Reliability Prediction Technique Research Articles

In Situ Prediction of Metal Fatigue Life Using Frequency Change

A reliable technique for rapid prediction of the remaining useful life (RUL) of components experiencing fatigue degradation is introduced. The approach is based on measuring the temperature signature of a component upon rapidly changing its operating frequency for a short period of time. The temperature variations caused by alterations in plastic work rate are correlated to the loading history. The efficacy of the approach is investigated by conducting a series of axial fatigue tests on stainless steel 316 specimens. The material characterization involves subjecting the material to a constant amplitude fatigue load at 4 Hz and 12 Hz frequencies. The operating frequency is temporarily adjusted to the characterization frequencies for a brief duration. During this period, the change in the slope of temperature rise is recorded. Subsequently, the operation frequency is reverted to its original state, and the remaining useful life is predicted based on the recorded data. The model provides predictions for operation frequencies of 6 Hz, 8 Hz, and 12 Hz, and notably, the error of predictions is consistently under 12% for all cases. The method allows the operator to reliably estimate the remaining usefulness for field applications without interrupting the operation.

StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm.

Intracranial Aneurysms (IA) present a complex challenge for neurosurgeons as the risks associated with surgical intervention, such as Subarachnoid Hemorrhage (SAH) mortality and morbidity, may outweigh the benefits of aneurysmal occlusion in some cases. Hence, there is a critical need for developing techniques that assist physicians in assessing the risk of aneurysm rupture to determine which aneurysms require treatment. However, a reliable IA rupture risk prediction technique is currently unavailable. To address this issue, this study proposes a novel approach for aneurysm segmentation and multidisciplinary rupture prediction using 2D Digital Subtraction Angiography (DSA) images. The proposed method involves training a fully connected convolutional neural network (CNN) to segment aneurysm regions in DSA images, followed by extracting and fusing different features using a multidisciplinary approach, including deep features, geometrical features, Fourier descriptor, and shear pressure on the aneurysm wall. The proposed method also adopts a fast correlation-based filter approach to drop highly correlated features from the set of fused features. Finally, the selected fused features are passed through a Decision Tree classifier to predict the rupture severity of the associated aneurysm into four classes: Mild, Moderate, Severe, and Critical. The proposed method is evaluated on a newly developed DSA image dataset and on public datasets to assess its generalizability. The system's performance is also evaluated on DSA images annotated by expert neurosurgeons for the rupture risk assessment of the segmented aneurysm. The proposed system outperforms existing state-of-the-art segmentation methods, achieving an 85 % accuracy against annotated DSA images for the risk assessment of aneurysmal rupture.

Comparison of the Stability and Accuracy of Deterministic Project Cost Prediction Methods in Earned Value Management

Completing a project on time and on budget are essential factors for the success of any project. One technique that allows predicting the final cost of a project is earned value management (EVM). In this technique, different mathematical methods for predicting the final project cost have been proposed over the last 30 years. These formulas make use of activities’ actual costs and durations as the project progresses. EVM is a technique widely used by many project management professionals. However, very few studies have compared the stability and accuracy of the multiple existing methods for predicting the final cost of the project (commonly abbreviated as estimated cost at completion, EAC). This study compares the stability and accuracy of 30 deterministic cost prediction methods (EAC) in EVM. For this purpose, a representative database of 4100 simulated projects of various topological structures is used. Our results suggest that the methods with the simplest mathematical configurations achieve better stability and accuracy performance. Knowing which EVM methods are the most stable and accurate for predicting the final cost of the project will help project practitioners choose the most reliable cost prediction techniques when they are managing their own projects in real contexts.

Numerical Investigation on Protective Mechanism of Metal Cover Plate for Alumina Armor against Impact of Fragment by FE-Converting-SPH Method.

It is of extreme importance to develop a reliable numerical prediction technique to simulate the ballistic response of ceramic armor subjected to high-velocity impact (HVI) to economize the test cost and shorten the design period. In the present manuscript, a series of experiments on tungsten heavy alloy (WHA) fragment's penetration into 99.5% alumina (AD995) armors are systematically simulated by employing the FE-converting-SPH technique. The numerical results are compared with the experimental counterparts to find that the FE-converting-SPH method is fairly efficient in predicting the depth of penetration, the residual velocity, length and mass of fragment, and reproducing the crack forms of ceramic. The applicability and accuracy of the numerical model in terms of the algorithm, material model parameters and contact definitions are validated. Then, the relevant parameters of the calibrated numerical model are incorporated to explore the influence of cover-layer thickness on the armor performance. A few mechanisms regarding the cover plate have been identified to act on the armor performance, such as the alteration of fracture cone half-angle, proportion of energy absorbed by ceramic, mushrooming deformation of fragment, etc. The result of multi-mechanism superposition is that the best ballistic performance is endued with 1 mm cover-layer armor, which demonstrates a 24.6% improvement over the bi-layer armor with 4.96 g/cm2 area density, only at the cost of 15.7% increase in areal density, when back-plate thickness is held as 2 mm; for a constant area density of 4.96 g/cm2, a 1 mm cover-layer is also expected to be the best choice, with 10.7% improvement in armor performance.

Fast and reliable prediction of scramjet flowfields via Gaussian process latent variable model and deep learning

Fast and accurate prediction of high-speed flowfields is of particular interest to researchers in fluid science and engineering to enable efficient design exploration and knowledge discovery. The reliability of prediction is another important metric for the performance of prediction models. While predictive modeling approaches with and without reduced-order modeling (ROM) via machine learning techniques have been proposed, they are inherently subject to loss of information for ROM-based approaches and substantial computational costs in modeling for non-ROM-based approaches. This paper proposes an accurate ROM-based predictive framework with minimum information loss enabled by incorporating Gaussian process latent variable modeling (GPLVM) and deep learning. The stochastic nature of GPLVM allows for uncertainty quantification that indicates the degree of prediction error or reliability of prediction without requiring validation data. The applicability for supersonic/hypersonic viscous flowfields has been examined for two cases including axisymmetric intakes and two-dimensional fuel injection in scramjet engines by comparison with other predictive models. Comparable or superior prediction accuracy over the other models has been achieved by the proposed approaches, demonstrating its high potential to serve as a new competent, data-driven technique for fast, accurate, and reliable prediction of scramjet flowfields.

OESV-KRF: Optimal ensemble support vector kernel random forest based early detection and classification of skin diseases

The most widespread disease that often puts the patient’s life in danger is skin disease. Avoiding this situation, it necessitates the development of efficient and reliable skin cancer prediction techniques. Besides, regular screening of suspicious lesions in the skin helps the dermatologist to detect skin cancer in the early phase. Although there exist numerous Machine Learning methods to detect skin cancer in its early stage, the accurate and timely prediction and classification of skin lesions continued to be demanding. To fulfill this aim, in this paper, we proposed an ensemble support vector kernel random forest-based hybrid equilibrium Aquila optimization (ESVMKRF-HEAO) approach. The proposed prediction model is examined using the HAM10000 dataset which contains numerous varieties of skin lesion images in it. Initially, the intrusions and noises in the dataset are removed, and image qualities are enhanced using preprocessing pipelines. Then, the thresholding-based segmentation technique is utilized to segment the cancerous lesion regions from the healthy background. Finally, the proposed classifier accurately predicts and classifies the segmented images based on their feature characteristics into five distinct categories namely melanocytic nevus, basal cell carcinoma, melanoma, actinic keratosis and dermatofibroma. The simulation of the proposed model is performed using MATLAB 2019a software. The evaluation of the proposed ESVMKRF-HEAO approach performance is done concerning metrics such as sensitivity, f-1 score, accuracy, precision and specificity. The proposed ESVMKRF-HEAO approach attained a greater performance rate with respect to all metrics especially, the prediction accuracy of about 97.4% in the experimental results.

Investigating the Bond Strength of FRP Laminates with Concrete Using LIGHT GBM and SHAPASH Analysis.

The corrosion of steel reinforcement necessitates regular maintenance and repair of a variety of reinforced concrete structures. Retrofitting of beams, joints, columns, and slabs frequently involves the use of fiber-reinforced polymer (FRP) laminates. In order to develop simple prediction models for calculating the interfacial bond strength (IBS) of FRP laminates on a concrete prism containing grooves, this research evaluated the nonlinear capabilities of three ensemble methods-namely, random forest (RF) regression, extreme gradient boosting (XGBoost), and Light Gradient Boosting Machine (LIGHT GBM) models-based on machine learning (ML). In the present study, the IBS was the desired variable, while the model comprised five input parameters: elastic modulus x thickness of FRP (EfTf), width of FRP plate (bf), concrete compressive strength (fc'), width of groove (bg), and depth of groove (hg). The optimal parameters for each ensemble model were selected based on trial-and-error methods. The aforementioned models were trained on 70% of the entire dataset, while the remaining data (i.e., 30%) were used for the validation of the developed models. The evaluation was conducted on the basis of reliable accuracy indices. The minimum value of correlation of determination (R2 = 0.82) was observed for the testing data of the RF regression model. In contrast, the highest (R2 = 0.942) was obtained for LIGHT GBM for the training data. Overall, the three models showed robust performance in terms of correlation and error evaluation; however, the trend of accuracy was obtained as follows: LIGHT GBM > XGBoost > RF regression. Owing to the superior performance of LIGHT GBM, it may be considered a reliable ML prediction technique for computing the bond strength of FRP laminates and concrete prisms. The performance of the models was further supplemented by comparing the slopes of regression lines between the observed and predicted values, along with error analysis (i.e., mean absolute error (MAE), and root-mean-square error (RMSE)), predicted-to-experimental ratio, and Taylor diagrams. Moreover, the SHAPASH analysis revealed that the elastic modulus x thickness of FRP and width of FRP plate are the factors most responsible for IBS in FRP.

Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network

Accurate forecasting is indispensable for improving solar renewables integration and minimizing the effects of solar energy's intermittency. Existing research on time series solar forecasting confronts challenges such as determining the accurate hyperparameters and flexibility in considering meteorological parameters. This study proposes a novel deep learning model, namely an optimized stacked Bi-directional Long Short-Term Memory(BiLSTM)/ Long Short-Term Memory(LSTM) model to forecast univariate and multivariate hourly time series data by integrating stacked LSTM layers, drop out architecture, and LSTM based model. The performance of the model is enhanced by Bayesian optimization with the tuning of six relevant hyperparameters. To evaluate the model, standard Global Horizontal Irradiance (GHI) and observed Plane of Array (POA) irradiance with meteorological real-world solar data from Sweihan Photovoltaic Independent Power project in Abu Dhabi, UAE, and NREL solar data for year-round data are forecasted. Furthermore, the performance of the proposed algorithm is also evaluated under weather uncertainty for different climate types. The forecasting accuracy is evaluated based on various performance metrics and it is observed that the proposed model offered the best R2 values, 0.99 for univariate as well as multivariate models using GHI data and 0.97 using POA data. The findings suggest that the proposed model is a reliable technique for solar prediction due to its comparable performance with both GHI and POA in terms of accuracy.

Computational prediction of heteromeric protein complex disassembly order using hybrid Monte Carlo/molecular dynamics simulation.

The physicochemical entities comprising the biological phenomena in the cell form a network of biochemical reactions and the activity of such a network is regulated by multimeric protein complexes. Mass spectroscopy (MS) experiments and multimeric protein docking simulations based on structural bioinformatics techniques have revealed the molecular-level stoichiometry and static configuration of subcomplexes in their bound forms, thus revealing the subcomplex population and formation orders. Meanwhile, these methodologies are not designed to straightforwardly examine the temporal dynamics of multimeric protein assembly and disassembly, essential physicochemical properties to understand the functional expression mechanisms of proteins in the biological environment. To address this problem, we have developed an atomistic simulation in the framework of the hybrid Monte Carlo/molecular dynamics (hMC/MD) method and succeeded in observing the disassembly of a homomeric pentamer of the serum amyloid P component protein in an experimentally consistent order. In this study, we improved the hMC/MD method to examine the disassembly processes of the tryptophan synthase tetramer, a paradigmatic heteromeric protein complex in MS studies. We employed the likelihood-based selection scheme to determine a dissociation-prone subunit pair at every hMC/MD simulation cycle and achieved highly reliable predictions of the disassembly orders without a priori knowledge of the MS experiments and structural bioinformatics simulations. The success rate for the experimentally-observed disassembly order is over 0.9. We similarly succeeded in reliable predictions for three other tetrameric protein complexes. These achievements indicate the potential applicability of our hMC/MD approach as a general-purpose methodology to obtain microscopic and physicochemical insights into multimeric protein complex formation.

The prediction analysis of compressive strength and electrical resistivity of environmentally friendly concrete incorporating natural zeolite using artificial neural network

To decrease the environmental and climatic effects of rising concrete consumption, more environmentally friendly concretes are required. One approach to achieve this goal is using natural pozzolans (NP) in concrete mixtures. Natural zeolite (NZ) as a highly reactive NP can improve concrete's durability and mechanical properties. However, tests to estimate concrete strength may take a long time and be costly. Therefore, using computational intelligence techniques, particularly artificial neural networks (ANNs), can help speed up and simplify the process. Hence, this study aims to explore the potential of employing an ANN model to predict compressive strength (CS) and electrical resistivity (ER) of natural zeolitic concrete (NZC). The experimental results of 324 NZC specimens made from 54 different mix designs are used to develop the ANN model. Seven variable inputs are considered when designing the ANN model to predict CS and ER values, including specimen age, water-to-cementitious materials ratio, cement, NZ, gravel, sand, and superplasticiser contents. Extensive computational tests were conducted to evaluate the performance of the proposed model against results obtained by experimental tests and existing gene expression programming (GEP) in the literature. The RMSE values for CS and ER are 1.65 MPa and 3.96 Ω-m, respectively, which confirm the model's accuracy and robust predictive capability. The study's findings have the potential to assist in cutting costs and saving time by using a reliable prediction technique rather than conducting costly and time-consuming tests.

Elasto-plastic adhesive joint design approach by a radial point interpolation meshless method

ABSTRACT For efficient use of adhesive joints, reliable prediction techniques should be made available to the designer. Simulation of these joints’ behaviour is usually performed using the Finite Element Method (FEM). However, it is known that, in adhesive joints, the adhesive thickness (t A) is much smaller than the adherend thickness (t P), thus requiring a highly refined mesh to produce good results. Linked to this, the adhesive has to withstand high strains, causing mesh distortion and hindering the resolution. In these cases, meshless methods can be a good alternative. This work aims to implement the von Mises (vM) and Exponent Drucker-Prager (EDP) criteria combined with a meshless formulation based on the Radial Point Interpolation Method (RPIM), for the strength prediction of adhesively-bonded single-lap joints (SLJ). Validation with experiments is undertaken for joints with brittle to ductile adhesives, with varying overlap lengths (L O). Stress and strain distributions were plotted in the adhesive layer, and the failure load (P m) was assessed by strength of materials failure criteria. Significant adhesive and L O effects were found on P m. The RPIM proved to be a promising tool to predict the behaviour of bonded joints, although some limitations were found by using strength of materials criteria.

Meshless approach to material plasticity in adhesive joints

For an efficient use of adhesive joints, reliable prediction techniques should be made available to the designer. Simulation is usually accomplished by the Finite Element Method (FEM). However, in adhesive joints, the adhesive thickness is much smaller than that of the adherends, thus requiring a refined mesh to produce good results. Linked to this, the adhesive has to withstand high strains, causing mesh distortion and making difficult to attain solution. In these cases, meshless methods can be a good alternative, although these are not sufficiently validated yet. This work aims to implement the von Mises (vM) and Exponent Drucker-Prager (EDP) criteria into a user-built meshless method software based on the Radial Point Interpolation Method (RPIM), for the strength prediction of adhesively-bonded single-lap joints (SLJ). Validation with experiments is undertaken for a medium-ductile adhesive (Araldite® 2015) with varying overlap lengths (LO). Stress and strain distributions were plotted in the adhesive layer, and the failure load (Pm) was assessed by continuum mechanics failure criteria. The meshless method showed to be promising in predicting the behavior of bonded joints, although limitations were found by using continuum mechanics criteria.

Partial Discharge Location Identification Using Permutation Entropy Based Instantaneous Energy Features

Localization of partial discharge (PD) is a reliable and necessary technique for early prediction of impending failure in transformer windings. Different PD sources have diverse impacts on insulation, and PDs measured at different locations exhibit unique energy characteristics. An approach based on the energy characteristics of the PD signal is proposed here to locate PD inside the winding. An electrical detection method has been considered with PD pulses measured at the top and bottom of the winding through high-frequency current transformers (HFCTs). To reveal multiscale intrinsic characteristics of the PD signal, authors extracted intrinsic mode function (IMF) using ensemble empirical mode decomposition (EEMD). Next, the instantaneous energy distribution-permutation entropy (PE-IED) values of the first several IMFs were calculated to capture the energy of the PD. Then, dimensionality reduction was ensured by the fast principal component analysis that uses the fixed-point algorithm method. Finally, adaptive density-based clustering using the nearest neighbor graph (ADBSCAN-NNG) approach is proposed to identify the local high-density energy components. The proposed method is verified using the winding of a vacuum cast coil transformer and the localization results reveal the feasibility of the proposed method in locating PDs over existing localization algorithms. The reduced complexity of the proposed algorithm ensures the accurate onsite PD localization in transformer windings.

A comparative study of statistical and soft computing techniques for reliability prediction of automotive manufacturing

Reliability and safety analyses are the most important activities for reducing risk of failure events and upgrading availability of manufacturing industries. The traditional statistical models have been currently used; however, the complexity growth and diversity of systems as well as uncertainty of their functions result in extreme difficulties in analyzing the reliability by such models. To overcome such drawbacks, the soft computing techniques are useful alternative for modeling of complex systems and prediction applications. Hence, this paper provides a comparative structure for predicting the operational reliability in automotive manufacturing industry, using soft computing + statistical techniques. The results of comparative structure revealed that the soft computing techniques can estimate the reliability function with the lowest error in all cases. Based on the performance criteria, it was observed that among the soft computing techniques, the Adaptive Neuro-Fuzzy Inference System (ANFIS) model yields better results in most cases and thus can be used for predicting operational reliability, since it predicts the reliability more accurately and precisely than the statistical models. Ultimately, the maintenance intervals based on the ANFIS model are proposed to upgrade the reliability and safety of automotive manufacturing process.

Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy

Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy and entails high costs for health systems. Currently, no reliable labor proximity prediction techniques are available for clinical use. Regular checks by uterine electrohysterogram (EHG) for predicting preterm labor have been widely studied. The aim of the present study was to assess the feasibility of predicting labor with a 7- and 14-day time horizon in TPL women, who may be under tocolytic treatment, using EHG and/or obstetric data. Based on 140 EHG recordings, artificial neural networks were used to develop prediction models. Non-linear EHG parameters were found to be more reliable than linear for differentiating labor in under and over 7/14 days. Using EHG and obstetric data, the <7- and <14-day labor prediction models achieved an AUC in the test group of 87.1 ± 4.3% and 76.2 ± 5.8%, respectively. These results suggest that EHG can be reliable for predicting imminent labor in TPL women, regardless of the tocolytic therapy stage. This paves the way for the development of diagnostic tools to help obstetricians make better decisions on treatments, hospital stays and admitting TPL women, and can therefore reduce costs and improve maternal and fetal wellbeing.

Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have evidenced their capacity to solve dynamic, uncertain and complex tasks. The aim of this current study is to develop a hybrid artificial intelligence model called integrative Random Forest classifier with Genetic Algorithm optimization (RF-GA) for delay problem prediction. At first, related sources and factors of delay problems are identified. A questionnaire is adopted to quantify the impact of delay sources on project performance. The developed hybrid model is trained using the collected data of the previous construction projects. The proposed RF-GA is validated against the classical version of an RF model using statistical performance measure indices. The achieved results of the developed hybrid RF-GA model revealed a good resultant performance in terms of accuracy, kappa and classification error. Based on the measured accuracy, kappa and classification error, RF-GA attained 91.67%, 87% and 8.33%, respectively. Overall, the proposed methodology indicated a robust and reliable technique for project delay prediction that is contributing to the construction project management monitoring and sustainability.

Multiobjective Optimization for the Greener Synthesis of Chloromethyl Ethylene Carbonate by CO2 and Epichlorohydrin via Response Surface Methodology

In this paper, a statistical analysis with response surface methodology (RSM) has been used to investigate and optimize process variables for the greener synthesis of chloromethyl ethylene carbonate (CMEC) by carbon dioxide (CO2) and epichlorohydrin (ECH). Using the design expert software, a quadratic model was developed to study the interactions effect between four independent variables and the reaction responses. The adequacy of the model was validated by correlation between the experimental and predicted values of the responses using an analysis of variance (ANOVA) method. The proposed Box-Behnken design (BBD) method suggested 29 runs for data acquisition and modelling the response surface. The optimum reaction conditions of 353 K, 11 bar CO2 pressure, and 12 h using fresh 12% (w/w) Zr/ZIF-8 catalyst loading produced 93% conversion of ECH and 68% yield of CMEC. It was concluded that the predicted and experimental values are in excellent agreement with ±1.55% and ±1.54% relative errors from experimental results for both the conversion of ECH and CMEC yield, respectively. Therefore, statistical modelling using RSM can be used as a reliable prediction technique for system optimization for greener synthesis of chloromethyl ethylene carbonate via CO2 utilization.

Data-driven predictive models for daily electricity consumption of academic buildings

Academic buildings in a typical university campus occupy 42% of the total space and are responsible for nearly 50 percent of the total energy use and carbon emissions of the campus. Forecasting of energy consumption in this energy intensive building category could help higher education institutions in taking energy saving initiatives and in revising their building operating strategies. Reliable predictive techniques does not only help in forecasting a building’ energy consumption, but also help in identifying a variety of factors affecting the energy consumption of that building. This study attempts to forecast and benchmark the daily electricity consumption of an academic building situated in London, United Kingdom using two different data-driven modeling techniques, i.e., Multiple Regression and Artificial Neural Network. Hourly dataset for the electricity consumption was available for the period 2007 to 2011 from the smart meter whereas hourly data of different factors such as ambient temperature, relative humidity, wind speed and solar radiation were downloaded from the website of environmental research group of Kings College London. The performances of the two predictive models have been critically analyzed by comparing their predicted consumption with a real dataset of the same building for the year 2012. A comparison shows that both Multiple Regression (MR) and Artificial Neural Network (ANN) perform reasonably well with a Mean Absolute Percentage Error (MAPE) of 3.34% and 2.44% for working days and 5.12% and 4.59% for non-working days respectively. ANN performs slightly better than MR. This energy consumption forecasting approach can easily be adapted for predicting energy use of similar buildings.

Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data.

Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least 2-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.

Cooperative Position Prediction: Beyond Vehicle-to-Vehicle Relative Positioning

Reliable and accurate relative position prediction techniques are vital to the integrity of cooperative intelligent transportation systems (C-ITS) because most C-ITS safety applications should constantly access a (relative-) localization mechanism that satisfies not only a certain (relative-) position accuracy range but also a minimal computational complexity. A global navigation satellite system (GNSS) coupled with dedicated short-range communications (DSRC) links is a candidate for the localization system required for vehicular relative positioning. The vehicle-to-vehicle (V2V) exchange of safety-related data such as raw GNSS measurements, as accurate and frequent as possible, via DSRC links allows the vehicular localization mechanism to maintain real-time relative positioning (RRP) vectors. However, V2V DSRC safety messages may include inaccurate positioning data or may fast become outdated, and/or DSRC links may occasionally fail due to various adverse scenarios including network overheads that drop the bandwidth of vehicular ad-hoc networks. Hence, each DSRC-enabled vehicle may not necessarily know the latest positioning data of neighboring vehicles. This calls upon reliable and accurate relative position prediction mechanisms to cover the DSRC-related deficiencies of relative positioning. This paper, at first, studies the benefits of a terrestrial communications system complementing DSRC-based V2V RRP and then considers position prediction techniques suitable for V2V RRP and examines a few existing solutions and makes a detailed comparison based on a set of required positioning performance parameters for vehicle safety application.