Civilian Vehicle Research Articles

Spatio-temporal heterogeneity and scenario prediction of influencing factors of transportation carbon emissions in the Yangtze River Economic Belt, China

Abstract Reducing carbon emissions in the transportation sector is a crucial aspect of China achieving its carbon peak and carbon neutrality goals. This study investigates the spatiotemporal differentiation characteristics of carbon emissions from transportation in the Yangtze River Economic Belt. Using the Geographically and Temporally Weighted Regression(GTWR) model to reveal the spatio-temporal heterogeneity of factors influencing transportation carbon emissions. Additionally, the Support Vector Regression(SVR) is trained to predict the carbon emissions reduction potential of transportation under different scenarios. The results showed that: From 2000 to 2021, the transportation emissions of the Yangtze River economic belt showed an overall upward trend. The high carbon emission regions are Jiangsu Province, Shanghai, Zhejiang Province and Hubei Province, and the emission center is located in Hubei Province. The total population, urbanization rate, per capita GDP, carbon emission intensity, passenger turnover volume, and civilian vehicle ownership all have a positive effect on transportation carbon emissions, while energy structure has a negative impact. Moreover, the influence of each factor exhibits significant spatial heterogeneity. Under three scenarios: baseline, low-carbon scenario I, and low-carbon scenario II, transportation carbon emissions in the Yangtze River Economic Belt are projected to peak by 2030. With the application of clean energy and a reduction in population size, low carbon scenario II demonstrates greater potential for carbon emission reduction, with a projected value of 88.552 million tons by 2032.

Atmospheric microplastic input into wetlands: Spatiotemporal patterns, drivers, and unique ecological impacts

Wetlands are major microplastic sinks with a large atmospheric input. However, many details of such deposited atmospheric microplastics entering into wetlands remain unclear, including temporal patterns of input and ecological effects. We monitored the aerial microplastics during four seasons in eleven economically developed cities along the lower reaches of the Yangtze River Basin, China. The average microplastic deposition rate was 512.31 items m−2 d−1, equivalent to an annual contribution of 17.46 metric tons of plastic to the surveyed wetlands with a total area of 1652 km2. These microplastics were predominantly composed of polyamide and polyethylene terephthalate with 61.85 ± 92.29 µm sized pellets, and we obtained similar results for microplastics intercepted on moss in wetlands. Microplastic input varied between wet and dry periods, primarily influenced by wind, rainfall and ozone concentration. Civilian vehicle density and textile industry were the primary socioeconomic factors driving microplastic deposition. Further indoor microcosm experiments revealed that moss phyllosphere bacterial community structure and function were influenced by microplastic abundance and size, exemplifying the unique ecological risks of aerially deposited microplastics to wetlands. These results indicate that mosses and their phyllosphere microbiota could serve as bio-indicators of aerial microplastic characteristics and impacts.

Leveraging Visual Language Model and Generative Diffusion Model for Zero-Shot SAR Target Recognition

Simulated data play an important role in SAR target recognition, particularly under zero-shot learning (ZSL) conditions caused by the lack of training samples. The traditional SAR simulation method is based on manually constructing target 3D models for electromagnetic simulation, which is costly and limited by the target’s prior knowledge base. Also, the unavoidable discrepancy between simulated SAR and measured SAR makes the traditional simulation method more limited for target recognition. This paper proposes an innovative SAR simulation method based on a visual language model and generative diffusion model by extracting target semantic information from optical remote sensing images and transforming it into a 3D model for SAR simulation to address the challenge of SAR target recognition under ZSL conditions. Additionally, to reduce the domain shift between the simulated domain and the measured domain, we propose a domain adaptation method based on dynamic weight domain loss and classification loss. The effectiveness of semantic information-based 3D models has been validated on the MSTAR dataset and the feasibility of the proposed framework has been validated on the self-built civilian vehicle dataset. The experimental results demonstrate that the first proposed SAR simulation method based on a visual language model and generative diffusion model can effectively improve target recognition performance under ZSL conditions.

Pollution Characteristics and Ecological Risk Assessment of Microplastics in the Yangtze River Basin

The Yangtze River, the largest river in China, has not been comprehensively studied for its basin's microplastic pollution status. Therefore, a comprehensive investigation and assessment system of microplastics was developed at the river basin scale to characterize the spatial distribution and composition of microplastics in the Yangtze River Basin in order to analyze their influencing factors and assess their ecological risks. The results showed that the microplastic abundance in the study area ranged from 21 to 44 080 n·m-3, with an average abundance of 4 483 n·m-3. The spatial distribution of microplastic abundance was higher in the tributaries than in the main streams (except the Ganjiang Basin), with the Chengdu of the Minjiang Basin being the tributary area with the highest abundance of microplastics detected. The size of microplastics in the river basin was concentrated in the 0-1 mm range; the shapes were mainly fiber and fragment; and the colors were mainly colored and transparent. Further, introducing the diversity index of microplastics, it was found that both the Simpson index and the Shannon-Wiener index could quantify the diversity of microplastic characteristic composition in the river basin, but there were certain differences in the changing trends between the two. Regression analysis showed that anthropogenic activities were significantly and positively correlated with microplastic abundance (P<0.05), and among the eight anthropogenic activity factors, civilian vehicle ownership and tourism income were the most strongly correlated with microplastic abundance, indicating that transportation and tourism were the main factors influencing microplastic distribution. From the perspective of the potential ecological risk index of microplastics, microplastics in the Yangtze River Basin posed a certain ecological risk, with 68.97% of the area falling within risk zones III and IV, with the ecological risk of microplastics in Taihu Lake warranting more widespread attention.

Rapid Initial Heading Alignment for MEMS Land Vehicular GNSS/INS Navigation System

For the low-cost microelectromechanical system (MEMS) inertial navigation system (INS) and global navigation satellite system (GNSS) integrated system in land vehicular applications, quick and accurate coarse alignment is still a challenge, especially for a host vehicle that moves at low speed under low dynamics. In this paper, we propose an in-motion coarse initial heading alignment algorithm for the low-cost MEMS INS aided by the GNSS, which is specifically designed for land vehicular conditions. The principle of the proposed method is straightforward when considered in the context of trajectory matching: the MEMS can compute a relative vehicle trajectory through a dead reckoning (DR) calculator using the gyro-derived attitude solution and the travel distance, and the GNSS can provide an absolute vehicle trajectory through its positioning solution. The initial heading is then computed by comparing these DR-indicated and GNSS-indicated trajectories. The proposed algorithm is verified using a civilian vehicle, a wheeled robot, and an agricultural tractor under different motion and dynamic conditions. The results show that the initial heading could be quickly determined with an accuracy of 0.25°, 0.6°, and 1.6° at a 98.6% confidence level within 5 s for the car, robot and tractor tests, respectively.

Analysis of PM2.5 spatial association evolution in the Sichuan Basin and its driving factors

The swift urbanization in China during the past two decades heightened the environmental pressure on cities from anthropogenic production and consumption beyond the regional capacity. The Sichuan Basin, situated in southwest China, faced severe air pollution as its unique topography as a basin surrounded by mountains caused frequent temperature inversion and trapped air pollutants. In this paper, we investigated the evolution of spatiotemporal patterns of particulate matter with diameter not greater than 2.5 µm (PM2.5) across eighteen cities in the Sichuan Basin. In addition, a spatial association network was characterized through social network analysis using China’s air monitoring data and relevant socioeconomic data. The results were as follows: (1) A significant reduction in PM2.5 emissions in the Sichuan Basin occurred from 2015 to 2020, but challenges remain: the number of cities with a PM2.5 annual concentration above 35.00 µg/m3 decreased from seventeen to six, and the annual concentration declined from 62.92 µg/m3 to 40.83 µg/m3 in Chengdu and from 55.08 µg/m3 to 32.67 µg/m3 in Chongqing. PM2.5 concentrations were the highest in the winter, around 1.50–2.00 times the annual concentration. (2) The PM2.5 overall network displayed significant spatial association with periodic changes implying that the inter city association strengthened , then weakened, and then strengthened again from 2015 to 2019 despite a sharp drop in the PM2.5 concentration. Network density remained at 0.29 between 2015 and 2019 but increased to 0.30 in 2017, efficiency increased from 0.72 to 0.80 and connectedness from 0.78 to 1.00. A “core edge” pattern explicitly presented that Chengdu and Chongqing were located at the center with degree of 94.40 and 82.35, respectively, while other cities marginalized less than 35.30. (3) The driving factors of network structure were explored by quadratic assignment procedure, which showed that enlarging the difference in gross domestic product gap between secondary and tertiary industries and urban population and narrowing the difference among urban ratio, the number of civilian vehicle and distance between cities efficiently promotes the PM2.5 spatial association in the Sichuan Basin. The dominant factors impacting the PM2.5 spatial and temporal differences were the gross domestic product gap between secondary and tertiary industries, urban population and the number of civilian vehicle identified by quadratic assignment procedure. A synergetic effect among these factors played an important role as the cities with annual concentration PM2.5 &amp;lt; 35.00 µg/m3 corresponded to those with a combination of relatively small value for these driving factors.

Multi-baseline SAR 3D reconstruction of vehicle from very sparse aspects: A generative adversarial network based approach

Reconstruction of anisotropic targets using SAR from multiple observation aspects is an important research direction. Conventionally, to obtain reconstruction of high quality using compressed sensing (CS) or back-projection (BP) methods, it is essential to acquire signals from plenty of aspects. However, limited by the flight trajectory and other factors, only few aspects are available. Besides, massive signal data from abundant aspects brings huge burden in computation. Thus, a new framework to generate 3D reconstruction of anisotropic targets from few aspects with low computational cost is necessary. To tackle this problem, we propose a new framework, which combines conventional CS algorithm and neural network. The CS algorithm is an imaging module to transform data from frequency domain into spatial domain with high resolution in each aspect to generate incomplete outputs. The sparse-aspects-completion network (SACNet) based on GAN principle is originally introduced to predict integral structures from the incomplete results. To evaluate the effectiveness and robustness of our framework, the simulation data (Civilian Vehicle Demo) is used to train while the measured data (GOTCHA) is for validating. Extensive experiments are conducted under condition of {4,6,8,10} aspects to estimate the performance with respect to the number of aspects. The proposed framework achieves the highest IoU and the lowest BCE metrics compared with the conventional algorithms and the SOTA networks used in similar optical tasks in both simulation and measured datasets under various number of aspects, which proves the effectiveness and robustness of our framework. The source code is available at: https://gitee.com/WshongCola/sar_3D_multi_aspect.

Modern structures of military logistic bridges

Abstract The growing paste of military operations, as well as the increased involvement of the Armed Forces in countering the ramifications of natural disasters, impacts the design solutions used for military bridging systems. The most vital optimization factor is the speed of deployment to which all the structural solutions are subject. Another determinant is a wide range of the parameters of the gap to be crossed using one bridging structure transported using typical means of transportation, including its width and the height of the banks. There is a worldwide tendency to fill the space between the treadways, making them more solid and thus much more usable for civilian vehicle traffic as well. That in turn makes them a provisional replacement for the damaged bridge infrastructure used by the population. This poses a number of challenges to be tackled by the state-of-the-art line-of-communication (LOC) bridges and both the close- and general support bridges, which seem to assume the tasks of the LOC bridges.

Optimal Target Classification Using Frequency-Based Radar Waveform Design

In this article, we introduce a frequency-based waveform design to maximize binary target classification. The optimization problems can be solved via optimal solvers available in the literature. The presented formulation is applicable to the classification scenarios where the extended target frequency responses (TFRs) are complex, random, and normally distributed with unequal mean vectors. However, their covariance matrices are either identical or different. For the former, i.e., identical covariance matrices, the optimization problem consists of a cost function that has been obtained by deriving a closed-form expression of the probability of misclassification. For the latter, i.e., different covariance matrices, we present an optimization problem where the cost function has been obtained via the Fisher separation function. For this, we also present a closed-form expression for an approximate solution to the optimization problem. We show that the proposed solution achieves performance levels comparable to the exact solution of the optimization problem obtained via state-of-the-art optimization solvers while incurring low computational complexity. We expand on the two main scenarios by introducing clutter (i.e., signal-dependent interference) in the signal model and studying possible closed-form designs in extreme waveform energy levels. Simulations are conducted using synthetically generated data in addition to the civilian vehicle data from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, in order to validate the proposed method.

Two-Stream Deep Fusion Network Based on VAE and CNN for Synthetic Aperture Radar Target Recognition

Usually radar target recognition methods only use a single type of high-resolution radar signal, e.g., high-resolution range profile (HRRP) or synthetic aperture radar (SAR) images. In fact, in the SAR imaging procedure, we can simultaneously obtain both the HRRP data and the corresponding SAR image, as the information contained within them is not exactly the same. Although the information contained in the HRRP data and the SAR image are not exactly the same, both are important for radar target recognition. Therefore, in this paper, we propose a novel end-to-end two stream fusion network to make full use of the different characteristics obtained from modeling HRRP data and SAR images, respectively, for SAR target recognition. The proposed fusion network contains two separated streams in the feature extraction stage, one of which takes advantage of a variational auto-encoder (VAE) network to acquire the latent probabilistic distribution characteristic from the HRRP data, and the other uses a lightweight convolutional neural network, LightNet, to extract the 2D visual structure characteristics based on SAR images. Following the feature extraction stage, a fusion module is utilized to integrate the latent probabilistic distribution characteristic and the structure characteristic for the reflecting target information more comprehensively and sufficiently. The main contribution of the proposed method consists of two parts: (1) different characteristics from the HRRP data and the SAR image can be used effectively for SAR target recognition, and (2) an attention weight vector is used in the fusion module to adaptively integrate the different characteristics from the two sub-networks. The experimental results of our method on the HRRP data and SAR images of the MSTAR and civilian vehicle datasets obtained improvements of at least 0.96 and 2.16%, respectively, on recognition rates, compared with current SAR target recognition methods.

Single Target SAR 3D Reconstruction Based on Deep Learning.

Synthetic aperture radar tomography (TomoSAR) is an important 3D mapping method. Traditional TomoSAR requires a large number of observation orbits however, it is hard to meet the requirement of massive orbits. While on the one hand, this is due to funding constraints, on the other hand, because the target scene is changing over time and each observation orbit consumes lots of time, the number of orbits can be fewer as required within a narrow time window. When the number of observation orbits is insufficient, the signal-to-noise ratio (SNR), peak-to-sidelobe ratio (PSR), and resolution of 3D reconstruction results will decline severely, which seriously limits the practical application of TomoSAR. In order to solve this problem, we propose to use a deep learning network to improve the resolution and SNR of 3D reconstruction results under the condition of very few observation orbits by learning the prior distribution of targets. We use all available orbits to reconstruct a high resolution target, while only very few (around 3) orbits to reconstruct a low resolution input. The low-res and high-res 3D voxel-grid pairs are used to train a 3D super-resolution (SR) CNN (convolutional neural network) model, just like ordinary 2D image SR tasks. Experiments on the Civilian Vehicle Radar dataset show that the proposed deep learning algorithm can effectively improve the reconstruction both in quality and in quantity. In addition, the model also shows good generalization performance for targets not shown in the training set.

Uncovering impact factors of carbon emissions from transportation sector: evidence from China’s Yangtze River Delta Area

Transportation sector contributes a significant proportion to the overall carbon emission. This paper aims at measuring the impact factors of the transportation sector’s carbon emission in China’s Yangtze River Delta Area (YRDA) so that mitigation strategies on promoting low-carbon transportation can be raised. The partial least squares method and an extended STIRPAT (stochastic impacts by regression on population, affluence, and technology) model were employed for quantifying the contributions of different impact factors that affect transportation carbon emission within the YRDA region for the period of 1995–2014. Results show that population size, GDP, civilian vehicle inventory, energy intensity, passenger transportation, freight turnover, and transport sector output are key factors inducing transportation carbon emission, while energy structure and transportation sector employees mitigate the overall transportation carbon emission. Such results provide valuable policy implications for preparing appropriate mitigation strategies, such as the optimization of energy structure, the development of energy efficient technologies, the improvement of public awareness, and the implementation of intelligent transportation management.

Evaluation of traffic behavior in response to alternative police lighting

More than 1500 U.S. law enforcement personnel fatalities occurred from 2007 to 2016, with 39 % of these related to automobile crashes. This study looked at various types of lighting on police vehicles to determine if changes made to the visibility of a police vehicle can impact the surrounding traffic behavior and increase safety for both law enforcement and the general public. Unmarked and marked police vehicles were positioned behind a civilian vehicle on the shoulder of five different multi-lane highways in Virginia, simulating a routine traffic stop. The data collected indicated that more lighting and the use of red in a light bar impact traffic behavior in terms of merging and speed when passing a police vehicle. The benefits may be attributed to the symbolic influence of red as denoting a different type of emergency than a traffic stop in addition to red’s chromatic contrast against the blue sky during the daytime.

Radar probabilistic data association filter/DGPS fusion for target selection and relative range determination for a ground vehicle convoy

As navigation systems are being developed, it is apparent that cost-effective accurate and precise positioning is an imperative for both military and civilian ground vehicle guidance. As this need increases, navigation systems incorporating multiple sensors have been developed and relied upon in many navigation situations. In this work, radar and global positioning system (GPS) measurements are utilized in a multi-sensor fusion scheme allowing for a robust ranging solution utilizing the accuracy of a differential GPS (DGPS) solution and higher update rate of the radar solution in a Kalman filter. A probabilistic data association filter (PDAF) is utilized to determine a weighted mean of the radar channels' solutions that fall within a validation region set in the algorithm. In this work, radar was used to track vehicles and not surrounding landmarks. This paper intends to evaluate the accuracy and availability of a GPS/radar fusion algorithm in vehicle convoying scenarios.

Modeling Metropolitan-Area Ambulance Mobility Under Blue Light Conditions

Actions taken immediately following a life-threatening personal health incident are critical for the survival of the sufferer. The timely arrival of specialist ambulance crew in particular often makes the difference between life and death. As a consequence, it is critical that emergency ambulance services achieve short response times. This objective sets a considerable challenge to ambulance services worldwide, especially in metropolitan areas, where the density of incident occurrence and traffic congestion are high. Using London as a case study, in this paper, we consider the advantages and limitations of data-driven methods for ambulance routing and navigation. Our long-term aim is to enable considerable improvements to their operational efficiency through the automated generation of more effective response strategies and tactics. A key ingredient of our approach is to use a large historical dataset of incidents and ambulance location traces to model route selection and arrival times. Working on the London road network graph modified to reflect the differences between emergency and civilian vehicle traffic, we develop a methodology for the precise estimation of expected ambulance speed at the individual road segment level. We demonstrate how a model that exploits this information achieves best predictive performance by implicitly capturing route-specific persistent patterns in changing traffic conditions. We then present a predictive method that achieves a high route similarity score while minimising journey duration error. This is achieved through the combination of a technique that correctly predicts routes selected by the current LAS navigation system and our best performing speed estimation model. This hybrid approach outperforms alternative mobility models. To the best of our knowledge, this paper represents the first attempt to apply a data-driven methodology for route selection and the estimation of arrival times of ambulances travelling with blue lights and sirens on.

Mobile test stand for evaluation of electric power plants for unmanned aircraft

The absence of accurate performance data is a common problem with most civilian unmanned aerial vehicle (UAV) power plant producers. The reasons for this are the small size of most of the manufacturers and the high price of precise wind tunnel testing and computer simulations. To overcome this problem at Dronamics Ltd., with support from the Department of Aeronautics of TU-Sofia, a mobile test stand for evaluation of electric power plants for unmanned aircraft was developed. The stand may be used statically, or may be installed on the roof of an automobile. The measurement system of the stand is based on popular hardware that is used in radio controlled models and in general automation. The verification of the measurement system is performed by comparing static test results with data published by the manufacturer of the tested electric motor. Tests were carried out with 2 different types of propellers and the results were compared with published results for common propellers as well as with results of theoretical studies. The results are satisfactory for practical applications. The use of this type of test stands can be a cheap and effective alternative for research and development start-up companies like Dronamics.

Performance prediction of quantized SAR ATR algorithms

Automatic target recognition (ATR) of synthetic aperture radar (SAR) target chips is a difficult problem, complicated by the number of nuisance parameters typically present in SAR imagery. This also complicates performance analysis because fully sampling the space of nuisance parameters in an evaluation dataset is intractable. ATR algorithms that first quantize pixel intensity values have been shown to be effective for SAR ATR due to hypothetically reducing the sensitivity to these nuisance parameters. Here we study the performance of two such algorithms, multinomial pattern matching and quantized grayscale matching, and compare them with the traditional mean squared error (MSE) template matching based classification approach. Our approach is to approximate the decision statistic of each algorithm as a Gaussian random variable (RV) parameterized by the noise power, or alternatively signal-to-noise ratio (SNR). This allows the analytic prediction of algorithm performance when the noise process of test images differs from that of the dataset used to train each algorithm, without having to rely on costly empirical simulation. We verify our results in simulations utilizing the AFRL Civilian Vehicle dataset.

China’s electric car frustrations

By 2020, the vehicle population in China will likely exceed 280million—exacerbating national energy security, urban air pollution, and traffic congestion. In response, many local and regional governments in China are pursuing an expanding array of measures to restrain growth in personal vehicle ownership and, along with the central government, reducing emissions and energy use of vehicles. One prominent strategy is the promotion of new energy vehicles, especially plug-in electric vehicles (PEVs). Large subsidies were offered—up to $27,600 (171,000RMB) per vehicle in some regions, including almost $9200 (57,000RMB) from the central government—which suggests that China is making a major commitment to PEVs. But sales have been meager. In 2013, only 17,600 PEVs, mostly buses and utility trucks, were sold, less than 0.1% of total civilian vehicle sales. Several factors explain the failure of PEV sales to take off: (1) protectionism by local governments; (2) uncertainty over which electric-drive vehicle technologies to promote and what consumers are willing to pay, (3) lagging investments in charging infrastructure, and (4) conservative investment behavior by automakers and battery manufacturers. The central government issued directives to local governments in late 2013 to reduce barriers to out-of-town companies, resulting in modest sales increases in early 2014, but a more coherent, broader, and effective set of policies, incentives, and strategies are needed to overcome consumer and industry resistance and the lack of charging infrastructure.

Research on tourism development of Xinjiang based on grey relational analysis and gravity model

Purpose This paper attempts to apply grey correlation analysis method and gravity model to solve the research on Xinjiang tourism resources strategic development , which can be viewed as an important part of the problems of Xinjiang tourism economic development. Design/methodology/approach Starting from the fact that the unbalanced development of local tourism economy often appears in Xinjiang tourism economic development.On the basis of the theory of grey relational analysis of Ju-long Deng and the theory of gravity model of Smith D and Haggett P, this paper systematically utilizes the grey relational analysis to analyze the key factors affecting tourism economy in Xinjiang ,and apply gravitational model to analysis tourism impact degree Between different areas in Xinjiang. Findings The results show that different areas of Xinjiang are in the oasis belt, the economic level is not balanced, effects of passenger volume and civilian vehicle number of tourism economy is relatively large. In addition, the stu...

Rethinking vehicle classification with wide-angle polarimetric SAR

Wide-angle SAR presents an opportunity to explore new methods for CV classification. Polarization features are of particular interest and various examples of wide-aperture images using flight-collected and simulated data are shown to emphasize how odd and even reflectors are revealed in the imagery. Using the well-known frequency parameter from high-frequency scattering models and the Krogager polarization decomposition, wide-angle polarimetric SAR imagery is attributed with a basis of ASC primitive targets. We observe trends when extracting ASCs from 2D images and study the behavior for 10 civilian vehicles using the AFRL Civilian Vehicle Data Dome. Passenger cars categorized as the sedan class tended to exhibit scattering by cylinders, horizontal edges, and horizontal dihedrals at low elevation angle (30°) but primarily showed horizontal cylinders and horizontal dihedrals for the case of high elevation angle (60°). Likewise, the dominant scatterers for the SUV class were horizontal cylinders and horizontal edges for 30° elevation and primarily horizontal cylinders and dihedrals for 60° elevation. These results suggest that there are noticeable differences between the classes when viewed as primitives in the joint frequency/polarization/wide-angle SAR feature space. Thus, rethinking vehicle classification to include efficient primitive type extraction during image formation may lead to new, near real-time vehicle classification algorithms.