Snowy Conditions Research Articles

Trajectory planning for intelligent vehicle obstacle avoidance in rainy and snowy conditions

Trajectory planning for intelligent vehicle obstacle avoidance in rainy and snowy conditions

Flexible boron nitride-based composite membrane with superhydrophobic/electrothermal synergistic response for enhanced passive anti-icing and active de-icing

Ice crystals and snow adhesion can affect the operation and reliability of machines in cold, snowy conditions, so anti-freeze systems are often installed in machines. Commonly used ice protection systems usually place electrothermal and a hydrophobic layer in one layer, or create a hydrophobic layer at top of the electrothermal layer. However, the hydrophobic layer ruptures and there is a risk of a short circuit when water comes into contact with the electrically heated layer. In this paper, a method of using boron nitride film to separate the functional layer is proposed to reduce the risk of short circuit in the electric heating layer. Finally, boron nitride film is successfully used as the intermediate layer to connect the superhydrophobic layer with the electric heating layer, which realizes the passive. The results show that the contact angle of the BN-based composite membrane can reach more than 160°. Under the heating power of 0.54 W/cm2, the BN-based composite membrane can reach the steady state temperature of 82.6 °C in 6 min, the deicing efficiency can reach 15.435 kg h−1 m−2, and the removal of 6 mm thick melting ice takes only 185 s. In addition, after 10 electric heating cycles, the BN-based composite membrane can still maintain a temperature of about 80 °C, indicating that it has good durability. This study is of great significance for practical ice control applications.

High-accuracy road surface condition detection through multi-sensor information fusion based on WOA-BP neural network

High-accuracy road surface condition detection is crucial for enhancing traffic safety. However, existing methods struggle to comprehensively cover various road surface conditions, and the detection accuracy still requires improvement. In this study, we propose a novel road surface condition detection system integrating the whale optimization algorithm-enhanced backpropagation neural network (WOA-BP) with multi-sensor data, which significantly improves the detection accuracy. An experimental system was established, and the model's performance was validated through multiple experiments across a temperature range of −30℃ to 50℃. The results demonstrate that, compared to traditional machine learning algorithms such as back propagation neural network (BPNN), support vector machine (SVM), and random forest (RF), the WOA-BP neural network model shows superior performance in terms of detection accuracy and model stability. Specifically, it achieves a maximum accuracy rate of 100 % and an average accuracy rate of 98.8 % for detecting dry, wet, icy, and snowy conditions. By employing the WOA to refine the initial weights and thresholds of the BPNN, the issues of susceptibility to local optima and slow convergence are addressed, thereby enhancing the model's classification performance and robustness. Furthermore, the collaboration between the capacitive measurement-based road condition sensor, temperature sensor, and microwave measurement-based water film thickness sensor further enhances detection accuracy. This research delves into high-accuracy road condition detection technology utilizing the WOA-BP neural network, offering valuable insights for improving traffic safety and developing intelligent transportation systems.

Snow-CLOCs: Camera-LiDAR Object Candidate Fusion for 3D Object Detection in Snowy Conditions.

Although existing 3D object-detection methods have achieved promising results on conventional datasets, it is still challenging to detect objects in data collected under adverse weather conditions. Data distortion from LiDAR and cameras in such conditions leads to poor performance of traditional single-sensor detection methods. Multi-modal data-fusion methods struggle with data distortion and low alignment accuracy, making accurate target detection difficult. To address this, we propose a multi-modal object-detection algorithm, Snow-CLOCs, specifically for snowy conditions. In image detection, we improved the YOLOv5 algorithm by integrating the InceptionNeXt network to enhance feature extraction and using the Wise-IoU algorithm to reduce dependency on high-quality data. For LiDAR point-cloud detection, we built upon the SECOND algorithm and employed the DROR filter to remove noise, enhancing detection accuracy. We combined the detection results from the camera and LiDAR into a unified detection set, represented using a sparse tensor, and extracted features through a 2D convolutional neural network to achieve object detection and localization. Snow-CLOCs achieved a detection accuracy of 86.61% for vehicle detection in snowy conditions.

Generalized-Norm-Based Robustness Evaluation Model of Bus Network under Snowy Weather

Global climate change leads to frequent extreme snowfall weather, which has a significant impact on the safety and operating efficiency of urban public transportation. In order to cope with the adverse effects of extreme weather, governments should vigorously develop sustainable transportation. Since urban public transportation is a critical component of building a sustainable city, traffic management departments should quantitatively analyze the performance changes of the urban public transportation network under extreme weather conditions. Therefore, fully considering the comprehensive effects of network performance and topology to improve the robustness of urban public transportation systems requires more attention. The urban public transport network with high robustness can achieve fewer recovery costs, lower additional bus scheduling costs, and achieve the sustainable development of the public transport network. Considering the impact of travelers’ travel time tolerance and in-vehicle space congestion tolerance under snowy conditions, this paper proposes a generalized-norm-based robustness evaluation model of the bus network. Example analyses are conducted using checkerboard and ring-radial topological network structures to verify the applicability of the proposed model. The results show the following: (1) In an extreme snowfall scenario, the robustness of checkerboard and ring-radiating bus networks is reduced by 38% and 39%, respectively. (2) In the checkerboard network, the central area units are always more important to the system robustness than the peripheral units, while, in the ring-radial network, the units with higher importance are all in the ring line. (3) The failure of Ring Line 5 has a great impact on both the checkerboard and ring-radial networks, causing the system robustness to decrease by 43% and 50%, respectively.

Fractional B-Spline Wavelets and U-Net Architecture for Robust and Reliable Vehicle Detection in Snowy Conditions.

This paper addresses the critical need for advanced real-time vehicle detection methodologies in Vehicle Intelligence Systems (VIS), especially in the context of using Unmanned Aerial Vehicles (UAVs) for data acquisition in severe weather conditions, such as heavy snowfall typical of the Nordic region. Traditional vehicle detection techniques, which often rely on custom-engineered features and deterministic algorithms, fall short in adapting to diverse environmental challenges, leading to a demand for more precise and sophisticated methods. The limitations of current architectures, particularly when deployed in real-time on edge devices with restricted computational capabilities, are highlighted as significant hurdles in the development of efficient vehicle detection systems. To bridge this gap, our research focuses on the formulation of an innovative approach that combines the fractional B-spline wavelet transform with a tailored U-Net architecture, operational on a Raspberry Pi 4. This method aims to enhance vehicle detection and localization by leveraging the unique attributes of the NVD dataset, which comprises drone-captured imagery under the harsh winter conditions of northern Sweden. The dataset, featuring 8450 annotated frames with 26,313 vehicles, serves as the foundation for evaluating the proposed technique. The comparative analysis of the proposed method against state-of-the-art detectors, such as YOLO and Faster RCNN, in both accuracy and efficiency on constrained devices, emphasizes the capability of our method to balance the trade-off between speed and accuracy, thereby broadening its utility across various domains.

MSGC-YOLO: An Improved Lightweight Traffic Sign Detection Model under Snow Conditions

Traffic sign recognition plays a crucial role in enhancing the safety and efficiency of traffic systems. However, in snowy conditions, traffic signs are often obscured by particles, leading to a severe decrease in detection accuracy. To address this challenge, we propose an improved YOLOv8-based model for traffic sign recognition. Initially, we introduce a Multi-Scale Group Convolution (MSGC) module to replace the C2f module in the YOLOv8 backbone. Data indicate that MSGC enhances detection accuracy while maintaining model lightweightness. Subsequently, to improve the recognition ability for small targets, we introduce an enhanced small target detection layer, which enhances small target detection accuracy while reducing parameters. In addition, we replaced the original BCE loss with the improved EfficientSlide loss to improve the sample imbalance problem. Finally, we integrate Deformable Attention into the model to improve the detection efficiency and performance of complex targets. The resulting fused model, named MSGC-YOLOv8, is evaluated on an enhanced dataset of snow-covered traffic signs. Experimental results show that the MSGC-YOLOv8 model is used for snow road traffic sign recognition. Compared with the YOLOv8n model mAP@0.5:0.95, mAP@0.5:0.95 is increased by 17.7% and 18.1%, respectively, greatly improving the detection accuracy. Compared with the YOLOv8s model, while the parameters are reduced by 59.6%, mAP@0.5 only loses 1.5%. Considering all aspects of the data, our proposed model shows high detection efficiency and accuracy under snowy conditions.

Automated snow intensity measurement (ASIM)

During winter airports operations, the need for specialized de/anti-icing fluids for aircraft is paramount. To determine the effectiveness of these fluids when subjected to snowy conditions, endurance time tests are conducted in natural conditions. The actual method to measure the snow precipitation rate during those tests are time-consuming and prone to errors. To address this issue, a new prototype for an Automated System for Intensity Measurement has been developed. This system allows for precise measurement during the snow endurance test without any human intervention, reducing the risk of errors. A successful proof of concept in real natural conditions has validated to proper functioning of the prototype and results' consistency with traditional methods.

Multi-Modal Contrastive Learning for LiDAR Point Cloud Rail-Obstacle Detection in Complex Weather

Obstacle intrusion is a serious threat to the safety of railway traffic. LiDAR point cloud 3D semantic segmentation (3DSS) provides a new method for unmanned rail-obstacle detection. However, the inevitable degradation of model performance occurs in complex weather and hinders its practical application. In this paper, a multi-modal contrastive learning (CL) strategy, named DHT-CL, is proposed to improve point cloud 3DSS in complex weather for rail-obstacle detection. DHT-CL is a camera and LiDAR sensor fusion strategy specifically designed for complex weather and obstacle detection tasks, without the need for image input during the inference stage. We first demonstrate how the sensor fusion method is more robust under rainy and snowy conditions, and then we design a Dual-Helix Transformer (DHT) to extract deeper cross-modal information through a neighborhood attention mechanism. Then, an obstacle anomaly-aware cross-modal discrimination loss is constructed for collaborative optimization that adapts to the anomaly identification task. Experimental results on a complex weather railway dataset show that with an mIoU of 87.38%, the proposed DHT-CL strategy achieves better performance compared to other high-performance models from the autonomous driving dataset, SemanticKITTI. The qualitative results show that DHT-CL achieves higher accuracy in clear weather and reduces false alarms in rainy and snowy weather.

Risk assessment and electrical control of high-permeability renewable energy photovoltaic system under extreme weather conditions

Abstract High-permeability photovoltaic (PV) systems are a key technical way to bring about new energy changes in electric power networks. Risk assessment and electrical regulation during extreme weather can make PV systems with high permeability work much more efficiently during severe weather. We establish an operational risk model for a high penetration rate PV system using the PV probability model, load parameter model, and probabilistic trend calculation model. Based on the results of the tidal current calculation, the risk assessment indexes of PV systems are constructed, including the voltage overrun risk index and branch circuit tidal current overrun risk index. The Monte Carlo simulation method using the Latin hypercubic sampling method is used to simulate the operational loads of PV systems under extreme weather conditions such as windy, rainy, and snowy conditions and to form a fault simulation set. Finally, we implement the QMC method to assess the risk of PV systems with high permeability and use the RBF neural network design to achieve automatic control of PV electrical equipment. When the weather is the same extreme, the risk of overrun at each node goes up with the number of nodes. When the weather is different, the risk of voltage overrun at each node under ice-covered load goes up by about half compared to rain load, and the risk of active overrun at each branch goes up by almost six times compared to rain load.

Camouflage efficiency in a colour-polymorphic predator is dependent on environmental variation and snow presence in the wild.

Colour polymorphism can be maintained by colour morph-specific benefits across environmental conditions. Currently, the amount and the duration of snow cover during winter decrease especially in northern latitudes, which can alter the potential for camouflage of animals with light and dark morphs. Tawny owls, Strix aluco, are colour-polymorphic avian predators with dark (brown) and light (grey) colour morphs, where the grey morph is presumed to enjoy camouflage benefits under snowy conditions. We studied the camouflage potential of morphs in two tawny owls potential using passerines' probability to mob in the wild during winter with and without snow. For comparison with other seasons, we also repeated the experiment during spring and autumn. We found that grey tawny owls have a lower probability of being mobbed than the brown tawny owls only during snowy winters. The two colour morphs therefore experience differential benefits across snow conditions, which may help to maintain colour morphs in the population, although further warming of winter climate will reduce the potential for camouflage for grey tawny owls in northern latitudes.

Exploring the short-term water damage characteristics of asphalt mixtures: The combined effect of salt erosion and dynamic water scouring

In icy or snowy conditions, improper application and uneven dispersion of deicing salts on roadways can lead to saltwater accumulation, subsequently causing roadway scouring when vehicles transit. This study aims to assess the combined influence of salt erosion and dynamic water scouring on short-term water damage of asphalt mixtures. A custom-designed dynamic water scouring simulation apparatus is used, employing three diverse deicing salt solutions as agents for dynamic water scouring. Numerous iterations of dynamic water scouring tests are conducted on two types of graded asphalt mixtures. Post-scouring, alterations in air void content and water stability are evaluated through void content tests, Marshall water immersion tests, and freeze-thaw splitting tests. Additionally, the GM (1,N) damage prediction model from gray theory is applied for data modeling and prediction. The results reveal a significant impact of the combined effects of salt erosion and dynamic water scouring on the short-term performance of asphalt mixtures. In the early phase, dynamic water scouring primarily influences the asphalt composite, augmenting micro-cracks and causing aggregate disintegration. The intermediate phase sees the disintegration of the asphalt membrane by deicing salts, disrupting adhesion between asphalt and aggregate, and progressively leading to the emergence of new micro-cracks. During later stages, dynamic water scouring continues to deteriorate the asphalt composite, building on the erosion instigated by deicing salts, resulting in the expansion of newly formed micro-cracks and further aggregate disintegration. Among the three deicing salts, CH4N2O exhibits the most pronounced impact on short-term water damage, followed by NaCl, while CH2CH3OH has the least effect. Increasing the fine aggregate proportion in the asphalt mixture enhances water damage resistance. The GM (1,N) damage prediction model effectively anticipates air void content and water stability of asphalt mixtures. The research outcomes offer theoretical and technical support for understanding the durability and service lifespan of asphalt pavements in cold regions.

Pull-out failure capacities of cold-formed steel batten screw connections at sub-zero temperatures

Cold-formed steel (CFS) roof and wall cladding systems are used worldwide, and are constructed using high-strength, thin steel battens and claddings. In North American countries, which are frequently subjected to blizzards (winter storms), these cladding systems are particularly vulnerable to pull-out failures during combined windstorm and snowy conditions. Despite extensive research studies investigating the pull-out failure mechanism and the critical parameters of screw connections between cladding and battens at ambient temperature, the behaviour of these connections under blizzard conditions has not been experimentally investigated. To fill this knowledge gap, an experimental study was undertaken that included 144 laboratory tests of screw connections in battens of varying grades and thicknesses, and three distinct types of screws. The pull-out failure capacities of screw connections in battens were evaluated under wind suction loading at four different temperatures, including ambient and sub-zero temperatures. The results of the study provided insights into the pull-out failure mechanisms of screw connections in battens and enabled the development of suitable design rules for combined wind action and sub-zero temperature conditions. The findings of this study are expected to be of significant value to engineers and building designers who are involved in the construction of lightweight cold-formed steel buildings.

IMPACT OF DEICING SALT ON THE PERFORMANCE OF ASPHALT MIXTURES IN NORTHWEST CHINA: AN INVESTIGATION INTO MECHANICAL PROPERTIES AND INFLUENTIAL FACTORS

Currently, the primary form of high-grade highways is constituted by asphalt concrete pavement. Winter conditions often result in ice and snow accumulation on these pavements, precipitating severe traffic incidents. Statistically, around 15%-30% of such incidents are directly linked to icy and snowy conditions. Hence, when roads are laden with ice and snow, the most cost-effective and efficient countermeasure remains the dispersal of deicing salt onto the road surface. Particularly in China's northwestern region, which experiences low precipitation and consistent droughts, the deicing salts applied during winter aren't diluted or transported away by water flow. Consequently, the soil surrounding the roads retains a higher concentration of deicing salts than other regions, leading to a pronounced impact on the pavement's service life. This investigation aims to experimentally emulate the impact of this high-salt environment on the mechanical properties of asphalt mixtures, followed by an analysis of the crucial factors that influence the asphalt mixtures' durability. The current study employs measures such as high-temperature rut testing, Marshall water immersion testing, and freeze-thaw splitting testing to investigate the damage patterns of mechanical properties in asphalt mixtures under varying grading, diverse deicing salt solutions, and differing frequencies of dry-wet cycles. In addition, the study employs grey correlation entropy analysis to ascertain the interdependence among factors influencing the performance of asphalt mixtures. The findings reveal that after undergoing 0, 5, 10, 15, 20, 25, and 30 dry-wet cycles in solutions of 20% industrial salt (NaCl), 15% urea (CH4N2O), and 20% anhydrous ethanol (CH2CH3OH), both the high-temperature stability and water stability of asphalt mixtures with AC-13 and AC-16 gradings displayed varying levels of decline. Overall, an enhancement in the fine aggregate percentage in asphalt mixtures can augment the asphalt concrete's resistance to deicing salt erosion. As per the grey correlation entropy analysis, gradation variances exerted the most significant impact on diverse mechanical properties, followed by the type of deicing salt solution, with the least significant impact attributed to the frequency of dry-wet cycles. Thus, judicious selection of road materials and structural design can effectively counter the erosive action of deicing salts, thereby enhancing the service life of the road surface.

Research on Photovoltaic Carport System Performance

The demand and quantity of photovoltaic carports are increasing rapidly in recent years, and in this study the factors affecting the performance of photovoltaic carport components based on the electricity generation data from a factory in Tianjin, China are analyzed. The research validates that HJT and TOPCon modules outperform PERC double-glass modules and PERC modules in terms of electricity generation efficiency. Practical tests were conducted under various weather conditions such as sunny, cloudy, snowy, and enclosed environments, and the study results demonstrate that under the same low-light conditions, N-type modules exhibit better performance. The comparison of low-light performance among the four module types is as follows: HJT modules > TOPCon modules ≈ PERC double-glass modules > PERC modules. Due to the higher distance between photovoltaic carport modules and the ground, the performance of the bifacial module is significantly affected. In snowy conditions, the cost-effectiveness of PERC double-glass modules in photovoltaic carports is higher than that of TOPCon modules. In sunny and cloudy conditions, HJT modules show a higher increase in electricity generation. This study also examines the impact of enclosing the bottom space of photovoltaic carports on the electricity generation performance of TOPCon modules and PERC double-glass modules, revealing that TOPCon modules experience more power loss. The analysis of the practical operation of the four module types under various conditions are summaried in this paper, which providing valuable insights for the development of future solar cell technologies and the selection strategy of photovoltaic modules in different scenarios.

Multi-supervised bidirectional fusion network for road-surface condition recognition.

Rapid developments in automatic driving technology have given rise to new experiences for passengers. Safety is a main priority in automatic driving. A strong familiarity with road-surface conditions during the day and night is essential to ensuring driving safety. Existing models used for recognizing road-surface conditions lack the required robustness and generalization abilities. Most studies only validated the performance of these models on daylight images. To address this problem, we propose a novel multi-supervised bidirectional fusion network (MBFN) model to detect weather-induced road-surface conditions on the path of automatic vehicles at both daytime and nighttime. We employed ConvNeXt to extract the basic features, which were further processed using a new bidirectional fusion module to create a fused feature. Then, the basic and fused features were concatenated to generate a refined feature with greater discriminative and generalization abilities. Finally, we designed a multi-supervised loss function to train the MBFN model based on the extracted features. Experiments were conducted using two public datasets. The results clearly demonstrated that the MBFN model could classify diverse road-surface conditions, such as dry, wet, and snowy conditions, with a satisfactory accuracy and outperform state-of-the-art baseline models. Notably, the proposed model has multiple variants that could also achieve competitive performances under different road conditions. The code for the MBFN model is shared at https://zenodo.org/badge/latestdoi/607014079.

Finite element analysis of studded tyre performance on snow: a study of traction

This paper presents a finite element analysis (FEA) model of a studded tyre rolling on snow. The model takes into account the mechanical properties of both the tyre and the snow, as well as the interactions between them. The FEA simulations were performed using single set of 99 studs with constant compaction depth, and the results were compared to experimental data. The study found that the studded tyre was able to significantly improve traction on snow-covered roads by a minimum factor of 1.17 at −50% slip and a maximum factor of 1.5 at an interval of 50% to 100% slip, compared to non-studded tyre, and that the FEA model accurately predicted the tyre’s behaviour in these conditions. The results of this study can be used to optimise the design of studded tyres and to improve the safety of vehicles in snowy conditions.

Optimizing species selection for the structural timbers of traditional farmhouses in a snowy rural area of northeastern Japan

AbstractVernacular houses constructed using local woods contain information about forest‐resource use. To clarify how timber was used to construct wooden farmhouses, we identified the timber species composition of 11 houses built between 1845 and about 1940 in Tadami, a snowy rural area in northeastern Japan, and interviewed 70 residents. We recorded 2004 timber parts (99–308 parts per house) and 171.2 m3(1828 parts) of known species (14 taxa).Cryptomeria japonica(cedar) andPinus parvifloravar.pentaphylla(white pine) were used for all essential structural parts and accounted for 44% and 39% of the total timber volume, respectively.Fagus crenata(beech) was the third most commonly used species (7%), for roof supports, beams, and girders. The interviews revealed that the timber was typically sourced from local private or common woodlands within 1 km and that local craftsmen selected and hauled the trees. Considering the region's unique and complex vegetation, as land‐cover distribution is strongly influenced by frequent avalanches, the timber was likely managed within local stands of tall trees, and the most available species would have been cedar (although whether native or planted is unknown), followed by native white pine on mountain ridges around settlements. In comparison, beech stands that could supply sufficient amounts of timber might have been limited. Based on the characteristics of the trees used as house timber in Tadami, we conclude that despite the surrounding harsh, snowy conditions, locally available species of tall trees were historically selected for timbers.

Coexistence of European hares and Alpine mountain hares in the Alps: what drives the occurrence and frequency of their hybrids?

AbstractAs a glacial relict species, mountain hares are adapted to cold and snowy conditions. Conversely, European hares originate from the grasslands of the Middle East and spread from there throughout low‐lying agricultural areas of Europe. Mountain hares and European hares generally occur allopatrically, however, sympatry occurs in some areas. In sympatric areas, introgressive hybridisation poses a threat to the Alpine mountain hare by reducing its genetic integrity. Introgressed individuals can be found in both species but are far more frequent in European hares than in mountain hares. The ecology of hybrids is poorly known in these species. To examine the Alpine mountain hare and European hare populations in the Alps with a particular focus on the occurrence and ecology of their hybrids, we performed molecular genetic analysis of hare faecal samples collected in four study areas in the Alps in South Tyrol for three winters and compared habitat associations of the genotyped samples. We recorded 150 individuals (i.e. 14 hybrids, 25 European hares and 111 Alpine mountain hares). Four introgressed individuals were at levels consistent with F2 hybrids, whereas the others showed an older interspecific gene flow. We found that hybrid faeces tended to be at lower elevations compared to those of Alpine mountain hare but at higher elevations than those of the European hare. The frequency of Alpine mountain hares decreased as the proportion of Alpine grassland increased but was positively correlated with the proportion of dwarf shrub heaths. No effect of vegetation type was found for the frequency of European hares and hybrids. Our results support the widely raised concerns that the European hare, as a generalist, is a strong competitor with the Alpine mountain hare in the Alpine ecosystem in the time of global climate change.

Experimental Measurement and Numerical Simulation on the Snow-Cover Process of Solar Photovoltaic Modules and Its Impact on Photoelectric Conversion Efficiency

The snow falling on the surface of photovoltaic modules tends to reduce the output power. In order to understand the process of snow accumulating on solar photovoltaic modules and reveal the impact of snow accumulation on photovoltaic conversion efficiency, the snow-cover process was simulated on the surface of photovoltaic modules with different tilt angles by computational fluid dynamics (CFD). On this basis, the relationship between the amount of snow and tilt angle was explored. The snow effect of photovoltaic modules on photoelectric conversion efficiency was studied by building a test platform. At the same time, a measurement platform of snow accumulation on photovoltaic modules and photoelectric conversion efficiency was constructed. Through the experiment of the relationship between snow thickness and snow sliding distance and the power generation efficiency of photovoltaic (PV) modules, the influence of snow thickness and snow area on the power generation efficiency of PV modules is discussed. The results show that the larger angle between the photovoltaic panel and the ground is adverse to the accumulation of snow on the panel. When the thickness of snow reaches 1 cm, the power generation efficiency of the entire photovoltaic module reduces to 7.1% of that as normal. At the same time, the sliding of snow on the photovoltaic panel improves the efficiency of photoelectric conversion. Through the analysis of numerical simulation and experimental results, targeted suggestions are made on how to improve the efficiency of power generation for photovoltaic power stations under snowy conditions, which may provide a reference for engineering work.