Search Results (178)

The spatial genes of rural settlements show a lot of different traditional settlement traits, which makes them a great starting point for studying rural spatial morphology. However, qualitative and macro-regional statistical indicators are usually used to find and extract rural settlement spatial genes. Taking Yubei Village in the Nanxi River Basin as an example, this study combined remote sensing images, real-time drone mapping, GIS (geographic information system), and space syntax, extracted 12 key indicators from five dimensions (landform and water features (environment), boundary morphology, spatial structure, street scale, and building scale), and quantitatively “decoded” the spatial genes of the settlement. The results showed that (1) the settlement is a “three mountains and one water” pattern, with cultivated land accounting for 37.4% and forest land accounting for 34.3% of the area within the 500 m buffer zone, while the landscape spatial diversity index (LSDI) is 0.708. (2) The boundary morphology is compact and agglomerated, and locally complex but overall orderly, with an aspect ratio of 1.04, a comprehensive morphological index of 1.53, and a comprehensive fractal dimension of 1.31. (3) The settlement is a “clan core–radial lane” network: the global integration degree of the axis to the holy hall is the highest (0.707), and the local integration degree R3 peak of the six-room ancestral hall reaches 2.255. Most lane widths are concentrated between 1.2 and 2.8 m, and the eaves are mostly higher than 4 m, forming a typical “narrow lanes and high houses” water town streetscape. (4) The architectural style is a combination of black bricks and gray tiles, gable roofs and horsehead walls, and “I”-shaped planes (63.95%). This study ultimately constructed a settlement space gene map and digital library, providing a replicable quantitative process for the diagnosis of Jiangnan water town settlements and heritage protection planning. Full article

In the field of autonomous driving, the accurate identification of drivable areas on roads is the key to ensuring the safe driving of vehicles. However, unstructured roads lack clear lane lines and regular road structures, and they have fuzzy edges and rutting marks, which greatly increase the difficulty of identifying drivable areas. To address the above challenges, this paper proposes a drivable area detection method for unstructured roads based on the MRNet model. To address the problem that unstructured roads lack clear lane lines and regular structures, the model dynamically captures local and global context information based on the self-attention mechanism of a Transformer, and it combines the input of image and LiDAR data to enhance the overall understanding of complex road scenes; to address the problem that detailed features such as fuzzy edges and rutting are difficult to identify, a multi-scale dilated convolution module (MSDM) is proposed to capture detailed information at different scales through multi-scale feature extraction; to address the gradient vanishing problem in feature fusion, a residual upsampling module (ResUp Block) is designed to optimize the spatial resolution recovery process of the feature map, correct errors, and further improve the robustness of the model. Experiments on the ORFD dataset containing unstructured road data show that MRNet outperforms other common methods in the drivable area detection task and achieves good performance in segmentation accuracy and model robustness. In summary, MRNet provides an effective solution for drivable area detection in unstructured road environments, supporting the environmental perception module of autonomous driving systems. Full article

The emergence of autonomous vehicles offers the potential to eliminate traditional traffic lanes, enabling vehicles to navigate freely in two-dimensional spaces. Unlike conventional traffic constrained by physical lanes, autonomous vehicles rely on real-time data exchange within platoons to adopt cooperative movement strategies, similar to synchronized flocks of birds. Motivated by this paradigm, this paper introduces an innovative traffic flow model based on the principles of particle swarm intelligence. In the proposed model, each vehicle within a platoon is treated as a particle contributing to the collective dynamics of the system. The motion of each vehicle is determined by the following two key factors: its local optimal velocity, influenced by the preceding vehicle, and its global optimal velocity, derived from the average of the optimal velocities of M vehicles within its observational range. To implement this framework, we develop a novel particle swarm optimization algorithm for autonomous vehicles and rigorously analyze its stability using linear system stability theory, as well as evaluate the system’s performance through four distinct indices inspired by traditional control theory. Numerical simulations are conducted to validate the theoretical assumptions of the model. The results demonstrate strong consistency between the proposed swarm intelligent model and the Bando model, providing evidence of its effectiveness. Additionally, the simulations reveal that the stability of the traffic flow system is primarily governed by the learning parameters $c_1$ and $c_2$, as well as the field of view parameter M. These findings underscore the potential of the swarm intelligent model to improve traffic flow system dynamics and contribute to the broader application of autonomous traffic systems management. In addition, it is worth noting that this paper explores the operational control of an AV platoon from a theoretical perspective, without fully considering passenger comfort, as well as “soft” instabilities (vehicles joining/leaving) and “hard” instabilities (technical failures/accidents). Future research will expand on these related aspects. Full article

Cooperative driving systems can coordinate individual vehicles on the road in a platoon, holding significant promise for enhancing traffic efficiency and lowering the energy consumption of vehicle movements. For an extended period, vehicles on the road will consist of a mix of traditional gasoline and electric vehicles. To explore the economic driving strategies for diverse vehicles on the road, this paper introduces a collaborative eco-driving system that takes into account the energy consumption traits of vehicles. Unlike prior research, this paper puts forward a lane change decision-making approach that integrates energy modeling and speed prediction. This method can effectively capture the speed variations in the vehicle ahead and facilitate lane changes with energy efficiency in mind. The system encompasses three vital functions: vehicle cooperative architecture, ecological trajectory planning, and power system control. Specifically, eco-speed planning is carried out in two stages: the initial stage is executed globally, with cooperative speed optimization performed based on the energy consumption characteristics of different vehicles to determine the economical speed for vehicle platoon driving. The subsequent stage involves local speed adaptation, where the vehicle platoon dynamically adjusts its speed and makes lane change decisions according to local driving conditions. Ultimately, the generated control information is fed into the powertrain control system to regulate the vehicle. To assess the proposed collaborative eco-driving system, the algorithms were tested on highways, and the results substantiated the system’s efficacy in reducing the energy consumption of vehicle driving. Full article

The growing demand for equitable and efficient transportation solutions has positioned autonomous vehicles (AVs) as a transformative technology with significant potential for rural areas. This literature review examines the challenges and opportunities associated with AV deployment in rural environments, characterized by sparse infrastructure, diverse road conditions, and aging populations. Using a systematic analysis of field tests, simulation-based studies, and survey research, key obstacles are identified, including limited lane markings, unpaved roads, digital connectivity gaps, and user acceptance issues. The results highlight the critical role of advancements in sensor technology, localization methods, and edge computing in addressing these barriers. Additionally, strategic infrastructure modifications, such as enhanced road signage and reliable communication systems, are essential for AV integration. This paper emphasizes the need for tailored AV solutions to meet the specific requirements of rural settings, including adaptability to adverse weather conditions and mixed traffic environments. Insights into public perception reveal the importance of trust-building initiatives and community engagement to foster widespread acceptance. The findings provide actionable recommendations for policymakers, industry leaders, and infrastructure operators, focusing on scalable deployment strategies, policy adaptations, and sustainable solutions. By addressing these challenges, AVs enhance mobility, safety, and accessibility, transforming rural transportation networks into more equitable and efficient systems. This review serves as a foundational reference for future research, charting pathways for the integration of AVs in rural contexts. Full article

To address the challenges of lane line recognition failure and insufficient segmentation accuracy in complex autonomous driving scenarios, this paper proposes a dual-branch instance segmentation method that integrates multi-scale modeling and dynamic feature enhancement. By constructing an encoder-decoder architecture and a cross-scale feature fusion network, the method effectively enhances the feature representation capability of multi-scale information through the integration of high-level feature maps (rich in semantic information) and low-level feature maps (retaining spatial localization details), thereby improving the prediction accuracy of lane line morphology and its variations. Additionally, hierarchical dilated convolutions (with dilation rates 1/2/4/8) are employed to achieve exponential expansion of the receptive field, enabling better fusion of multi-scale features. Experimental results demonstrate that the proposed method achieves F1-scores of 76.0% and 96.9% on the CULane and Tusimple datasets, respectively, significantly enhancing the accuracy and reliability of lane detection. This work provides a high-precision, real-time solution for autonomous driving perception in complex environments. Full article

The rapid advancement of computer vision technology is transforming how transportation agencies collect roadway characteristics inventory (RCI) data, yielding substantial savings in resources and time. Traditionally, capturing roadway data through image processing was seen as both difficult and error-prone. However, considering the recent improvements in computational power and image recognition techniques, there are now reliable methods to identify and map various roadway elements from multiple imagery sources. Notably, comprehensive geospatial data for pedestrian and bicycle lanes are still lacking across many state and local roadways, including those in the State of Florida, despite the essential role this information plays in optimizing traffic efficiency and reducing crashes. Developing fast, efficient methods to gather this data are essential for transportation agencies as they also support objectives like identifying outdated or obscured markings, analyzing pedestrian and bicycle lane placements relative to crosswalks, turning lanes, and school zones, and assessing crash patterns in the associated areas. This study introduces an innovative approach using deep neural network models in image processing and computer vision to detect and extract pedestrian and bicycle lane features from very high-resolution aerial imagery, with a focus on public roadways in Florida. Using YOLOv5 and MTRE-based deep learning models, this study extracts and segments bicycle and pedestrian features from high-resolution aerial images, creating a geospatial inventory of these roadway features. Detected features were post-processed and compared with ground truth data to evaluate performance. When tested against ground truth data from Leon County, Florida, the models demonstrated accuracy rates of 73% for pedestrian lanes and 89% for bicycle lanes. This initiative is vital for transportation agencies, enhancing infrastructure management by enabling timely identification of aging or obscured lane markings, which are crucial for maintaining safe transportation networks. Full article

The aim of this research is to analyze new proposals for the organization of traffic in the intersection model with rational solutions to increase its performance. The intersection represents a traffic node on a major European corridor and can be classified as highly loaded with frequent traffic jams. The signal-controlled intersection does not currently provide a suitable solution for traffic jams. Using Aimsun version 8 software, a detailed model of the intersection was developed, where the current situation and nine alternative scenarios (an optimization of the signal plan, an addition of a fourth signal phase, a widening of the connecting lane and the construction of an overpass) were tested. The simulation results were compared with the results of calculations according to the applicable technical regulations. The performance was evaluated based on delay time, travel time, queue length, and vehicle speed. The results showed that the overpass is the most effective solution, significantly reducing delay times and improving traffic flow. While other solutions offered local improvements, the overpass provided the most significant overall performance increase. Full article

Cities, particularly megacities, face significant challenges in transitioning toward sustainability. Many countries have developed dual or multiple capitals for diverse purposes (e.g., political, administrative, economic, touristic, and cultural). Limited research exists on the ‘15-minute city’ (15-MC) concept, particularly in regions like Middle East and North Africa (MENA region). This study evaluates the application of the ‘15-MC’ concept globally and regionally to derive Urban Planning Principles (UPPs) and indicators for livability and accessibility. Using a theoretical framework supported by site visits and quantitative assessments, the research examines two districts in the NAC as case studies. Key UPPs (e.g., proximity to services, mixed-use development, public transport, green spaces, community engagement, local economy, and sustainability) were evaluated along with walkability scores, bike infrastructure, and environmental impact indicators. The results reveal that most services in the two districts are accessible within a 15-minute walk or bike ride. However, essential facilities (e.g., universities and hospitals) exceed this threshold (20–30 min). The green area per inhabitant (17 m²/capita) meets WHO and European recommendations. The NAC has clean, green public transportation and 94.26 km of cycling lanes. For the sustainability indicator, air pollutants (PM₁₀ and NO₂) slightly exceed the WHO guidelines, but SO₂ and Ozone levels are below the limits. The estimated waste per capita (274 kg) is lower than Cario and other counties. The findings suggest the NAC has the potential to fulfill the 15-MC concept through mixed-use developments, accessibility, and sustainable planning. This study serves for future research and modeling of the NAC when it is fully occupied. Full article

High-definition (HD) maps aim to provide detailed road information with centimeter-level accuracy, essential for enabling precise navigation and safe operation of autonomous vehicles (AVs). Traditional offline construction methods involve several complex steps, such as data collection, point cloud generation, and feature extraction, but these methods are resource-intensive and struggle to keep pace with the rapidly changing road environments. In contrast, online HD map construction leverages onboard sensor data to dynamically generate local HD maps, offering a bird’s-eye view (BEV) representation of the surrounding road environment. This approach has the potential to improve adaptability to spatial and temporal changes in road conditions while enhancing cost-efficiency by reducing the dependency on frequent map updates and expensive survey fleets. This survey provides a comprehensive analysis of online HD map construction, including the task background, high-level motivations, research methodology, key advancements, existing challenges, and future trends. We systematically review the latest advancements in three key sub-tasks: map segmentation, map element detection, and lane graph construction, aiming to bridge gaps in the current literature. We also discuss existing challenges and future trends, covering standardized map representation design, multitask learning, and multi-modality fusion, while offering suggestions for potential improvements. Full article

In container terminal operations, truck lifting accidents pose a serious threat to the safety and efficiency of automated equipment. Traditional detection methods using visual cameras and single-line Light Detection and Ranging (LiDAR) are insufficient for capturing three-dimensional spatial features, leading to reduced detection accuracy. Moreover, the boundary features of key accident objects, such as containers, truck chassis, and wheels, are often blurred, resulting in frequent false and missed detections. To tackle these challenges, this paper proposes an accident detection method based on multi-line LiDAR and an improved PointNet++ model. This method uses multi-line LiDAR to collect point cloud data from operational lanes in real time and enhances the PointNet++ model by integrating a multi-layer perceptron (MLP) and a mixed attention mechanism (MAM), optimizing the model’s ability to extract local and global features. This results in high-precision semantic segmentation and accident detection of critical structural point clouds, such as containers, truck chassis, and wheels. Experiments confirm that the proposed method achieves superior performance compared to the current mainstream algorithms regarding point cloud segmentation accuracy and stability. In engineering tests across various real-world conditions, the model exhibits strong generalization capability. Full article

Adjacent existing box girder bridges should be spliced in the long-span bridge expansion project. A type of integral splicing composite structure for connecting the adjacent flange plates is designed herein. The mechanical characteristic of the integral splicing composite structure is tested using a local full-scale model, and a refined simulation model is also proposed for the optimization of the integral splicing composite structure. The loop bar in the joint connection segment and the application of Ultra-High-Performance Concrete (UHPC) material can guarantee the effective connection between the existing flange plate and the splicing structure. The embedded angled bar can delay the interface debonding failure and interface slip. The UHPC composite segment below the flange plate (segment CF) can bend together with the existing flange plate. In this study, an innovative integral splicing composite structure for a long-span bridge extension project is proposed and verified using both a local full-scale model test and finite element simulation. The adaptation of UHPC material and loop bar joint connection form can meet the cracking loading requirements of the splicing box girder structure. By proposing a refined simulation model and comparing the calculation result with the test result, it is found that the flexural performance of the integral splicing composite structure depends on the size of the composite segment below the flange plate (segment CF). Increasing the width of segment CF is beneficial to delay the interface debonding failure, and increasing its thickness can effectively delay the cracking load of the flange plate. Finally, the scheme of segment CF with one side width of 200 cm and a minimum thickness of 15 cm can improve the flexural resistance of the spliced structure and avoid the shear effect caused by the lane layout scheme and the location of the segment CF end. Through the research in this paper, the reasonable splicing form of a long-span old bridge is innovated and verified, which can be used as a reference for other long-span bridge splicing projects. Full article

Bus lanes with intermittent prioritization (BLIPs) have been proposed as a way to reduce traffic burden and improve air quality along busy urban streets; however, to date, the impacts of BLIPs on local-scale air quality have not been thoroughly evaluated, due in part to challenges in study design. We measured traffic-emission proxies—black carbon aerosol and ultrafine particles—before and after the installation of a BLIP in the Boston area (Massachusetts, USA) in 2021, and compared our data with traffic measurements to determine whether changes in air quality were attributable to changes in traffic patterns. We used both stationary and mobile monitoring to characterize temporal and spatial variations in air quality both before and after the BLIP went into operation. Although the BLIP led to a reduction in traffic volume (~20%), we did not find evidence that this reduction caused a significant change in local air quality. Nonetheless, substantial spatial and temporal differences in pollutant concentrations were observed; the highest concentrations occurred closest to a nearby highway along a section of the bus lane that was in an urban canyon, likely causing pollutant trapping. Wind direction was a dominant influence: pollutant concentrations were generally higher during winds that oriented the bus lane downwind of or parallel to the highway. Based on our findings, we recommend in future studies to evaluate the effectiveness of BLIPs that: (i) traffic and air quality measurements be collected simultaneously for several non-weekend days immediately before and immediately after bus lanes are first put into operation; (ii) the evaluation should be performed when other significant changes in motorists’ driving behavior and bus ridership are not anticipated; and (iii) coordinated efforts be made to increase bus ridership and incentivize motorists to avoid using the bus lane during the hours of intermittent prioritization. Full article

Agricultural navigation, as an essential part of smart agriculture, is a crucial step in realizing intelligence and, compared with the structured features of urban roads, such as lane-keeping lines, traffic guidance lines, etc., the field environment is more complex. Especially at agricultural intersections, traditional navigation line extraction algorithms make it difficult to achieve the automatic prediction of multiple road navigation lines due to complex unstructured features such as weeds and trees. Therefore, this study proposed a field road navigation line prediction method based on an improved context guided network (CGNet), which can quickly, stably, and accurately detect intersection fields and promptly predict navigation lines for two different directional paths at intersections. Firstly, CGNet will be used to learn the local features of intersections and the joint features of video frames before and after the surrounding environment. Then, the CGNet with a self-attention block module is proposed by adding the self-attention mechanism to improve the semantic segmentation accuracy of CGNet in field road scenes, and the detection speed is not significantly reduced. The semantic segmentation accuracy mIoU is 0.89, and the processing speed is 104 FPS. Subsequently, a field road centerline extraction algorithm is proposed based on the partitioning idea, which can accurately obtain the centerlines of road intersections in the image. The average lateral deviation of each centerline is less than 4%. This study achieved the prediction of intersection navigation lines in mountainous field road scenes, which can provide technical support for field operation road planning of agricultural equipment such as plant protection and harvesting. At the same time, the research findings provide theoretical references for sustainable agricultural development. Full article

The detection of road features is a necessary approach to achieve autonomous driving. And lane lines are important two-dimensional features on roads, which are crucial for achieving autonomous driving. Currently, research on lane detection mainly focuses on the positioning detection of local features without considering the association of long-distance lane line features. A grid anchor lane detection model based on attribute correlation is proposed to address this issue. Firstly, a grid anchor lane line expression method containing attribute information is proposed, and the association relationship between adjacent features is established at the data layer. Secondly, a convolutional reordering upsampling method has been proposed, and the model integrates the global feature information generated by multi-layer perceptron (MLP), achieving the fusion of long-distance lane line features. The upsampling and MLP enhance the dual perception ability of the feature pyramid network in detail features and global features. Finally, the attribute correlation loss function was designed to construct feature associations between different grid anchors, enhancing the interdependence of anchor recognition results. The experimental results show that the proposed model achieved first-place F1 scores of 93.05 and 73.27 in the normal and curved scenes on the CULane dataset, respectively. This model can balance the robustness of lane detection in both normal and curved scenarios. Full article