High aspect ratio (HAR) ultrafine tapered holes  are the most important elements for some high-tech perforated metallic products, but they are very difficult to manufacture. Therefore, this paper proposes a nontraditional over-growth electroforming process. The formation mechanism of the HAR ultrafine tapered holes is investigated, and the factors controlling the geometric shape evolution are analyzed numerically. It was found that the geometric shape and dimensions of the holes are highly dependent on the diameter and thickness of the photoresist film patterns, but are hardly affected by the spacing between two neighboring patterns; the achievable diameter for a given hole depth becomes small with the increasing pattern diameter, but it becomes big with the increasing pattern thickness. These correlations can be well interpreted by the established two empirical equations that characterize the relationship between the minimum orifice of the tapered hole and the structural parameters of the photoresist film patterns previously formed on the substrate. Application of the fabricated 1500 tapered holes with 3-μm diameter and 17-AR as the nozzles of the medical precision nebulizer is also examined. The studies show that the over-growth electroforming process is highly applicable in fabricating the perforated metallic plate with HAR ultrafine tapered holes. Tapered microholes are frequently regarded as the crucial elements for some high-tech perforated products, such as inkjet printing nozzle plates, filtration media, printing nickel screens, surface-mount technology stencils, shaver foils, nebulizer plates, etc. Thus, these holes are required to have well-defined geometric shapes, dimensional accuracies, and surface quality to favorably meet their application requirements. However, these requirements are very difficult to be simultaneously met during manufacturing, especially for the high aspect ratio (HAR) ultrafine holes  such as medical nebulizer nozzles, which need to have well-defined tapered or conical geometric profiles. Up to now, several techniques have been exploited to fabricate these specially shaped holes: jet electrochemical machining , chemicaThe evolution mechanisms of the features formed by over-growth electrodeposition processes have been investigated for some other applications. Yang et al.  fabricatTherefore, it can be deduced from the formation procedure of the tapered hole by the over-growth electroforming process that the resulting geometric shape and dimensions of the hole could be highly dependent on the thickness and area of the film patterns, the deposit thickness, and the spacing between two neighboring film patterns. To make the tapered holes with the desired geometric shape and high tolerance dimensions, the above-described structural parameters of the film patterns need to be carefully designed. To facilitate numerical simulation of the metallic deposit growth during through-mask electroforming and the over-growth electroforming process, a two-dimensional (2-D) model was developed, as shown in the potential of each electrode surface is equal, ignoring the boundary effect;the electrolyte is isotropic and its conductivity is constant in the enclosed area Ω;regardless of electrochemical polarization and concentration polarization;the electric field between the electrodes approximates to a steady electrostatic field at a special moment.To simplify the calculation and simulation operations but without loss of generality, the following assumptions were made : the potX and Y are the location along the x-axis and y-axis in the interelectrode gap, respectively.According to the electric field theories, the electric potential (Ø) distribution in the domain (Ω) is governed by Laplace’s equation:The boundary conditions for electrodeposition are:κ is the conductivity of the electrolyte solution and E is the electric field strength.Based on the above four equations, the electric field distribution in Ω can be obtained. Moreover, the current density, J, at a specific point, which can be calculated from the field Ω, is stated as Ohm’s law,v, is expressed as3/(A·s)).Then, the instantaneous electrodeposition velocity, P0  at the cathode surface at the initial time of the electrodeposition process (t = 0), after a period, iΔt, of electrodeposition, the point P0  moves to the point iP  and then moves to the point iP+ 1  after a time step, Δt. The coordinate relationship between iP and iP+ 1 can be expressed asxE and yE represent the electric field strength along the x-axis and y-axis, respectively.An orthogonal coordinate system is established in which the origin of the coordinates is set to be at the midpoint of the two neighboring patterned films. Set the point iP  at any time can be obtained. Therefore, if all the concerned points iP  at a given time are calculated and then are connected together, the profile of the deposit at that time will be achieved. According to the superposition principle of electrodeposition, if the original shape of the cathode substrate is given, the profile of the electrodeposit at any moment can be achieved by simulations. The simulations were stopped when the minimum diameter of the aperture reached the desired value. To understand the mechanisms and obtain the optimal formation parameters of the aperture by electrodeposition, numerous simulations were carried out by changing some of the key parameters . In the simulation, the required electroforming thickness was obtained by adjusting the electroforming time. The main parameters needed for the simulation operations are listed in Based on Equation (7), the point An SUS 304 stainless steel plate  was used as the substrate on which insulating photoresist film patterns  were previously prepared using a standard photolithography process. The electrolyte compositions and electrodeposition process parameters are listed in The geometric profile of the electroformed tapered hole was obtained by fitting the data, which were measured by the tool measuring microscope . As shown in Determine the whole depth, H, of the formed hole, and then divide it into n (n = 6–8) parts.Along the depth direction of the hole, define n + 1 measuring planes which are parallel to the substrate surface, and the first measuring plane is designated as the surface of the deposit contacting directly the substrate.Acquire the coordinate values of at least five different points that are located at the measuring circle formed by transecting the hole with the measuring plane, and then fit the values into a circle based on the least square method, thus calculating the diameter of each fitted circle.Form the three-dimensional geometric profile by connecting all the above fitted circles.Here, we define the orifice of the hole that contacts with the substrate during electroforming as the outlet of the tapered hole, and the other orifice as the inlet.Scanning electron microscopes  and a digital microscope  were employed to characterize the features of the formed apertures.The electroformed ultrafine tapered holes show a well-defined three-dimensional geometric profile whose average outlet diameter is about 3 μm and average inlet diameter is approximately 50 μm, as shown in outlet diameterY, and the film pattern thickness, pattern thicknessX, is given as follows:As shown in From this formula, one can determine the required film pattern thickness according to the designed outlet size of the nebulizer nozzles.Comparing the simulation results with the experimental results, some differences can be found. The real outlet of the tapered hole is a bit larger than the simulated one. This is because in the simulation, the current efficiency in the nickel electrodeposition was assumed to be 100%, while in the real electroforming process, it is smaller than 100%. As a result, under the conditions where the electrodeposition time and the current density are both constantly given, a higher current efficiency generally means that more metal is deposited, leading to a smaller outlet. Another obvious difference between them is that in the simulated geometric shape, a humped deposit can be observed surrounding the inlet of the tapered holes, while it almost disappears in the electroformed ones regardless of the thickness of the film patterns used, as shown in The variation of the geometric shape of the tapered hole with the film pattern diameter was shown in outlet diameterY is the outlet diameter of the tapered hole and pattern diameterX is the film pattern diameter. It can be seen from this formula that in order to achieve an outlet as fine as possible, a small film pattern diameter should be selected. It was also shown that the simulated geometric shape of the tapered hole is very similar to the experimental one, but in general, the electroformed hole is a bit bigger than the simulated one, which is mainly because the real electrodeposition rate is smaller than that assumed in the simulation.As for the outlet of the tapered hole, the relationship between its size and the film pattern diameter applied can be expressed as an empirical formula by fitting the measured dataTo evaluate the atomization effect of the nebulizer plate electroformed with the optimized process conditions and parameters, atomization tests were carried out. As shown in To fabricate desirable HAR ultrafine tapered holes for some applications, a nontraditional over-growth electroforming process was proposed. The evolution of the geometric shape of the holes being formed was investigated numerically and experimentally, and the correlations between the outlet size of the tapered hole and the key formation parameters were established. The application effect using the electroformed HAR ultrafine tapered holes as the medical nebulization nozzles was evaluated. Some conclusions were made as follows.(1)The geometric shape and dimensions of the tapered holes are highly dependent on the formation parameters, including the film pattern thickness, film pattern diameter, and metallic deposit thickness, but are hardly affected by the spacing between two neighboring film patterns.(2)outlet diameterY = 2.93 + 0.6pattern thicknessX.The outlet diameter of the tapered hole becomes smaller with reducing the film pattern thickness when the metallic deposit thickness is kept constant, and these two parameters show a linear relation: (3)outlet diameterY =-5.95 + 0.315pattern diameterX.The outlet diameter of the tapered hole increases with the increasing film pattern diameter when the metallic deposit thickness is kept constant, and these two parameters also show a linear relation: (4)The over-growth electroforming process is highly effective and efficient in fabricating a perforated metallic plate with HAR ultrafine tapered holes.


Anomaly detection is an important task in hyperspectral processing. Some previous works, based on statistical information, focus on Reed-Xiaoli (RX), as it is one of the most classical and commonly used methods. However, its performance tends to be affected when anomaly target size is smaller than spatial resolution. Those sub-pixel anomaly target spectra are usually much similar with background spectra, and may results in false alarm for traditional RX method. To address this issue, this paper proposes a hierarchical RX (H-RX) anomaly detection framework to enhance the performance. The proposed H-RX method consists of several different layers of original RX anomaly detector. In each layer, the RX’s output of each pixel is restrained by a nonlinear function and then imposed as a coefficient on its spectrum for the next iteration. Furthermore, we design a spatial regularization layer to enhance the sub-pixel anomaly detection performance. To better illustrate the hierarchical framework, we provide a theoretical explanation of the hierarchical background spectra restraint and regularization process. Extensive experiments on three hyperspectral images illustrate that the proposed anomaly detection algorithm outperforms the original RX algorithm and some other classical methods. Hyperspectral imagery (HSI) records detailed spectral information in hundreds of narrow contiguous bands over the visible light spectrum to the Near Infrared (NIR) or Short-Wave Infrared (SWIR) bands. Each HSI pixel is a vector that represents the radiance or reflectance value of each band; therefore, HIS can be considered a 3-D cube. Such data provide deterministic spectral information about different materials and objects in the scene. Benefitting from this unique characteristic, HSI is becoming a valuable tool for many real-world applications, such as agriculture surveillance ,2, minerAnomaly detection in HSI can be regarded as a particular case of target detection that assumes no prior knowledge of the target’s spectral signature . The maiThe most widely used statistical-based anomaly detection method is the RX anomaly detection algorithm, which is also considered a benchmark among most classical anomaly detection algorithms . The RX Another anomaly detection class consists of low-rank or sparse matrix decomposition-based methods. The RPCA method separates the original data matrix into a low-rank matrix and a sparse matrix by two simple constraints. The LSMAD method combines the RPCA and RXD, and then obtains a low-rank background matrix and calculates the Mahalanobis distance according to the obtained low-rank components. For the LRASR anomaly detection method , a backgIn this paper, instead of adding or improving sparse constraints directly as in LRASR and RPCA, we build a new hierarchical architecture to restrain the background spectra while preserving the anomaly target spectra. A simple and effective method, i.e., the hierarchical-RX (H-RX) method, is proposed for improving the performance of classical RX algorithms and achieving better anomaly detection performance, especially for sub-pixel anomaly detection tasks.Since the classical RX anomaly detector in certain situations cannot accurately accentuate the anomaly targets and restrain the backgrounds, we use a hierarchical framework to solve this problem by processing the hyperspectral data with several layers, and the RX anomaly detectors of different layers are linked in series. After each layer’s anomaly detection, a nonlinear function is used to restrain the background spectra based on the results of the RX detector. Then, the adjusted spectral data are organized and sent to the next layer until the RX detector’s result meets the iteration stop condition. Restraining the background of spectral components facilitates modeling their distribution and leads to more significant anomaly spectra. In this approach, the RX performance is gradually enhanced layer by layer. The contributions of our work are summarized as follows.(1) A novel hierarchical framework for I anomaly detection is proposed. It restrains the background spectra and highlights the anomalies by multilevel processing.(2) We prove the rationality of the proposed method theoretically. After several layers of background restraints, the anomaly detection performance is gradually enhanced in theory, especially for sub-pixel anomaly detection tasks.(3) Experimental results on three hyperspectral images show that the proposed method significantly enhances the traditional RX method and outperforms the other improved classical RX-based anomaly detection algorithms.The remainder of this paper is organized as follows. In this section, we first review the original RX anomaly detection algorithm. Then, we propose the hierarchical-RX method.The RX anomaly detection algorithm is one of the most fundamental and popular algorithms for anomaly detection tasks, and it has been considered the benchmark of anomaly detection fIHSI . The RX In the RX algorithm, anomaly detection is formulated as two hypotheses, Therefore, all spectra of this hyperspectral image can be arranged in an In addition, this formula can be simply defined as:Here,  measure .Due to the equivalence mentioned above, influence factors on the RX performance can be identified from the perspective of Mahalanobis metrics. The larger Mahalanobis distance indicates the spectra belongs to anomaly category with a higher probability. That is, it greatly depends on the dissimilarity between the anomaly spectra and the average one. Considering the facts that anomaly spectra are sparse among background, and background spectra are not always same, since of the same object with different spectra phenomenon, we can thus infer that the metric is affected by two aspects: (1) Difference between anomaly and average spectra; (2) variation of background spectra.Such kind of factors would have more impact on the sub-pixel anomaly detection with small ratio. In general, it is intractable to discriminate legitimate anomalies from detections that are not of interest, since of the deficiency of prior knowledge on the anomaly type . SpecifiGiven aforementioned analysis, one of feasible strategies to improve the original RX is enhancing discriminability between anomaly spectra and background ones. This is also the motivation of the proposed hierarchical anomaly detection framework. But unlike the iterative methods ,17, whicIn this paper, we propose that a restraint on the background spectra is helpful for anomaly detection problems. In addition, regularization on the anomaly detection result is helpful for pixel/sub-pixel anomaly detection tasks. The major characteristics of the proposed hierarchical framework can be summarized as follows.(1) The original RX detector can be considered a single-layer detector, whereas the proposed H-RX detector consists of several layers of traditional RX detectors, and each layer is linked in series.(2) After each layer of detection, the background spectra are restrained based on the current layer’s anomaly score. In this approach, anomaly spectra become more distinguished from the background spectra, which implies that the H-RX method can achieve a better performance in the same situations to which the original RX detector is applied. (3) To enhance the result for pixel/sub-pixel anomaly detection, a regularization method based on point spread characteristics is designed to eliminate the effects between the anomaly spectra and background spectra around anomaly pixels.The flowchart of the proposed hierarchical-RX anomaly detection method is shown in For the former phase, a background suppression layer is developed to restrain the background spectra of the input hyperspectral data and a stop criterion layer is designed to determine whether the data need further detection processing. For the anomaly detection result regularization phase, the result regularization layer is tactically designed based on the point spread characteristics, and it is designed to enhance the anomaly detection result.Consider HSI with In the formulation, the nonlinear function The potential anomaly spectra and the transformed background spectra will be used to construct the new input data for the anomaly detector in the In the Equation (9), on (PSF) ,19-basedThe spectral mixing model for a sub-pixel target is a difficult and complicated modeling task. We consider spectral mixing to have an effect on the detection result; therefore, we design the regularization method in the spatial domain to enhance the anomaly detection performance. In this regularization method, we modeled the radiation character of the anomaly target by using the PSF as follows:In the Equation (10), At this point, the H-RX is complete. Briefly, the outline of the proposed H-RX algorithm for hyperspectral anomaly detection is given as follows.Algorithm 1 Hierarchical-RX AlgorithmInput and Initialization: 1. Spectral matrix Hierarchical Background Spectral Restraint: 2.  3.  4. Rebuild data  5. Stop Criterion: 6. Anomaly Regularization: 7. Output: 8. Final outputs: Here, we provide theoretical analyses of the proposed H-RX algorithm. Firstly, a detailed description of our designed background spectral suppression layer is provided. Secondly, the stop criteria layer is analyzed. Finally, the motivation and function of the spatial regularization layer is clarified.Here, we provide a detailed description and theoretical analysis of the background spectral suppression layer. For the Because Based on this relationship, the spectral result between the For the background spectral results, each spectral vector As a result, the output of the background spectrum will be restrained to a small value, but the output will be much larger for the anomaly spectrum. In the ideal situation,After performing background spectral suppression layer processing, we have the following:After several layers of restraint operations, the background spectral suppression results in the vector containing only the value 0 and only the anomaly target spectrum retaining its original vector value. (1)The output of the H-RX algorithm will be restrained to a small constant for the background spectrum, whereas this constant will generally retain the original value for the anomaly target spectrum.(2)This layer can increase the difference between the target spectrum and background spectrum.(3)Because the background spectrum is restrained to a zero vector, the sparsity of the rebuilt data will be significantly increased.(4)Although the H-RX algorithm contains several RX detectors, as the data sparsity increases, the calculation speed of each layer with the exception of the first layer will increase.The background spectral suppression layer has the following characteristics.The abovementioned analysis indicates that the performance of the H-RX detector of the (1)The background spectral magnitude is reduced while its direction in the spectral space is maintained, and after several ranges of suppression operations, it will be transformed close to a zero vector.(2)Concurrently, the target spectra remain unchanged.Based on the analysis of the background spectral suppression layer, we know that after several layer restraint operations, the spectra in the hyperspectral image contains the following characteristics:Then, after a range of suppression and detection, the spectral vector in the transformed hyperspectral image is almost restrained to a zero vector and only the anomaly target spectra contain the original spectral vector. As a result, the detection results will contain a small value (close to zero) that corresponds to the background spectra, whereas a much larger value is observed for the anomaly target spectra.In the hierarchical framework, if there is only one anomaly target, the output energy of the current layer can be formulated as follows:Based on these equations, we can calculate Thus, we have:Based on the above analysis, Then, the stop criteria layer can be described as a low-pass filter like In the whole H-RX algorithm process, the stop criteria later plays the important roles of determining when to stop the detection and identifying whether a potential advantage still exists to improve the anomaly detection performance.Before the final detection result is output, we design a regularization layer to enhance the detection performance in the spatial domain. In HSI, the background spectra around the target may be influenced by the target spectra, which prompts consideration of the similarity of the background spectra with the target spectra. As a result, these background pixels have a similar or larger value than the target in the detection result, which leads to false detection. In this paper, we focus on achieving a better anomaly detection performance for pixel/sub-pixel anomaly targets. To achieve this goal, the noise caused by pixel-mixing phenomena should first be smoothed. Therefore, the key issue of the filtering module is determining how to distinguish the target and noise. Here, we introduce a key technique in infrared target detection systems reported in the literature , and theBased on the assumption that the model of pixel-mixing phenomena influences the detection result, we define the following formula:Considering the task of single pixel target detection and the above radiation character of the target, a point spread indicator is proposed to protect the potential target and combined with a median filter to smooth the noise. As shown in In the formulation, Thus, we have the following:If the center pixel is an anomaly target, the value of this pixel and the surroundings will be obtained from Equation (26), whereas a misleading target will not be obtained. To distinguish the target and misleading target effectively, the point spread indicator The indicator In this section, we evaluate the proposed method on three hyperspectral images. In the experiments, we compare our H-RX algorithm with commonly used anomaly detection algorithms: Original RX, Kernel RX, RPCA and low-rank and sparse representation (LRASR). The former algorithm is a classical anomaly detection algorithm, which is also considered a benchmark among the most classical anomaly detection algorithms. Kernel RX is one of the most famous of the improved algorithms for RX. Another two algorithms are recently proposed low-rank-based anomaly detection algorithm. It may be a stretch to try and draw too much from a comparison of four algorithms and only three images, however, we believe our experiments point to the clear potential of the proposed framework.To compare the performance of different algorithms, we demonstrate the algorithms based on receiver operating characteristic (ROC) curves, which describe the varying relationship of the detection probability and the false alarm rate and are used to provide performance comparisons of different detectors ,22. Baseve (AUC) ,24, whicIn this paper, we evaluate the performance of the proposed H-RX method on three hyperspectral images. Moreover, for the purpose of verifying the method more objectively and comprehensively, we select datasets that were obtained from different hyperspectral imaging cameras. http://aviris.jpl.nasa.gov/data/get_aviris_data.html.), where the index of these two datasets are f100830t01p00r19 and f131117t01p00r05, respectively. Due to the large size of the original data, we select two images for the experiments. The upper-left corner of each image is  and , respectively.The first two datasets are collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over ocean regions ,26. The https://www.erdc.usace.army.mil/Media/Fact-Sheets/.). Here, 162 bands remained after removing the water absorption bands.The third dataset is the most commonly used one for hyperspectral anomaly target detection. It is collected by the Hyperspectral Digital Imagery Collection Experiment (HYDICE) from a land cover region , which iThe descriptions of each dataset are shown in The proposed hierarchical anomaly detection algorithm is composed of several layers with different purposes. The most important and novel one is the background suppression layer, which significantly enhances the original RX detection performance. Then, to prove the theory and real effect of this layer, we use a part of the data region in Dataset-1 for a detailed analysis.In this experiment, the ship spectra have been used as the target spectra. We select the seventh patch from the first dataset as the background region  Regards the performance of the proposed background suppression layer, (2) considers whether rebuilding the hyperspectral information is useful for enhancing the anomaly detection performance.For the first question, the difference between the anomaly target and background according to the previously mentioned theoretical analysis will remarkably increase after several background suppression layer processing steps. Then, to verify this phenomenon, the detection results of the original RX with the restraint process applied once and twice were compared directly.As illustrated in Moreover, to quantitatively analyze the background suppression performance, the distance between the value of the anomaly target and the background with different process steps was also calculated with following formula, and the results are illustrated in As shown in For the second problem, as mentioned in Subjectively, the spectra of the original data and the rebuilt data are compared directly in To compare the difference between the sub-pixel anomaly target and the background spectra objectively, we introduce two spectral distance metrics to measure the distance among these spectra. The first distance metric we used is the spectral angle mapper (SAM) , which dThe spectral difference between two spectra We use the distance mentioned earlier to calculate the distance between the anomaly target and the average background spectra. In the ideal situation, the average background spectra may suppress the spectral vector close to the zero vector. To avoid this situation, we add a small positive value to each band value in the average background spectra. We set As shown in We evaluate the proposed method using three hyperspectral datasets, including two pure ocean surface and one relatively complicated land cover scene. For the first two datasets, we use Target Implantation Method mentioned in  to generFor the third dataset, there already exists 21 pixels marked as anomaly targets, and thus we do not add any other synthetic targets in this scene. As we calculated with the linear unmixing method , the ratFor space and brevity, only the patches involving the target are illustrated in the first two datasets . As shown in In the proposed method, two hyper parameters, including iteration numbers and the size of spatial regulation windows are illustrated in All the five methods have the ability to distinguish between anomalies and most background pixels on three datasets. But the proposed hierarchical method and Kernel RX show better visual performance than the other three algorithms. Considering the fact that the visual assessments may be inaccurate, we employ the semilog ROC curves and AUC score to further assess the detection performance.In order to evaluate the performance in the low false alarm situation more clearly, we calculate the probability of detection when the false alarm rate equals 0.01. Additionally, we also calculate the false alarm rate when probability of detection equals 1. Here, we test all the patches and calculate the mean for the first two datasets. For the third dataset, we repeatly conduct the experiments 10 times, and then calculate the mean and standard deviation of the results. These resulting statistics are illustrated on We can see that the proposed method achieves the top two probability of detection  in all datasets. It proves that the proposed method has a good ability to distinguish most of anomalies with a low false alarm rate. When the probability of detection equals 1, the proposed method gets the second lowest false alarm rate to detect all the anomalies. Overall, our method shows better performance on detecting all the targets and getting higher probability of detection.To further evaluate the efficiency of the proposed method, the AUC score and time consumption are listed from We test all the discussed methods on an Z820 Workstation with Intel Xeon E5-2630 and 128 GB RAM. The programming environment is MATLAB 2015a. Compared with the Original RX, the proposed hierarchical framework consists of different layers, which increases computational complexity to some extent. As illustrated in , the comIt can be seen that the proposed method achieves the highest average AUC score on all datasets as expected. Even though our method shows a little more time-consuming than the Original RX, it is still meaningful for some applications that need high detection accuracy and efficiency simultaneously. Further, our method not only get better detection results, but also much faster than other algorithms .It is worth noting that the performance of H-RX is close to other methods on Dataset-3  that consists of several layers with different functions. The main contributions of the proposed method are: (1) A novel hierarchical framework is proposed to restrain background spectra and enlarge the difference between the background and anomaly target spectra; (2) we prove the rationality of the proposed method theoretically and the ability to construct more discriminative spectra than that in previous layers; and (3) experimental results on three datasets show that the proposed hierarchical method has a significant improvement over the classical RX method and other recent sub-pixel anomaly detection methods.


Signal-in-space (SIS) User Range Error (URE) is one of the major error sources for BeiDou Navigation Satellite System (BDS) applications and can reach tens of meters or even more. Therefore, real-time monitoring of SIS anomalies has a great realistic significance to guarantee the safety of users. According to an analysis of the BDS navigation messages, it showed that the User Range Accuracy (URA) index could not reflect the change of URE when it was abnormal. The conventional models using the relationship between URA and URE to monitor SIS anomalies are not suitable to the present BDS. Therefore, we use a prior information of SIS URE derived from ground observational data instead of URA to monitor BDS SIS anomalies. In order to realize the corresponding functions, we analysed the distribution of SIS UREs and obtained their prior models. Then, the monitoring threshold is determined using the prior models and a confidence interval instead of URA. The scheme was tested by applying to BDS SIS anomalies monitoring based on 13 ground tracking stations. The performance of this method was assessed by comparison with the satellite-health indicators from broadcast ephemeris. The results confirm that the method developed in this paper can rightly and timely detect abnormal SIS. The BeiDou Navigation Satellite System (BDS) was officially put into operation on 27 December 2012 and can overlap the Earth surface in 2020 s ,4.For most BDS OS users, real-time satellite orbits and clocks are derived from predicted ephemeris and clock parameters in navigation messages broadcast by BDS satellites. Signal-in-Space (SIS) errors arise primarily from imperfect estimation of a satellite orbit and clock error and are usually undetectable and uncorrectable for stand-alone OS users . It is mNominally, OS users can assume that broadcast navigation message is reliable and the URE derived from a healthy SIS is at the meter level. In practice, however, SIS anomalies occurred occasionally and UREs of tens of meters or even more were observed, which could lead to hazardous misleading position solutions for unaugmented receivers . TherefoThe study of monitoring SIS anomalies has long been conducted for Global Positioning System (GPS) ,12,13,14peration . Howeverperation . In the peration . Therefoperation . HoweverThe objective of the work presented here is to propose a scheme to monitor the BDS SIS anomalies using ground observation data. This program constructed a random model that was consistent with the actual URE based on the features of the BDS constellation and the observation conditions of the regional monitoring stations. According to the random models and a given confidence interval to obtain the threshold of URE instead of URA, the method is called BDS Ground SIS Monitoring (BDSGSISM). In this contribution, the BDS SIS UREs are derived from BDS satellites tracked by 13 ground stations of Multi-GNSS Experiment campaign (MGEX) of International GNSS Service (IGS) in the Asia-pacific region via deducting non-SIS errors from the total pseudorange errors in Day of Year (DOY) 36–56, 2017. In order to ensure the originality of broadcast navigation messages, the broadcast navigation messages are cleaned based on majority voting [The remainder of this contribution is organized as follows: Combining cleanly BDS ephemeris based on real-time raw data from tracking network.The SIS UREs of all the satellites are calculated by the selected tracking stations’ observations.The mean value and the standard deviation of the SIS UREs of each satellite observed by each station are calculated, as the prior information.The threshold of the SIS UREs is calculated using Equation (1) based on a given confidence level and the prior information obtained in step 3.Satellite-health status has specific markers in the broadcast ephemeris, however, if there is any sudden and unexpected failure, the Operational Control Segment (OCS) will guarantee a failure warning in the next broadcast ephemeris. This is insufficient for the users of BDS. Traditional SIS anomalies are monitored on the basis of the relationship between SIS UREs and URA as follows: if the SIS UREs are greater than aranteed . Howeveraranteed . Figure In this paper, the monitored satellite-health status is represented by one of the following three numbers: −1 , 0 (cannot be determined), and 1 . Determining the health status of a satellite requires more than three tracking stations observing the satellites simultaneously; otherwise, the status of the satellite cannot be determined and is flagged 0. If more than three tracking stations detecting anomalies  at the same time then it is determined that the satellite cannot be used, and the status is flagged with −1. If a satellite is observed by more than three tracking stations at the same time and the SIS UREs for fewer than three tracking stations exceed the not-to-exceed (NTE) limit, then it is determined that the satellite is working normally, and the status is flagged 1.The method may experience a missed alarm in the following situations: (1) when the satellite orbit is abnormal, but the projection of the orbital deviation is small in the monitoring service area, therefore, the monitoring stations cannot detect the abnormality. The effect of the anomaly is also not serious enough to cause dangerous misleading information for the users in the service area; (2) when a navigation satellite fails and misleads to users in the service area, but less than three monitoring stations simultaneously detect this anomaly in the service area. The probability of such event can be expressed as:In order to verify the reliability of the BDSGSISM, the BDS observation data from MGEX of International GNSS Service (IGS) for the years 2015 and 2017 have been used. As BDS mainly serves the Asia-pacific region, we selected 13 stations over the region and the nearby area as the experimental data sources. The specific information of these tracking stations including the tracking-station name, the receiver type, the antenna type and the BDS satellite is summarized in The MGEX tracking station has the following advantages: (1) the selected tracking station can provide dual-frequency observation on B1 and B2 bands; (2) the MGEX tracking station can provide 1 Hz observation data, which is conducive to monitoring short-time satellite anomalies; and (3) we can obtain relatively long-time BDS observation data. To verify the reliability of the algorithm, we re-encoded broadcast ephemeris and observation data that have been broadcasted and transmitted in the form of real-time streams.Due to accidental bad receiver data and various hardware/software bugs, a small proportion of the navigation data from the tracking stations have defects, such as losses, duplications, inconsistencies, discrepancies, and errors. Therefore, to accurately and effectively monitor BDS SIS anomalies, the navigation messages must be guaranteed authenticity. In this paper, we use a systematic methodology to combine BDS ephemeris using real-time data of simulation from multiple tracking stations.Currently, the combined broadcast ephemeris provided by IGS and other institutions mostly take pseudorandom noise (PRN) and Time of Clock (TOC)  at the same time.For each navigation message e in O, if there is already a navigation message f in the set P (its database of different robust parameters) having the same robust parameters as e, then add the fragile parameters of e into f ’s database; otherwise, add e into P.For each navigation message f in P, apply majority vote to each fragile parameter (except the Transmission Time of Message (TTOM)) according to f’s database, and record the number of stations that report f.The broadcast ephemeris can be combined using the algorithm described below.s, subscripts r and i signal; The observation equation for the pseudorange observables can be modelled as:For the real-time users computing satellite orbits and clock offsets based on broadcast navigation messages, the geometric distance and satellite clock offsets can be written as:Based on Equations (3) and (4), we can obtain the SIS URE, that is (5)SIS UIn order to obtain the SIS URE, en model , ρrs^ isFor the BDS, and SIS URE the pseudorange inaccuracy, attributable to the ground control and the space vehicles, is one of the major error sources affecting BDS application. SIS URE refers to errors caused by satellite segment, including satellite ephemeris and clock errors, satellite antenna variations, and signal imperfections. However, SIS anomalies are mainly caused by satellite ephemeris and clock errors because antenna variations and signal imperfections are at a level of millimeter or centimeter . As SIS The SIS UREs can be utilized to identify the satellite fault, and their description and assessment is very significant for BDS-SIS monitoring . At presObservations from 21-day intervals of DOY 036–056, 2017 were selected as the core data sets for studying BDS SIS UREs. The Although the SIS UREs are generally assumed to be zeroes-mean, the reality may be different ,25. To mThe prior models of SIS UREs can be written as:Representative results of such Fourier-series models are presented in By analyzing the SIS UREs (from which the periodic system error has been eliminated) from October 2016 to March 2017, there is a certain difference among the means and the standard deviations of the different satellites at different frequencies observed by different stations, as shown in SIS anomaly monitoring requires real-time data streams of the tracking stations, however, for various reasons, the current MGEX tracking stations have not provided the BDS real-time data streams. In order to verify the reliability of the proposed algorithm, we re-encoded the broadcast ephemeris and the observation data that have been broadcasted and transmitted in the form of real-time streams. Through processing the simulated real-time data streams, some potential SIS anomalies are found and listed in For the monitoring results in According to the analysis of the monitoring results, it can be found that in the BDS broadcast ephemeris there exist missed detection as well as false alarms. BDS SIS anomaly monitoring is essential to guarantee the safety of the user’s life and property. SIS anomaly is one of the major risk sources for user’s PNT services. However, the traditional monitoring method of SIS anomaly cannot be applied to BDS due to invalid URA parameter in BDS broadcast ephemeris. Therefore, it is essential to take an effective approach to monitor the SIS anomaly for the wide application of BDS. In this paper, we propose BDSGSISM method based on the prior information (mean and variance) of SIS UREs to monitor BDS SIS anomalies.In order to guarantee authenticity of broadcast ephemeris, the cleaned navigation messages are combined based on broadcast ephemeris received from a number of tracking stations. According to analysing BDS SIS UREs, it shows that the SIS UREs do not obey a Gaussian distribution due to systemic errors. Therefore, we utilize Fourier series to model the SIS UREs to eliminate the periodic system error, making them obey a Gaussian distribution and satisfy the precondition of SIS-anomaly monitoring. Besides, we calculate the SIS URE’s means and variances for all satellites observed by selected tracking stations and find that there are differences in these values for different satellites. Finally, the NTE threshold is determined by the prior information of SIS UREs and a given confidence interval, instead of URA.To verify the reliability of BDSGSISM, the collected BDS data of 57 days  were analysed using BDSGSISM and presented the potential signal-in-space anomalies. According to a comparison between the satellite health states obtained by BDSGSISM monitoring and those given by broadcast ephemeris, it can be found that there exist false alarms and missed detections in the results obtained by broadcast ephemeris, however, BDSGSISM can quickly and accurately find the abnormal satellite and overcome the shortcoming.


Traffic accidents have become an important problem for governments, researchers and vehicle manufacturers over the last few decades. However, accidents are unfortunate and frequently occur on the road and cause death, damage to infrastructure, and health injuries. Therefore, there is a need to develop a protocol to avoid or prevent traffic accidents at the extreme level in order to reduce human loss. The aim of this research is to develop a new protocol, named as the Traffic Accidents Reduction Strategy (TARS), for Vehicular Ad-hoc NETworks (VANETs) to minimize the number of road accidents, decrease the death rate caused by road accidents, and for the successful deployment of the Intelligent Transportation System (ITS). We have run multiple simulations and the results showed that our proposed scheme has outperformed DBSR and POVRP routing protocols in terms of the Message Delivery Ratio (MDR), Message Loss Ratio (MLR), Average Delay, and Basic Safety Message. Over the past few decades, the number of vehicles has significantly increased which has resulted in populated roads. This massive traffic load has uplifted the road accident graph, which has consequently raised injuries and death rates worldwide. In 2010, the states presented in the World Health Report (WHR) documented road traffic injuries as the ninth most common reason for the disability of people. According to the report by the World Health Organization (WHO) . FriendsThese road traffic accidents not only cause an increased death toll or injury, but also result in communal and financial damage to the state . The deaNumerous efforts have been made to avoid such drastic situations through the implications of speed limits, imposition of traffic rules, deployment of seat belts and air bags, boosting stiffness into the physical structure of a vehicle, and so on . FurtherVehicular Networks are the most important and emerging technology in the field of Intelligent Transportation Systems (ITS). A large number of new routing schemes and architectures have been suggested in recent years in order to successfully deploy ITS. The widespread deployment of ITS is a complex and challenging task. Numerous projects have been developed and their performance evaluated either through simulation or deployment in a real environment. Various projects developed by different countries include the project related with Automobile Safety Communication in the United State of America (USA) and Car-to-Car communication in the European Union. The European Union, USA, and Japan have already deployed a smart vehicular environment in both urban and highway areas. Many car manufacturing companies like Ford, Daimler, BMW, General Motors, and Audi have been inspired by the ITS and are motivated to manufacture smart vehicles for passenger safety.p denotes the specific version for communication between vehicles). There are three modes of communication in VANETs: Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V), and Vehicle 2 Hybrid (V2X) communication. Vehicles communicate with each other in order to get a better understanding of the surrounding environment to prevent any hazardous situations. Drivers must be given timely warnings about any expected hazardous situations in order to avoid accidents. Another concern is that drivers become doubtful as to which route to follow next even if warned about emergency situations, which can cause inefficient traffic flow and congestion on alternate routes [In VANETs, the topology of the network keeps changing due to the high mobility of vehicles, so the network is considered as an ad-hoc network. It enables vehicles to sense their environment and exchange their sensed data with surrounding vehicles. An infrastructure named Road Side Units (RSU) is installed along the roads to assist the vehicles moving in its vicinity. Standard IEEE 802.11p  has beene routes . Traffic(1)The design of a new protocol for accident prevention and, hence, reduction.(2)To forecast/guess the probability of the occurrence of an accident in advance before it occurs.(3)To re-route vehicle traffic to prevent traffic jams on the road that may cause accidents.(4)To maintain traffic flow efficiently.(5)To assist drivers to reach a destination on time.(6)To reduce safety messages broadcasting in order to avoid a broadcasting storm and network congestion.(7)To minimize the delay in re-routing of traffic to other available paths.The objectives of this research were focused on the following aspects:The roadmap of this research is arranged as follows. An overview of the existing protocols proposed to avoid traffic accidents is presented in This section describes a list of the extensive research conducted to avoid road accidents at the maximum level with a specific focus on the vehicular environment in order to reduce human loss. The Literature Review section is further divided into two sections for each category of traffic accident avoidance and prevention schemes, based on routing schemes and warning messages as shown in Lozano et al.  proposedGokulakrishnan et al.  proposedDawood et al.  proposedBenslimance et al.  considerRoy et al.  proposedKumar et al.  proposedKshirsagar et al. ,23 propoNzouonta et al.  proposedDevdhara et al.  considerBhumkar et al.  proposedNzouonta et al.  considerManoj et al.  proposedKhatri et al.  proposedIn this section, we discussed the traffic accident prevention and avoidance scheme with a specific focus on VANETs. We evaluated research papers from 2011–2017. In In this section, we propose a strategy named as the Traffic Accidents Reduction Strategy (TARS) to avoid accidents and to prevent congestion in VANETs. In the proposed strategy, the warning message is transmitted in time; therefore, potential vehicle collisions are avoided. Moreover, vehicles are assisted to another route. In this section, first, an introduction to some of the participating entities is presented. Second, a few assumptions were made for the smooth execution of our proposed strategy. Then, phases of Traffic Accidents Reduction Strategy (TARS) are described in detail. Following are the entities involved in the execution of the TARS.(1)Smart Vehicles(2)Road Side Units (RSUs)(3)Trusted Authority (TA)(4)Governmental Authority (GA).The entities involved in the traffic accident reduction strategy are as follows:Smart Vehicles(1) In VANETs, a communicating device called the On-Board-Unit (OBU) is installed in each vehicle which allows it to communicate with other entities in the system. Vehicles communicate with other vehicles and the RSU through the OBU to obtain a better awareness of their surroundings in order to react in a timely manner to any unusual situation, for example, accident, traffic jam, road blockage, and so on.Road Side Units (RSUs)(2) The RSU keeps the information of the automobiles (vehicles) moving in its transmission range so that it has a global view of the traffic. This feature enables the RSU to maintain the efficient traffic flow and, hence, accidents are also avoided.Trusted Authority (TA)(3) The Trusted Authority (TA) is a trusted administrative authority. Both vehicles and RSUs are registered with the TA. The TA assigns unique identities to both the RSUs and vehicles. The TA has a record of all the registered entities so that they remain liable to it.Governmental Authority (GA)(4) The Governmental Authority (GA) plays the role of a passive entity. The passive entity is not directly involved in the execution of TARS; however, it plays a vital role if a non-registered vehicle is encountered so that it should not affect the security of the VANET system at all.All vehicles must be equipped or armed with a Global Positioning System (GPS) in order to obtain the exact location so that it can be used in the Basic Safety Message (BSM).The RSU is an uncompromised entity. An uncompromised entity means that it can neither be effected by any attack nor can its record can be modified.All vehicles and RSUs are registered with the TA.The Vehicle to Infrastructure (V2I) communication mode is considered.For the smooth and efficient flow of our proposed strategy, we made the following assumptions:The Traffic Accidents Reduction Strategy (TARS) is divided into three phases. The first is the setup phase, which describes the process of establishing the VANET according to our proposed strategy; the authorization phase, the second phase of TARS, demonstrates the registration process of the entities. Finally, the execution of the proposed strategy is presented in the execution phase. We discuss these three phases in detail.Setup Phase(1) RSUFlocation is the end point of the transmission range of the RSU; iPosition is the current location of a vehicle received in the BSM; and iVelocity is the velocity of vehicle i.In this phase, the entities of VANETs are set up for the TARS scheme. A RSU periodically broadcasts its identity and location in its vicinity so that vehicles moving in its range can communicate with it. A vehicle broadcasts BSM containing VID, location, speed and direction to the RSU when it enters into the range of RSU and listens to RSU’s beacons. The RSU keeps the information of the automobiles (vehicles) travelling in its vicinity through the information received in the BSMs. The RSU data include the vehicle-ID, their velocity (V), location, and Tour-Span (TS). Tour-Span is the minimum time a vehicle spends in the range of a RSU. On reception of the BSM, the RSU extracts the vehicle-ID and verifies it by looking it up in its database. After verifying the vehicle-ID, the RSU calculates the tour-span ‘TS’ of the vehicle in its region as shown in Equation (1).Authorization Phase(2) This phase describes the procedure of registering the RSUs and vehicles with a Trusted Authority (TA). The TA is a trusted administrative authority that is responsible for the accountability of entities of the VANET system. Once the entities, RSUs, and vehicles become registered with the TA, then they remain liable to it. During the registration process, the TA provides a key and a vehicle ID to a vehicle as shown in Execution Phase(3) t milliseconds. The RSU updates the record at the reception of each BSM. If a distance between the two vehicles, moving on same road lane, is less than the safe distance (vD) then the leading vehicle sends warning message to the following vehicle. On the other hand, the RSU also monitors the distance (D) and velocity (Velocityi) of each vehicle. It sends a warning message to warn that vehicle. vD is calculated using the method presented in vD, the RSU sends a warning message to the following vehicle to keep the safe distance. If in a certain time period (pT), the RSU has sent alertT messages, then it means there is a high traffic load in that area. The values considered for pT and alertT were 10 s and 4, respectively. These values were taken from [vD at some time, the leading vehicle sends a warning message to the following vehicle to keep a safe distance.The TARS focuses on the use of the distance measurement approach for warning message generation to prevent accidents along with the efficient rerouting of traffic. An illustration of the proposed technique is shown in ken from . AfterwaMeanwhile, it also reports it to the previously connected RSU through neighboring vehicles. Neighboring vehicles only act as relay nodes and they pass on the message to the trailing vehicles until it reaches that particular RSU.Algorithm 1: Traffic Congestion Avoidance AlgorithmA flowchart of the methodology of the proposed strategy is shown in Scenario 1:If a vehicle travels outside of the region in which it is registered, then the RSU of the newly entered region contacts the TA for the verification of a vehicle. The TA checks whether the vehicle is a registered with it or not. The TA then sends a verification message to the RSU if it is a registered vehicle as illustrated in Scenario 2:If a non-registered vehicle enters into the range of a RSU, it then contacts the TA for vehicle verification. The TA reports it to the Governmental Authorities (GA) for further action if that vehicle is not registered at the TA as shown in a and b represents the following and leading vehicle. The change in position of a leading vehicle w.r.t. the following vehicle can be calculated as shown in Equation (2):Let The change in position of a following vehicle can be calculated as shown in Equation (4):Safety distance is denoted by By putting all the values 2 grid map with a different number of vehicular nodes. All vehicular nodes were deployed randomly with a randomly generated velocity range from 60 km/h to 120 km/h. Vehicles are uniformly distributed over four lanes of a road as shown in The performance of the proposed TARS routing protocol is analyzed and evaluated by using a real traffic simulation tool called the Simulation of Urban Mobility (SUMO)  integratThe TARS protocol simulation parameters are shown in The SUMO tool is used to simulate the real vehicular traffic. In order to implement a realistic highway scenario, we obtained the mobility file of the real time traffic using SUMO. A screenshot of a real highway environment using SUMO is shown in NS-2.35 is another tool used in the TARS routing scheme to implement the main purpose/procedure of the TARS routing protocol as shown in The mobility trace file is imported in NS-2.35. The TARS protocol simulation is presented in NS-2.35 as shown in We focused on the highway scenario for the TARS simulation. The TARS scenario was implemented in a VANETS environment by using NS-2 and SUMO. We ran the simulation twenty-five times for each vehicle density parameter, and average results are shown in graphs.The TARS, DBSR , and POVThe MDR shows the percentage of messages that are successfully delivered to the vehicles. The basic objective of each routing protocol is to gain a high delivery ratio. The following mathematical formula is used to calculate the MDR.The MLR shows the percentage of lost packets that are dropped during the transmission. The following formula is used to calculate the MLR.The Average Delay is the time taken by the RSU to send the safety warning message to the vehicle when two vehicles cannot maintain the predefined threshold distance between each other.The Basic Safety Message (BSM) shows the number of messages transmits from RSU to the vehicle or vehicle to vehicle.Now, we discuss the experimental results of our proposed protocol, TARS, and compare it with the results obtained for DBSR and POVRP protocols using the highway scenario on the basis of the performance parameters which are discussed in the previous section.The BSM plays a very important role in VANETs’ environment to transfer safety messages. Safety messages need to be transmitted all the time to all neighbor vehicles. In order to provide reliability in the traffic network, safety messages must reach their destinations without causing channel congestion. Furthermore, sending safety messages without using a congestion control or avoidance mechanism causes the broadcasting storm problem. In TARS protocol, when the number of vehicles increases, then the number of basic safety messages also increases. Moreover, RSU makes intelligent decisions, sending safety messages to the other vehicles while considering the dynamic nature of topology and the broadcasting storm problem in order to adjust, and considers the current road traffic information. The RSU monitors the distance between two vehicles, if the distance is less than the defined threshold, then the RSU sends warning messages in order to make the vehicle aware of the status of the traffic network (whether it is congested or not) and distance. In order to provide the stability of the channel network, the RSU and vehicle only send warning and safety messages when it is really needed. In TARS, the number of basic safety messages are fewer compared to DBSR and POVRP, meaning TARS decreases the chances of the occurrence of channel congestion in order to provide the stability of the network protocol as well as improving the network performance depending on the capacity of the channel. The RSU sends warning messages after verifying the possibility of congestion occurrence in terms of high traffic load in the network, and informs the upcoming vehicle to take alternate routes to avoid congestion as shown in The TARS protocol shows less average delay as compared to the DBSR and POVRP. TARS uses a traffic congestion avoidance algorithm which reduces the unnecessary messages caused by vehicular congested traffic. The RSU only transmits warning messages and vehicles exchange safety messages according to the present traffic condition . TARS overcomes the condition in which vehicles move at high speed and broadcast lots of messages, where there are a large number of vehicles causing congestion due to message dissemination. Moreover, after detecting congestion, the RSU transmits warning messages at the exact time for the upcoming vehicle to select an alternate route in order to prevent congestion and avoid accidents, the upcoming vehicle makes more intelligent decisions and selects a less congested route while applying a basic safety message strategy (vehicles share their velocity and vehicle-ID with other vehicles) to follow the specified path. In this way, the TARS protocol provides less delay in order to transmit warning messages, as a result, driver safety is increased, as shown in The TARS protocol shows a high MDR, as compared to the DBSR and POVRP with different numbers of vehicles. When the number of vehicles increases, then the rate of messages also increases. If the RSU and vehicles both transmit the messages, then heavy congestion in the channel will result, the frequency of transmitting messages gets reduced, and the messages cannot be delivered to the neighboring vehicles. Moreover, due to the unsuccessful reception of messages, there are a lot of chances of the data packet loss and due to this packet loss some safety messages also experience loss and cannot reach the specified destination. In order to overcome this challenge, the TARS protocol plays a very important role and shows high PDR, meaning the protocol builds a reliable connection between the two vehicles as well as the connection between the RSU and vehicle, with minimum packet and message loss ,41,42,43In order to calculate the confidence interval, the highway scenario is evaluated and analyzed. The geographical (highway) trace file was retrieved from SUMO in order to achieve the real world scenario. Simulation was conducted in NS-2, neighboring vehicles with 40–200 vehicles travelling random routes with the help of a random mobility model. For each configuration, 50 trials were executed for 500 s and the simulation has been conducted with different random seeds; as a result, MDR results were achieved in the transmission range of 30 m–2100 m that are assumed to be normally distributed.The MDR showed the average probability of receiving a single message with the confidence interval. We evaluated and analyzed the loss warning messages or safety messages due to an unstable link connection. The TARS protocol transmits messages successfully at the specified destination. In this way TARS shows less loss ratio as compared to other similar studies. The warning and beacon safety message must be delivered to each neighboring vehicle without delay. A single delay or loss of any packet could result in loss of life. However, if the number of vehicles increases then the chances of lost packets also increase, as result causing instability of the network. Therefore, the TARS protocol shows less loss packets as compared to DBSR and POVRP. It means the proposed protocol provides the stability of the network and more packets or messages are delivered up to the specified destination. So, TARS provides a better and reliable routing scheme as per shown in We have highlighted the issue of road safety in Our research paper has highlighted the issue of road safety. Road hazards have drastically increased with the increase in automobiles over the last ten years. Many efforts have been made to avoid such severe conditions. However, these efforts are restricted in providing a holistic solution. The proposed protocol, TARS, forecasts the probability of the occurrence of an accident in advance before it occurs. It also re-routes vehicle traffic to prevent traffic jams on the road that may cause accidents. TARS is aimed at maintaining the traffic flow efficiently. It also assists drivers in reaching a destination on time. Moreover, TARS reduces the safety messages broadcasting in order to avoid a broadcasting storm and network congestion and minimizes the delay in re-routing of traffic to other available paths. Our results demonstrated that TARS had a good performance in Message Delivery Ratio, Message Loss Ratio, Average Delay, and the Basic Safety Message Rate. In future, our goal is to propose a mathematical model and perform further simulations on performance metrics that are associated with TARS in an urban scenario. Our results and discussion showed a successful demonstration of the TARS proposed scheme as compared to other similar studies.


The road friction coefficient is a key parameter for autonomous vehicles and vehicle dynamic control. With the development of autonomous vehicles, increasingly, more environmental perception sensors are being installed on vehicles, which means that more information can be used to estimate the road friction coefficient. In this paper, a nonlinear observer aided by vehicle lateral displacement information for estimating the road friction coefficient is proposed. First, the tire brush model is modified to describe the tire characteristics more precisely in high friction conditions using tire test data. Then, on the basis of vehicle dynamics and a kinematic model, a nonlinear observer is designed, and the self-aligning torque of the wheel, lateral acceleration, and vehicle lateral displacement are used to estimate the road friction coefficient during steering. Finally, slalom tests and DLC (Double Line Change) tests in high friction conditions are conducted to verify the proposed estimation algorithm. Test results showed that the proposed method performs well during steering and the estimated road friction coefficient converges to the reference value rapidly. Vehicle safety-related state estimation ,2,3 and Road friction coefficient estimation methods can be divided into two general types: cause-based methods and effect-based methods. The state-of-the-art methods of road friction estimation have been reviewed . The priSince new sensors equipped on intelligent vehicles facilitate the estimation of vehicle states, they could be useful for improving the accuracy of the results of road friction coefficient estimation. In this paper, we introduced vehicle lateral displacement, which is based on the relationship between road friction and the self-aligning torque of the front axle, to the framework of road friction coefficient estimation. On the one hand, lateral displacement information contributes to improvement in the estimation accuracy of the vehicle’s sideslip angle so that tire forces can be estimated more precisely. For intelligent vehicles, vehicle lateral displacement information can be obtained from cameras, GNSS  and maps, or V2X (Vehicle to Everything) systems. We acquired this information from a high-accuracy GNSS and a pre-established lane line map. On the other hand, compared with methods based on the relationship between road friction and the longitudinal or lateral forces of the tires, the self-aligning torque-based method requires fewer excitations, so the road friction coefficient can be estimated before the vehicle drives into critical conditions. We adjusted the tire brush model to fit the tire test data more accurately. Then, by integrating lateral displacement information, self-aligning torque measurement, lateral acceleration measurement, the tire model, and the vehicle model, we developed a nonlinear observer for road friction coefficient estimation. The stability of the observer was proved, and the observer’s robustness was analyzed. A novel modified tire brush model based on tire test data is proposed. Compared with the traditional tire brush model, new mapping relationships between lateral tire force and the sideslip angle and between self-aligning torque and the sideslip angle are established, which can model tire forces and self-aligning torque more precisely. Further, the simple expression form of the modified tire model functions facilitates the proof of the non-linear observer’s stability.Lateral displacement information is introduced into the estimation system. Lateral displacement information can be obtained from new sensors equipped on intelligent vehicles, and it can be useful for accurate sideslip angle estimation, so that the road friction coefficient can be calculated more precisely.A non-linear observer for the road friction coefficient is proposed. The stability of the nonlinear observer is proved thorough the Lyapunov method, and the robustness is analyzed.The main contributions of this paper are summarized as follows:The remainder of this paper is organized as follows. In A nonlinear 2 DOF vehicle model is introduced to express the vehicle lateral dynamics, as shown in Lateral force A novel modified tire brush model is applied to describe lateral force Tire tests were conducted on a tire test bench to verify the proposed tire model. The raw test data are shown in The tire test data in Assuming that the road friction coefficient Since the lane line map information is available, we can measure (i) the distance between the COG of the vehicle and the lane line From the system defined by Equations (10)–(12), the corresponding nonlinear observer isdesigned as:According to the system dynamics in (9), (10), (12) and nonlinear observer in (13), (14), (15), the corresponding error dynamics is:The Lyapunov function is defined as:In Equation (27) If The uncertainties of the tire and vehicle models or measurements from sensors introduce perturbance to the system. It is necessary to analyze the performance of the estimator with bounded external excitation. According to the error dynamics of the system in (20), (21) and (22) without external inputs, the error dynamics of the system with inputs can be expressed asThe Lyapunov function is chosen and defined in Equation (23) thus, according to (39), (40), and (41) we have:.19 from , we can Tests based on an electric vehicle were conducted to verify the proposed road friction coefficient estimation algorithm. The experimental setup and results are discussed in this section.The test vehicle is an electric vehicle and shown in To verify the proposed road friction coefficient estimation algorithm, slalom tests and DLC (Double Line Change) tests were conducted on the road shown in Slalom test results are shown in In this paper, a nonlinear observer for the road friction coefficient during steering based on the self-aligning torque characteristics of the tires aided by vehicle lateral displacement information was proposed. A modified tire brush model was established according to the tire test data, and the model describes the tire characteristics more precisely than the original model. A nonlinear observer using vehicle lateral displacement information was designed, and the stability and robustness were analyzed. Experiments were conducted to verify the proposed road friction coefficient estimation algorithm. The test results demonstrate that the proposed method performs well during vehicle steering, and the estimated road friction coefficient converges to the reference value very rapidly. We have modified the tire brush model according to the tire test data, and the results show that the modified model describes the tire characteristics properly. However, the tire tests were done with only one type tire in high friction condition, which is not sufficient to verify that the modified tire model is suitable for other tires with different sizes or types. Tire tests with more tires in different friction conditions should be conducted to verify the modified tire brush model.The experiments were conducted only in high friction condition due to the test condition limitation. The algorithm should also be validated in low friction condition and high to low or low to high friction transition conditions in the future.


Traffic collisions between kangaroos and motorists are on the rise on Australian roads. According to a recent report, it was estimated that there were more than 20,000 kangaroo vehicle collisions that occurred only during the year 2015 in Australia. In this work, we are proposing a vehicle-based framework for kangaroo detection in urban and highway traffic environment that could be used for collision warning systems. Our proposed framework is based on region-based convolutional neural networks (RCNN). Given the scarcity of labeled data of kangaroos in traffic environments, we utilized our state-of-the-art data generation pipeline to generate 17,000 synthetic depth images of traffic scenes with kangaroo instances annotated in them. We trained our proposed RCNN-based framework on a subset of the generated synthetic depth images dataset. The proposed framework achieved a higher average precision (AP) score of 92% over all the testing synthetic depth image datasets. We compared our proposed framework against other baseline approaches and we outperformed it with more than 37% in AP score over all the testing datasets. Additionally, we evaluated the generalization performance of the proposed framework on real live data and we achieved a resilient detection accuracy without any further fine-tuning of our proposed RCNN-based framework. Recently, the kangaroo detection problem on Australian traffic environments has received some attention from both research communities and car manufacturers—specifically, with the recent reports of the increased number of accidents happening on the Australian roads due to collision between Kangaroos and human-driven vehicles . The conOn the contrary, vehicle-based techniques rely either on passive sensors externally mounted on top of the vehicles or on the tremendous amount of active sensors that exist nowadays in most of the newly released vehicles in order to deter or detect wild animals. External passive sensors mounted on the vehicles that were used to keep wild animals away from the vehicles include high pitched signal whistles and tungsten halogen (TH) lamps ,5. ActivIn the literature, for almost all of the vehicle-based techniques relying on active sensors for the detection of wild animals such as deer and moose, their methodology can be broken down into two main consecutive stages. The first stage is feature extraction, whereas a hand-crafted set of visual/appearance features are extracted from colour and/or thermal images. The second stage is the classification stage, whereas the combined extracted features from the previous stage became the basis for training an object classifier that can detect the class of wild animals of interest at testing time ,7,9.One of the drawbacks of the aforementioned approach is that it needs a lot of iterations and fine-tuning in order to determine what are the most effective features to be considered in the first place. Moreover, the separation that happens between the two stages  make it inefficient to accommodate any new features or training data in the future.On the other hand, data-driven approaches such as convolutional neural networks (ConvNets), do not suffer from these problems that conventional methods are challenged with because they can be trained in an end-to-end fashion which alleviates the need for handcrafting specific features. In addition, ConvNets have achieved state-of-the-art results in different tasks, especially when it comes to vision-based tasks such as object classification, object detection and semantic segmentation ,11,12. HA novel large annotated synthetic depth images dataset of kangaroos in highway traffic environment roads similar to Australian roads. To our knowledge, no other dataset for kangaroos similar to it exists in the literature.A ConvNet model based on Faster RCNN architecture trained on synthetic depth images that can accurately detect and localize any instances of kangaroos in synthetic depth images.A ConvNet model for kangaroo detection trained only on synthetic depth images that can generalize robustly to detect and localize kangaroo instances in real depth images.One of the approaches that have been explored recently to tackle the scarce amount of labeled data for training ConvNets is the utilization of photo-realistic simulation framework in order to generate labeled data for training and validating ConvNet models ,14. In oThe rest of the paper will be organized as follows: In the literature, the wild animal detection problem in general and the kangaroo detection specifically in the context of vehicle-based collision avoidance systems have not been thoroughly explored as opposed to other vulnerable road users such as pedestrians or cyclists ,17. In , one of Zhou et al.  presenteMost recently, Mammeri et al.  presenteDespite the improvements that have been achieved when compared to  by usingIn the previous section, we have discussed most of the vehicle-based approaches presented in the literature for the wild animal detection in the traffic environments problem. Despite how powerful these approaches are, they may not be the suitable approach when it comes to solving the kangaroo detection problem.Unlike other wild animals such as deer and moose, kangaroos inhibit much more rapid and unpredictable movements in road and traffic scenes. Thus, any proposed vehicle-based approach to tackle this problem needs to take into account the different activities done by the kangaroo in traffic road scenes and their different viewpoints when observed by any vehicle-based optical sensor. Given that the conventional approaches proposed in the literature rely on hand crafting specific features from each activity/scenario that are discriminative enough for a classification stage. Thus, these approaches will necessitate designing numerous individual classification stages for each activity/scenario for the kangaroo detection problem that would be inefficient in terms of scalability and real-time performance, which is a critical requirement for any vehicle collision avoidance system.In our formulation for the kangaroo detection problem, similar to the prior work on wild animal detection, we will be also pursuing a vehicle-based technique relying on active sensors (stereo cameras or 3D LIDAR). However, rather than explicitly extracting a specific feature for each possible activity/scenario performed by the kangaroo in traffic and road environments, we will utilize a data-driven approach based on Convolutional Neural Networks (ConvNets). More specifically, for the task of kangaroo detection, we will be utilizing the state-of-the-art data generation pipeline introduced in  to generGiven the annotated generated depth image dataset, we can build and train a ConvNet model based on one of the successful deep ConvNet architectures for object localization and detection, Region-based Convolutional Neural Networks (RCNN) . By traiRecently, ConvNets have been achieving state-of-the-art results on a number of computer vision tasks such as object classification, object detection and semantic segmentation ,11,12. OThe backbone module of the RCNN architecture is the typical ConvNet model used in image classification tasks, which consists of three main stacked and interleaving layers, namely convolution layers, pooling layers and fully connected layers. Convolution layers act as an automatic feature extractor and each layer of them produces unique feature maps attained spatially from various locations in their input data. Following each convolution layer, a rectified linear unit (ReLU) is typically existent in order to inject nonlinearity between the elements of the convoluted feature maps . PoolingThe other module of RCNNs is called a region proposal module, which yields a set of unique regions called RoIs in the form of rectangle windows from the input image. The RoIs are utilized at an intermediate stage by the backbone module of RCNN, which was described previously through a dedicated pooling layer, called RoI pooling layer. The feature maps outputted from the RoI pooling layer are then vectorized into a 1D feature array by the FC layer of the backbone module of RCNN. Finally, at the last layer of RCNN, the objective training function exists, which is a joint multi-task loss layer. Additionally, a softmax and regression layer follow, which are responsible for scoring object classes and regressing the positions of an object’s bounding box, respectively.There are two variants of RCNN architectures and the main distinction between the two lies in how the proposal of regions are generated. The first variant was the earliest one introduced in , which rIn order to train, validate and test any ConvNet-based model, a handful of annotated data is required. Given the problem at hand of kangaroo detection, getting a large amount of real data of them that can capture all their movements and activities in traffic environments is not an easy task. Moreover, the process for annotating them with bounding boxes is a time-consuming process. On the other hand, one of the recent cost-effective and efficient paradigms that have been explored to overcome the scarcity of annotated data for ConvNets is the use of synthetic data images generated from photo-realistic simulation and/or game engine frameworks ,14.Similarly, we will utilize the simulation-based data generation pipeline proposed in . Based oFirstly, a motion capture system (MoCap) is utilized to record a dataset of three distinctive activities performed by kangaroos in real life, namely standing, hopping, and walking. In We designed inside Blensor  four difFor rendering the scene and generating the depth images, we made a camera setup of four dynamic virtual range cameras mounted in the scene on a height similar to the height of vehicle-based cameras (roughly 1.5 m). The four cameras were dynamically flexible so that they could follow the kangaroo instances in the scene with a constraint of being on a variable distance ranging from 10 m to 50 m away from the kangaroos. Therefore, the kangaroos were tracked in the scene from four different viewing angles, namely the back, front and two side views. As a result, during the simulation session, a depth image containing at least one kangaroo model with the other objects in the scene was obtained at each time-instance from each virtual camera. Finally, with the help of our developed software plug-in tool inside Blensor, the scene was scanned twice from each virtual camera from the four cameras. The first scan was with only the kangaroo instances rendered from the scene. In the second scan, however, the rest of the scene’s background objects were rendered. Consequently, we achieved more precise bounding box labels of all kangaroos in the scene in each camera of the 4 = four virtual ones.By following the rendering procedure previously discussed, we obtained from the four modeled scenes a total of 17K depth images containing from one to four kangaroo instances annotated in each depth image. We split the dataset into two with 12,000 depth images for training and 5000 for testing, and we made sure that the training and testing dataset splits are spanning two different scenes from the total of four scenes used in the data generation process.One of the known issues with depth images when feeding them to ConvNet-based models is the low contrast property of depth images ,27. The Additionally, in order to simulate some of the realistic properties existing in real depth and range images such as the ones obtained from stereo cameras or other range sensors, we introduced some noise and distortions to the perfectly generated synthetic depth images from our data generation pipeline according to the noise model introduced in ,28. ApplAs we discussed in Our proposed FRCNN-based model for the task of kangaroo detection will be following the same organization as the original FRCNN network in . Howevern scales and m aspect ratios. The variables n and m are hyper-parameters for the network and were set similar to the original FRCNN network with 3 for both scales  and aspect ratios . The multiplication of n and m in the FRCNN architecture is called “anchors”.The 13 convolution layers of the backbone module will be shareable with the other module of our proposed RCNN-based network, the RPN module. The input to this module is the last convolution layer’s feature maps from the RoI pooling layer of our backbone module. The RPN module consists of three other convolution layers, which are responsible for a generation of a set of region proposals representing different objects in the input image, and, in our case, the total number of these proposals is 300. The extraction of the regions’ proposals is done automatically over the feature maps from the last convolution layer of the backbone module, just by sliding a set of bounding boxes over these feature maps with different One of the advantages of using anchors in our proposed network is that they provide an inexpensive method that is translation-agnostic to the different sizes and shapes of the class of objects need to be detected  in the input images. Thus, it alleviates the need for training a different cascading number of individual classifiers for each possible scale and aspect ratio of the kangaroo, which was a necessity for the other proposed approaches’ literature for other wild animal detection problems ,18. The The training of any ConvNet-based model can be viewed as an optimization problem, whereas, using a learning algorithm, the objective is to minimize a given and known loss function. In our proposed FRCNN-based network for the kangaroo detection problem, we have a two loss functions that are required to be trained jointly. The first loss function is a is the index of anchors and a-th anchor to be a kangaroo. a-th anchor, which is one if positive and zero otherwise. a-th anchor, and a anchors.The loss function for posed in  as follolized in . ta is tThe loss function for posed in  as folloIn order to jointly minimize the previous loss functions, we used the stochastic gradient descent (SGD) for training our proposed FRCNN-based network with the following hyper-parameters: mini-batches of 128; with learning rate of 0.0001 for 30K iterations; and with momentum of 0.9 and weight decay of 0.0005.In order to evaluate the performance of our proposed framework for the task of kangaroo detection quantitatively, we generated a total of 5000 synthetic depth images according to the procedure discussed in We trained the other two baseline approaches to compare our proposed framework against them. The first baseline is one of the most proposed approaches in the literature for the task of wild animal detection in general, and it is a combination between feature extractor, HOG and the SVM classifier, which is pretty similar to the ones proposed in ,18 for dThe second baseline is a similar approach to our FRCNN-based framework, however with a shallower backbone module architecture based on the VGG-CNN-M architecture proposed in . The VGGIn Side view subset: All images that contain any kangaroo instances observed from their side view either with an orientation towards right or left from the range sensor’s perspective.Frontal view subset: All images that contain only front views of kangaroo instances from the perspective of the range sensor.Back view subset: All images that contain only back views of kangaroo instances from the perspective of the range sensor.Due to the computational limitations of HoG-SVM based approach to obtain a trained model using the 15,000 synthetic depth images of our generated training dataset. We trained three individual HoG-SVM models on three subsets of the training dataset. The three subsets were covering the orientation of the kangaroos from the perspective of the simulated scanning range sensor. We defined the three subsets according to the following:The reported AP scores for each trained HoG-SVM model is evaluated with the corresponding image subset in the testing image dataset with the column names  of As it is shown from the AP scores and the precision-recall curves in In In For examining how our proposed framework would behave when tested on real live data of kangaroo scenes, we collected more than 250 real depth images containing kangaroo in an urban traffic environment using a Microsoft Kinect during night time. Furthermore, we have manually annotated all the kangaroo instances in all the 250 depth images with bounding boxes for a quantitative AP measure. The collected data have covered similar viewpoints of the kangaroos as the ones used in the synthetic data generation phase. It is worth noting that the collected real data were tested using our proposed models that were trained using the generated synthetic data only. In In In this paper, we have proposed a framework for the problem of kangaroo detection in urban and highway traffic environments based on fast object detection architecture and region-based convolution networks (RCNN). In order to overcome the scarcity of labeled data for kangaroos in traffic environments, we generated 17,000 synthetic depth images of traffic environments with kangaroo instances and annotated them was trained using our data generation pipeline. Our proposed Faster RCNN-based framework for the kangaroo detection problem on a subset of the generated synthetic depth images, and tested it on other different subsets, whereas we achieved a superior result over other two baseline models. We scored more than 92% in the AP score over the testing dataset with more than 37% improvement over one of the most utilized approaches for the wild animal detection in the literature. Furthermore, we collected more than 250 real depth images and our proposed framework have achieved a significant generalization capability with better detection accuracies when compared to the other baseline model. In our future work, we will focus on testing our proposed framework on more real live depth images of kangaroo instances in more diverse traffic environments and explore the possibilities of implementing it on a low-cost embedded GPU that could be mounted on vehicles.