virtual plane, parallel to the ground. Experiments are performed using a dataset collected on a smartphone and the results demonstrate improved performance, especially in drift reduction, achieving an mean position error of 0.9 m and a processing rate of 37.0 Hz. Full article

Urban streets in hot climates often suffer from inadequate shade, exacerbating pedestrian discomfort, urban heat island effects, and energy demands for cooling. Traditional tree-planting approaches overlook dynamic solar paths, building-induced shadows, and spacing requirements, resulting in suboptimal shade coverage and resource inefficiency. This study introduces a computational workflow in Rhino/Grasshopper to optimize tree placement and canopy radii through analysis of solar radiation and shadow patterns. By prioritizing sun-exposed zones, minimizing shadow overlaps, and ensuring growth-appropriate distances, the tool enhances shade distribution. Integration of parametric modeling and environmental simulations improved thermal comfort, reduced energy use, and evidence-based urban planning strategies. Across ten optimization runs, the workflow achieved a 68% increase in shade coverage, an 11.5 °C reduction in mean radiant temperature (MRT), and a 72% decrease in the spatial extent of high-risk heat-exposure zones, demonstrating its potential for climate-adaptive street design in hot-arid environments. Full article

Traditional pedestrian evacuation models struggle to balance global exit guidance with local, individual decision making under hazards. We address this by decomposing long-term objectives into Particle Swarm Optimization (PSO)-based micro-goals and proposing a hybrid Cellular Automaton (CA) and PSO model. The hybrid design reduces the decoupling between spatial discretization and individual choices and more tightly couples hazard and density fields with movement decisions. Two contributions are central. First, we develop an autonomous following pathfinding mechanism (AFPM) that linearly blends the direction toward a PSO micro-goal with a herd following direction and adds a small reward for directional consistency. This mitigates path chaos from purely autonomous moves and congestion aggregation from purely herding moves. Second, we build a multi-dimensional interpretability and robustness framework that combines the empirical Cumulative Distribution Function (CDF) and a kernel-smoothed Probability Density Function (PDF) of key evacuation times (T_clear, T_95%_alive) together with vulnerability curves, to analyze the data and assess robustness. It combines Shapley Sobol analysis to quantify parameter effects on clearance time T_clear and the 95% survival evacuation time T_95%_alive, with CDF/PDF summaries and vulnerability curves to assess anti-interference performance. Experiments use a simulated underground shopping mall. In a 60 pedestrian case, a geometry-only baseline yields T_clear $\approx 33 \mathrm{s}$; hazard- and density-aware strategies produce slightly longer T_clear but reduce peak bottleneck congestion by 20–30%. When one exit is closed, the exceedance probability at $\tau =70 \mathrm{s}$ drops from 0.44 to 0.36, reducing long tail risk. Compared with geometry-based Dijkstra, the proposed model slightly increases clearance time while lowering peak congestion by 20–30%, achieving a balance between efficiency and safety. The model and evaluation protocol provide technical support for evacuation policy, facility layout, and emergency system design in large complex buildings. Full article

The construction of post-industrial landscapes is increasingly regarded as an important pathway for promoting urban sustainability. However, limited attention has been given to the interconnections between post-industrial landscapes and local villagers in rural contexts. From the perspective of environmental justice, the ecological and cultural-tourism goals of post-industrial landscapes may be mismatched with villagers’ place-based needs. This study examines a typical rural post-industrial waterfront area in China to analyze villagers’ environmental justice. Representative project photographs were collected, and villagers’ perceptions were obtained through questionnaires and semi-structured interviews, yielding 98 valid responses (95% response rate). Quantitative measurements of landscape characteristics were combined with pairwise preference evaluations, and the analysis applied the framework of recognition, participatory, and distributive justice. A discrete choice model (DCM) and spatial analysis were then employed to explore the relationships. Quantitative analysis showed that natural vegetation, plazas, industrial heritage, and pedestrian paths had negative effects on villagers’ recognition (β = −0.36 to −0.18), whereas hardscape had a strong positive effect (β = 0.94). Moreover, spatial analysis indicated localized patterns of environmental injustice, highlighting uneven distribution of landscape benefits across the site. Semi-structured interviews revealed villagers’ priorities across landscape design, amenities, local livelihoods, and project implementation, highlighting the importance of safer, more functional, and well-managed spaces. Collectively, these findings underscore the importance of inclusive planning and design strategies that integrate ecological, cultural, and recreational considerations, thereby supporting the sustainable renewal of rural post-industrial waterfronts. Full article

This research situates urban planning as a social well-being determinant, highlighting that cities function as social habitats that shape individuals’ quality of life, as well as being physical spaces. The study emphasises the dangers of inadequate urban management, particularly when it is based on biased or incomplete information. This has the potential to exacerbate inequality and undermine the benefits of urbanisation. The present study focuses on the intersection of gender, social roles, and access to basic infrastructure, including childcare centres, elderly facilities, healthcare services, pedestrian paths, street lighting, and green areas. By addressing this critical urban issue, namely the uneven distribution of opportunities for well-being, the study contributes to the existing body of knowledge in this field. The Capability Approach, developed primarily by Amartya Sen and Martha Nussbaum, provides a theoretical framework for evaluating individuals’ freedom to pursue the lives they value. Theories of subjective well-being (SWB) are rooted in psychological and economic traditions that assess individuals’ life satisfaction, happiness, and emotional equilibrium The present study proposes a methodological framework that integrates the Capability Approach with Subjective Well-Being theory. This approach facilitates a more comprehensive measurement of citizens’ well-being, transcending the limitations of traditional gender dichotomies. The study identifies the manner in which infrastructural design affects individual capabilities and demonstrates the manner in which urban policies can foster gender equality and inclusive socio-economic development. It is therefore evident that the research provides urban planners and policymakers with actionable insights by demonstrating that equitable infrastructure provision is a cornerstone of sustainable, socially just urban development. Full article

Deploying autonomous vehicles in urban mobility systems promises significant improvements in safety, efficiency, and sustainability. On the other hand, running these vehicles in the continuously changing and often uncertain conditions of modern cities turns out to be a major challenge. These cars need advanced systems that can continuously change in order to observe conditions. This paper puts forward a new way that brings together multiple LIDAR sensors for the real-time spotting and following of objects, along with adaptive motion planning methods made to handle the difficulties of city traffic. Using LIDAR-based mapping for environmental modeling and predictive tracking techniques helps the system build a richly detailed, consistently updating depiction of surroundings that supports accurate and quick decisions. Another feature of the system is dynamic path planning that ensures safe navigation by considering traffic, pedestrian movement, and road conditions. Simulations carried out in highly dense urban scenarios show improvement in collision avoidance, path-planning optimization, and response to environmental dynamics. Such outcomes prove that combining multi-LIDAR tracking and adaptive motion planning contributes significantly to the performance and safety of an autonomous vehicle when operating in very complex urban conditions. Full article

With the acceleration of urbanization, environmental degradation is increasingly restricting the improvement of residents’ quality of life, and promoting the transformation of old communities has become a key path for sustainable urban development. However, existing buildings generally face challenges, such as the deterioration of the performance of the envelope structure and the rising energy consumption of the air conditioning system, which pose a serious test for the realization of green renovation. Inspired by the application of bionics in the field of architecture, this study innovatively designed five types of bionic envelope structures for outdoor air conditioning units, namely scales, honeycombs, spider webs, leaves, and bird nests, based on the aerodynamic characteristics of biological prototypes. The ventilation performance of these structures was evaluated at three scales—namely, single building, townhouse, and community—under natural ventilation conditions, using a CFD simulation system. The study shows the following: (1) the spider web structure has the best comprehensive performance among all types of enclosures, which can significantly improve the uniformity of the flow field and effectively eliminate the low-speed stagnation area on the windward side; (2) the structure reorganizes the flow structure of the near-wall area through the cutting and diversion of the porous grid, reduces the wake range, and weakens the negative pressure intensity, making the pressure distribution around the building more balanced; (3) in the height range of 1.5–27 m, the spider web structure performs particularly well at the townhouse and community scales, with an average wind speed increase of 1.1–1.4%; and (4) the design takes into account both the safety of the enclosure and the comfort of the pedestrian area, achieving a synergistic optimization of function and performance. This study provides new ideas for the micro-renewal of buildings, based on bionic principles, and has theoretical and practical value for improving the wind environment quality of old communities and promoting low-carbon urban development. Full article

This study evaluates four developed external Human–Machine Interface (eHMI) concepts for automated shuttles, focusing on improving communication with other road users, mainly pedestrians and cyclists. Without a human driver to signal intentions, eHMI systems can play a crucial role in conveying the shuttle’s movements and future path, fostering safety and trust. The four eHMI systems’ purple light projections, emotional eyes, auditory alerts, and informative text were tested in a virtual reality (VR) environment. Participant evaluations were collected using an approach inspired by Kansei engineering and Likert scales. Results show that auditory alerts and informative text-eHMI are most appreciated, with participants finding them relatively clear and easy to understand. In contrast, purple light projections were hard to see in daylight, and emotional eyes were often misinterpreted. Principal Component Analysis (PCA) identified three key factors for eHMI success: predictability, endangerment, and practicality. The findings underscore the need for intuitive, simple, and predictable designs, particularly in the absence of a driver. This study highlights how eHMI systems can support the integration of automated shuttles into public transport. It offers insights into design features that improve road safety and user experience, recommending further research on long-term effectiveness in real-world traffic conditions. Full article

Urban pedestrian-only spaces face challenges like inadequate leisure experiences and user discomfort. To enhance spatial conditions, it is crucial to evaluate various influencing factors. Many studies focus on individual elements, missing the benefits of a comprehensive approach. This study aims to propose a pedestrian behavior prediction model that establishes the relationship between multiple spatial factors and pedestrian distribution. We introduce a two-layer simulation framework for pedestrian dynamics, comprising a tactic layer responsible for path planning and an operational layer for velocity prediction based on the social force model. This framework enhances prediction accuracy, achieving a 46.3% improvement over the conventional model. Moreover, it underscores the importance of a holistic approach, emphasizing the need to consider group dynamics and random behaviors in pedestrian modeling. Full article

Rapid population aging calls for a shift from static facility configuration toward understanding how spatial structures coordinate with everyday behavior. This study develops a structure–behavior coordination framework to examine how the spatial embedding of community service centers and surrounding facilities aligns with older adults’ mobility and activity chains. Using Guangzhou as a case, three representative facility aggregation forms—clustered, linear, and patchy—were identified through POI-based spatial analysis. Behavioral mapping supported by Public Participation GIS (PPGIS) and social network analysis captured facility co-use and path continuity, while rank-based measures (Rank-QAP and Rank-Biased Overlap) evaluated correspondence between structural and behavioral centralities. Findings show form-sensitive rather than typological coordination: the clustered case (FY) exhibits compact, mixed-use integration; the linear case (DJ) requires ground-level access along main pedestrian corridors; and the patchy case (LG) relies on a few highly accessible dual-core nodes where improved connectivity strengthens cohesion. Everyday facilities such as markets, parks, and plazas act as behavioral anchors linking routine routes. The framework offers a transferable tool and comparable metrics for diagnosing alignment between built structure and everyday behavior, guiding adaptive, evidence-based planning for age-friendly community systems. Full article

With the increasing demand for intelligent mobility assistance among the visually impaired, machine guide dogs based on computer vision have emerged as an effective alternative to traditional guide dogs, owing to their flexible deployment and scalability. To enhance their visual perception capabilities in complex urban environments, this paper proposes an improved YOLOv8-based detection algorithm, termed TFP-YOLO, designed to recognize traffic signs such as traffic lights and crosswalks, as well as small obstacle objects including pedestrians and bicycles, thereby improving the target detection performance of machine guide dogs in complex road scenarios. The proposed algorithm incorporates a Triplet Attention mechanism into the backbone network to strengthen the perception of key regions, and integrates a Triple Feature Encoding (TFE) module to achieve collaborative extraction of both local and global features. Additionally, a P2 detection head is introduced to improve the accuracy of small object detection, particularly for traffic lights. Furthermore, the WIoU loss function is adopted to enhance training stability and the model’s generalization capability. Experimental results demonstrate that the proposed algorithm achieves a detection accuracy of 93.9% and a precision of 90.2%, while reducing the number of parameters by 17.2%. These improvements significantly enhance the perception performance of machine guide dogs in identifying traffic information and obstacles, providing strong technical support for subsequent path planning and embedded deployment, and demonstrating considerable practical application value. Full article