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Liang, Jun, Xu Chen, Mei-ling He, Long Chen, Tao Cai, and Ning Zhu. "Car detection and classification using cascade model." IET Intelligent Transport Systems 12, no. 10 (December 1, 2018): 1201–9. http://dx.doi.org/10.1049/iet-its.2018.5270.
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Shah, Dhairya. "Car Image Classification and Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 2096–101. http://dx.doi.org/10.22214/ijraset.2021.38336.
Повний текст джерелаАнотація:
Abstract: Vehicle positioning and classification is a vital technology in intelligent transportation and self-driving cars. This paper describes the experimentation for the classification of vehicle images by artificial vision using Keras and TensorFlow to construct a deep neural network model, Python modules, as well as a machine learning algorithm. Image classification finds its suitability in applications ranging from medical diagnostics to autonomous vehicles. The existing architectures are computationally exhaustive, complex, and less accurate. The outcomes are used to assess the best camera location for filming, the vehicular traffic to determine the highway occupancy. An accurate, simple, and hardware-efficient architecture is required to be developed for image classification. Keywords: Convolutional Neural Networks, Image Classification, deep neural network, Keras, Tensorflow, Python, machine learning, dataset
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Bralić, Niko, and Josip Musić. "System for automatic detection and classification of cars in traffic." St open 3 (October 31, 2022): 1–31. http://dx.doi.org/10.48188/so.3.10.
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Objective: To develop a system for automatic detection and classification of cars in traffic in the form of a device for autonomic, real-time car detection, license plate recognition, and car color, model, and make identification from video.Methods: Cars were detected using the You Only Look Once (YOLO) v4 detector. The YOLO output was then used for classification in the next step. Colors were classified using the k-Nearest Neighbors (kNN) algorithm, whereas car models and makes were identified with a single-shot detector (SSD). Finally, license plates were detected using the OpenCV library and Tesseract-based optical character recognition. For the sake of simplicity and speed, the subsystems were run on an embedded Raspberry Pi computer.Results: A camera was mounted on the inside of the windshield to monitor cars in front of the camera. The system processed the camera’s video feed and provided information on the color, license plate, make, and model of the observed car. Knowing the license plate number provides access to details about the car owner, roadworthiness, car or license place reports missing, as well as whether the license plate matches the car. Car details were saved to file and displayed on the screen. The system was tested on real-time images and videos. The accuracies of car detection and car model classification (using 8 classes) in images were 88.5% and 78.5%, respectively. The accuracies of color detection and full license plate recognition were 71.5% and 51.5%, respectively. The system operated at 1 frame per second (1 fps).Conclusion: These results show that running standard machine learning algorithms on low-cost hardware may enable the automatic detection and classification of cars in traffic. However, there is significant room for improvement, primarily in license plate recognition. Accordingly, potential improvements in the future development of the system are proposed.
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Kette, Efraim Kurniawan Dairo. "MODIFIED CORRELATION WEIGHT K-NEAREST NEIGHBOR CLASSIFIER USING TRAINING DATASET CLEANING METHOD." Indonesian Journal of Physics 32, no. 2 (December 28, 2021): 20–25. http://dx.doi.org/10.5614/itb.ijp.2021.32.2.5.
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In pattern recognition, the k-Nearest Neighbor (kNN) algorithm is the simplest non-parametric algorithm. Due to its simplicity, the model cases and the quality of the training data itself usually influence kNN algorithm classification performance. Therefore, this article proposes a sparse correlation weight model, combined with the Training Data Set Cleaning (TDC) method by Classification Ability Ranking (CAR) called the CAR classification method based on Coefficient-Weighted kNN (CAR-CWKNN) to improve kNN classifier performance. Correlation weight in Sparse Representation (SR) has been proven can increase classification accuracy. The SR can show the 'neighborhood' structure of the data, which is why it is very suitable for classification based on the Nearest Neighbor. The Classification Ability (CA) function is applied to classify the best training sample data based on rank in the cleaning stage. The Leave One Out (LV1) concept in the CA works by cleaning data that is considered likely to have the wrong classification results from the original training data, thereby reducing the influence of the training sample data quality on the kNN classification performance. The results of experiments with four public UCI data sets related to classification problems show that the CAR-CWKNN method provides better performance in terms of accuracy.
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Qu, Ying, Na Yang, and Zhuangzhi Sun. "Research on classification management of car dealers in used car platform based on sd simulation model." Journal of Physics: Conference Series 1774, no. 1 (January 1, 2021): 012057. http://dx.doi.org/10.1088/1742-6596/1774/1/012057.
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Buzzelli, Marco, and Luca Segantin. "Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results." Sensors 21, no. 2 (January 15, 2021): 596. http://dx.doi.org/10.3390/s21020596.
Повний текст джерелаАнотація:
We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download.
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Weidner, Wiltrud, Fabian W. G. Transchel, and Robert Weidner. "Telematic driving profile classification in car insurance pricing." Annals of Actuarial Science 11, no. 2 (September 13, 2016): 213–36. http://dx.doi.org/10.1017/s1748499516000130.
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AbstractThis paper presents pricing innovations to German car insurance. The purpose is to provide an effective approach to adapting actuarial pricing decision to incorporate telematic data, which differs substantially from established tariff criteria in complexity and volume. A vehicle mobility model and a real-world sample of driving profiles form the input into the analysis. We propose an allocation of the driving profiles based on velocity and acceleration parameters to specific driving styles for evaluating the driving behaviour to subsequently enable discounts or surcharges on the premiums to obtain usage-based insurance premiums. The result is highly relevant for actuaries, who calculate the tariffs, but also for managers, as they have to make a pricing decision.
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Darney, P. Ebby. "Automatic Car Damage detection by Hybrid Deep Learning Multi Label Classification." December 2021 3, no. 4 (December 10, 2021): 341–52. http://dx.doi.org/10.36548/jaicn.2021.4.006.
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Automating image-based automobile insurance claims processing is a significant opportunity. In this research work, car damage categorization that is aided by the hybrid convolutional neural network approach is addressed and hence the deep learning-based strategies are applied. Insurance firms may leverage this paper's design and implementation of an automobile damage classification/detection pipeline to streamline car insurance claim policy. Using deep convolutional networks to detect car damage is now possible because of recent improvements in the artificial intelligence sector, mainly due to less computation time and higher accuracy with a hybrid transformation deep learning algorithm. In this paper, multiclass classification proposed to categorize the car damage parts such as broken headlight/taillight, glass fragments, damaged bonnet etc. are compiled into the proposed dataset. This model has been pre-trained on a wide-ranging and benchmark dataset due to the dataset's limited size to minimize overfitting and to understand more common properties of the dataset. To increase the overall proposed model’s performance, the CNN feature extraction model is trained with Resnet architecture with the coco car damage detection datasets and reaches a higher accuracy of 90.82%, which is much better than the previous findings on the comparable test sets.
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Hosameldeen, Osama. "Deep learning-based car seatbelt classifier resilient to weather conditions." International Journal of Engineering & Technology 9, no. 1 (February 25, 2020): 229. http://dx.doi.org/10.14419/ijet.v9i1.30050.
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Deep Learning is a very promising field in image classification. It leads to the automation of many real-world problems. Currently, Car seatbelt violation detection is done manually or partial manual. In this paper, an approach is proposed to make the seat belt detection process fully automated. To make the detection more accurate, sensors are set to detect the weather condition. When spe-cific weather condition is detected, the corresponding pre-trained model is assigned the detection task. In other words, a research is conducted to check the possibility of dividing the big-sized deep-learning model - that can classify car seatbelt, into sub-models each one can detect specific weather condition. Accordingly, a single specialized model is used for each weather condition, Deep convolutional neural network (CNN) model AlexNet is used in the detection/classification process. The proposed system is sensor based AlexNet (S-AlexNet). Results support our hypothesis that “Using single model for each weather condition is better than gen-eral model that support all weather conditions”. On average, previous approaches that trained single model for all weather condi-tions have accuracy less than 90%. The proposed S-AlexNet approach successfully reaches 90+% accuracy.
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Bernardi, Mario Luca, Marta Cimitile, Fabio Martinelli, and Francesco Mercaldo. "Driver and Path Detection through Time-Series Classification." Journal of Advanced Transportation 2018 (2018): 1–20. http://dx.doi.org/10.1155/2018/1758731.
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Driver identification and path kind identification are becoming very critical topics given the increasing interest of automobile industry to improve driver experience and safety and given the necessity to reduce the global environmental problems. Since in the last years a high number of always more sophisticated and accurate car sensors and monitoring systems are produced, several proposed approaches are based on the analysis of a huge amount of real-time data describing driving experience. In this work, a set of behavioral features extracted by a car monitoring system is proposed to realize driver identification and path kind identification and to evaluate driver’s familiarity with a given vehicle. The proposed feature model is exploited using a time-series classification approach based on a multilayer perceptron (MLP) network to evaluate their effectiveness for the goals listed above. The experiment is done on a real dataset composed of totally 292 observations (each observation consists of a given person driving a given car on a predefined path) and shows that the proposed features have a very good driver and path identification and profiling ability.
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R.R., Ayalapogu,, Pabboju, S., and Ramisetty, R.R. "Pathological Analysis of Brain Tumor in MRI Images Using CAR-UNET Model Architecture." CARDIOMETRY, no. 24 (November 30, 2022): 1086–97. http://dx.doi.org/10.18137/cardiometry.2022.24.10861097.
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The most dangerous type of organ failure in humans is a brain tumor. A brain tumor’s incorrect segmentation and classification are critical for treatment and early diagnosis. Several Deep neural network-based architectures have recently been developed to improve brain tumor classification performance. However, brain tumor classification performance must be improved, which is a difficult area of research. The goal of this study is to analyze different types of brain tumors and how to classify them to increase the survival rate of people with brain tumors. The CAR-U-Net (Concatenation and Residual) image classification method is proposed in this paper to help with brain tumor segmentation and classification research. The baseline U-Net architecture employs concatenation and residual connections. The changes in the network help in discovering varied features by expanding the specific receptive field. We consider two factors for a better diagnosis system: finding missing feature maps and eliminating unfeatured feature maps. The residual connection can solve the over learn or Null feature map problem, while the concatenation connection can solve the missing feature maps problems. This model has been tested on the BraTS2017 Challenge datasets. The network’s concatenation and residual connections, which are used for better deep supervision and tumor differentiation, are accurate. The accuracy, sensitivity, specificity, and dice score were used to compare the performance quantitatively. The proposed system achieved 94.12% Accuracy and 97.16% sensitivity which is higher than the existing systems such as U-net, Residual U-net and, Bayesian SVNN. Also, the proposed system got the dice score coefficient of 90.32. which is higher compared to conventional U-net and Residual U-net. The proposed CAR-UNET model outperformed current best-practice techniques. The high-performance capacity of this model can help bioinformatics, medicine, and early diagnosis researchers.
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Sun, Shengnan, Jipeng Huang, Juan Zhu, Yan Yu, and Luyao Zheng. "Research on Both the Classification and Quality Control Methods of the Car Seat Backrest Based on Machine Vision." Wireless Communications and Mobile Computing 2022 (March 15, 2022): 1–11. http://dx.doi.org/10.1155/2022/3106313.
Повний текст джерелаАнотація:
In order to solve the problems of slow manual inspection speed and low fault detection accuracy of car seat back parts, this article using Q company’s car seat back parts researches and designs a car seat back classification and quality inspection screening system. Firstly, SURF (speeded up robust features) is combined with the CNN (convolutional neural network) to classify three types of car seat backrests: A, B, and C. Then, to establish the spring hook angle detection model of the car seat back to detect the misfitting and omission of the Class A car seat back springs, experimental results showed that the neural network-based car seat back detection method proposed in this paper had a feature point mismatch rate, which is less than 1.5% in the classification and recognition of car seat backs. The recognition rate of the training sample was 100% and that of the test sample was 99.56%. The accuracy rate of detection when inspecting 50 car seat backrests reached 98%, and the test results showed that the system can effectively reduce labor costs and improve the detection efficiency of auto parts.
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Kubiczek, Jakub, and Bartłomiej Hadasik. "Segmentation of Passenger Electric Cars Market in Poland." World Electric Vehicle Journal 12, no. 1 (February 10, 2021): 23. http://dx.doi.org/10.3390/wevj12010023.
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Striving to achieve sustainable development goals and taking care of the environment into the policies of car manufacturers forced the search for alternative sources of vehicle propulsion. One way to implement a sustainable policy is to use electric motors in cars. The observable development of the electric car market provides consumers with a wide spectrum of choices for a specific model that would meet their expectations. Currently, there are 53 different electric car models on the primary market in Poland. The aim of the article was to present the performed market segmentation, focused on identifying the similarities in the characteristics of electric car models on the Polish market and proposing their groupings. Based on the classification by the hierarchical cluster analysis algorithm (Ward’s method, squared Euclidean distance), the market division into 2, 3, and 4 groups was proposed. The Polish EV market segmentation took place not only in terms of the size and class of the car but primarily in terms of performance and overall quality of the vehicle. The performed classification did not change when the price was additionally included as a variable. It was also proposed to divide the market into 4 segments named: Premium, City, Small, and Sport. The segmentation carried out in this way helps to better understand the structure of the electric car market.
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Piwowarski, Paweł, and Włodzimierz Kasprzak. "Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison." Applied Sciences 11, no. 13 (June 24, 2021): 5863. http://dx.doi.org/10.3390/app11135863.
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We consider the problem of image set comparison, i.e., to determine whether two image sets show the same unique object (approximately) from the same viewpoints. Our proposition is to solve it by a multi-stream fusion of several image recognition paths. Immediate applications of this method can be found in fraud detection, deduplication procedure, or visual searching. The contribution of this paper is a novel distance measure for similarity of image sets and the experimental evaluation of several streams for the considered problem of same-car image set recognition. To determine a similarity score of image sets (this score expresses the certainty level that both sets represent the same object visible from the same set of views), we adapted a measure commonly applied in blind signal separation (BSS) evaluation. This measure is independent of the number of images in a set and the order of views in it. Separate streams for object classification (where a class represents either a car type or a car model-and-view) and object-to-object similarity evaluation (based on object features obtained alternatively by the convolutional neural network (CNN) or image keypoint descriptors) were designed. A late fusion by a fully-connected neural network (NN) completes the solution. The implementation is of modular structure—for semantic segmentation we use a Mask-RCNN (Mask regions with CNN features) with ResNet 101 as a backbone network; image feature extraction is either based on the DeepRanking neural network or classic keypoint descriptors (e.g., scale-invariant feature transform (SIFT)) and object classification is performed by two Inception V3 deep networks trained for car type-and-view and car model-and-view classification (4 views, 9 car types, and 197 car models are considered). Experiments conducted on the Stanford Cars dataset led to selection of the best system configuration that overperforms a base approach, allowing for a 67.7% GAR (genuine acceptance rate) at 3% FAR (false acceptance rate).
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Kaewwichian, Patiphan. "Multiclass Classification with Imbalanced Datasets for Car Ownership Demand Model – Cost-Sensitive Learning." Promet - Traffic&Transportation 33, no. 3 (May 31, 2021): 361–71. http://dx.doi.org/10.7307/ptt.v33i3.3728.
Повний текст джерелаАнотація:
In terms of the travel demand prediction from the household car ownership model, if the imbalanced data were used to support the transportation policy via a machine learning model, it would negatively affect the algorithm training process. The data on household car ownership obtained from the study project for the expressway preparation in the Khon Kaen Province (2015) was an unbalanced dataset. In other words, the number of members of the minority class is lower than the rest of the answer classes. The result is a bias in data classification. Consequently, this research suggested balancing the datasets with cost-sensitive learning methods, including decision trees, k-nearest neighbors (kNN), and naive Bayes algorithms. Before creating the 3-class model, a k-folds cross-validation method was applied to classify the datasets to define true positive rate (TPR) for the model’s performance validation. The outcome indicated that the kNN algorithm demonstrated the best performance for the minority class data prediction compared to other algorithms. It provides TPR for rural and suburban area types, which are region types with very different imbalance ratios, before balancing the data of 46.9% and 46.4%. After balancing the data (MCN1), TPR values were 84.4% and 81.4%, respectively.
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Zhou, Qianwei, Baoqing Li, Zhijun Kuang, Dongfeng Xie, Guanjun Tong, Liping Hu, and Xiaobing Yuan. "A quarter-car vehicle model based feature for wheeled and tracked vehicles classification." Journal of Sound and Vibration 332, no. 26 (December 2013): 7279–89. http://dx.doi.org/10.1016/j.jsv.2013.08.042.
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Wang, Yi Qiang, Rui Jian Huang, Tian Yi Xu, and Ke Hong Tang. "Vehicle Model Recognition Based on Fuzzy Pattern Recognition Method." Advanced Materials Research 383-390 (November 2011): 4799–802. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.4799.
Повний текст джерелаАнотація:
The method based on the theory of Fuzzy Pattern Recognition is divided into three parts. Firstly, use Hough transformation to extract the feature points of vehicles, and use the ratio between two absolute distance of adjacent feature points as the characteristic values of vehicles; secondly, use Fuzzy C-mean Classification to handle feature data of 75 car model, then establish a degree of membership matrix as the sample space; thirdly, consider the classification algorithm based on fuzzy approach degree and the credibility of the vehicle feature to propose a weighted close- degree recognition algorithm. This recognition method has a good effect.
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Ahmed, Ahmed Abdelmoamen, and Sheikh Ahmed. "A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition." Algorithms 14, no. 11 (October 30, 2021): 317. http://dx.doi.org/10.3390/a14110317.
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Automatic Number Plate Recognition (ANPR) has been widely used in different domains, such as car park management, traffic management, tolling, and intelligent transport systems. Despite this technology’s importance, the existing ANPR approaches suffer from the accurate identification of number plats due to its different size, orientation, and shapes across different regions worldwide. In this paper, we are studying these challenges by implementing a case study for smart car towing management using Machine Learning (ML) models. The developed mobile-based system uses different approaches and techniques to enhance the accuracy of recognizing number plates in real-time. First, we developed an algorithm to accurately detect the number plate’s location on the car body. Then, the bounding box of the plat is extracted and converted into a grayscale image. Second, we applied a series of filters to detect the alphanumeric characters’ contours within the grayscale image. Third, the detected the alphanumeric characters’ contours are fed into a K-Nearest Neighbors (KNN) model to detect the actual number plat. Our model achieves an overall classification accuracy of 95% in recognizing number plates across different regions worldwide. The user interface is developed as an Android mobile app, allowing law-enforcement personnel to capture a photo of the towed car, which is then recorded in the car towing management system automatically in real-time. The app also allows owners to search for their cars, check the case status, and pay fines. Finally, we evaluated our system using various performance metrics such as classification accuracy, processing time, etc. We found that our model outperforms some state-of-the-art ANPR approaches in terms of the overall processing time.
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Zhao, Yinan, and Boliang Lin. "Optimization of the Classification Yard Location Problem Based on Train Service Network." Symmetry 12, no. 6 (May 26, 2020): 872. http://dx.doi.org/10.3390/sym12060872.
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This paper investigates the problems of locating yards and allocating works of train formation among yards based on the train service network. It not only involves the modifications of the scales of existing yards, either improving, or downsizing or even demolishing, but also the determinations of building new yards in a given rail network. Besides, the train service network is also taken into consideration, so that the car-hour costs that are incurred from the reclassification and accumulation operations of railcars can be optimized simultaneously. The accumulation parameter setting is symmetrical on both directions of traffic flows. A binary integer programming model is first proposed for optimizing the train service network, the solution of which is employed as a benchmark of the further integrated optimization. Based on this, a nonlinear joint optimization model is developed aiming at striking a balance between the capital investment and car-hour consumptions, with the constraints of the reclassification capacities of yards and the number of sorting tracks, and multiple logical relations among decision variables. Corresponding linearization techniques are introduced for transforming the nonlinear models into the linear ones. Finally, an exact solving approach is presented with computational results that are based on a real-world based case to illustrate the efficacy of the linear model.
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Sundt, Bjørn. "Two Credibility Regression Approaches for the Classification of Passenger Cars in a Multiplicative Tariff." ASTIN Bulletin 17, no. 1 (April 1987): 41–70. http://dx.doi.org/10.2143/ast.17.1.2014983.
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AbstractIn the present paper we present two credibility regression models for the classification of passenger cars. As regressors we use technical variables like price, weight, etc. In both models we derive credibility estimators and find expressions for their estimation errors. Estimators for structure parameters are proposed. A numerical example based on real data is given. The second model is hierarchical with a level for make of car.
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Harmanta, Harmanta, Nur M. Adhi Purwanto, and Fajar Oktiyanto Oktiyanto. "INTERNALISASI SEKTOR PERBANKAN DALAM MODEL DSGE." Buletin Ekonomi Moneter dan Perbankan 17, no. 1 (December 22, 2014): 23–60. http://dx.doi.org/10.21098/bemp.v17i1.43.
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We build DSGE model for small open economy with financial friction in the form of collateral constrain on banking sector, designed for Indonesian economy. The constructed model is capable to simulate the monetary policy (Bank Indonesia rate) and macroprudential policy (reserve requirement, capital adequacy ratio – CAR, and loan to value – LTV). By internalizing banking sector into the model, this model also enable us to simulate the impact of any shock originated from banking sector. Keywords: monetary policy, DSGE with banking sector, macroprudential policy JEL Classification: E32, E44, E52, E58
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Sintha, Lis. "Bankruptcy Prediction Model of Banks in Indonesia Based on Capital Adequacy Ratio." Journal of Finance and Banking Review Vol. 4 (1) Jan-Mar 2019 4, no. 1 (March 19, 2019): 08–16. http://dx.doi.org/10.35609/jfbr.2019.4.1(2).
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Objective - The purpose of this study is to examine the influence of capital on bankruptcy banks. The hypothesis of this research is that capital has an effect on the bankruptcy of a bank. Methodology/Technique - This research examines financial reports between 2005-2014. An econometric model with a logistical regression analysis technique is used. In this study, capital is measured by CAR, taking into account credit risk; CAR by taking into account market risk; Ratio of Obligation to Provide Minimum Capital for Credit Risk and Operational Risk; Ratio of Minimum Capital Adequacy Ratio for Credit Risk, Operational Risk and Market Risk; Capital Adequacy Requirements (CAR). Findings - The results show that the capital adequacy ratio for market ratio and capital adequacy ratio for credit ratio and operational ratio support the research hypothesis and can form a logit model. The test results of CAR by taking into account credit risk, Minimum Capital Requirement Ratio for Credit Risk, Operational Risk and Market Risk and Minimum Capital Provision Obligations do not support the research hypothesis. Novelty – This paper contribute to bank bankruptcy prediction models based on time dimension and bank groups using financial ratios which are expected can influence bank in bankrupt condition. Type of Paper - Empirical. Keywords: Banking crisis, Cost of bankruptcy, Adequacy Ratio, Financial ratios, Prediction models JEL Classification: G32, G33, G39. DOI: https://doi.org/10.35609/jfbr.2019.4.1(2)
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Paulraj, Paulraj, Allan Andrew Melvin, and Yaacob Sazali. "Car Cabin Interior Noise Classification Using Temporal Composite Features and Probabilistic Neural Network Model." Applied Mechanics and Materials 471 (December 2013): 64–68. http://dx.doi.org/10.4028/www.scientific.net/amm.471.64.
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Determination of vehicle comfort is important because continuous exposure to the noise and vibration leads to health problems for the driver and passengers. In this paper, a vehicle comfort level classification system has been proposed to detect the comfort level in cars using artificial neural network. A database consisting of sound samples obtained from 30 local cars is used. In the stationary condition, the sound pressure level is measured at 1300 RPM, 2000 RPM and 3000 RPM. In the moving condition, the sound is recorded while the car is moving at 30 km/h up to 110 km/h. Subjective test is conducted to find the Jurys evaluation for the specific sound sample. The correlation between the subjective and the objective evaluation is also tested. The relationship between the subjective results and the sound metrics is modelled using Probabilistic Neural Network. It is found from the research that the Temporal Composite Feature gives better classification accuracy for both stationary and moving condition model, 89.51% and 85.61% respectively.
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Hou, Wenhui, and Caiwen Niu. "A Consumer-Oriented Car Style Evaluation System Based on Fuzzy Mathematics and Neural Network." International Journal of Circuits, Systems and Signal Processing 15 (August 24, 2021): 986–95. http://dx.doi.org/10.46300/9106.2021.15.106.
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As an important link in product development, car style evaluation could ensure the quality of car style design, making the design more efficient, laying the foundation for production planners, production managers, and investment decision-makers in automobile manufacturing. The consumer-centered evaluation should accurately reflect the psychological cognition and subjective feelings of consumers. However, the current studies have not provided a unified evaluation standard, nor fully utilized the massive data on the evaluations made by consumers. Considering in advantages of fuzzy mathematics and neural network in processing massive data on consumer evaluations, this paper designs a consumer-oriented car style evaluation system based on these two techniques. Firstly, a scientific evaluation index system was designed for consumer-oriented car style evaluation, the index scores were classified into different levels, and a judgment matrix was constructed for indices on each layer and subject to consistency check. Next, absolute weights were assigned to alternatives, and the corresponding fuzzy membership functions were determined, producing a fuzzy comprehensive evaluation (FCE) model based on analytic hierarchy process (AHP) (AHP-FCE model) for car style evaluation. Furthermore, car styles were categorized by appearance structure, and the car style samples were parametrized for evaluation. Finally, particle swarm optimization (PSO) was improved, and then combined with backpropagation neural network (BPNN) into a classification model for consumer-oriented car style evaluation. The proposed consumer-oriented car style evaluation model was proved effective and superior through experiments. The results offer a reference for the application of the model in other evaluation scenarios
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Niklas, Ulrich, Sascha von Behren, Tamer Soylu, Johanna Kopp, Bastian Chlond, and Peter Vortisch. "Spatial Factor—Using a Random Forest Classification Model to Measure an Internationally Comparable Urbanity Index." Urban Science 4, no. 3 (August 10, 2020): 36. http://dx.doi.org/10.3390/urbansci4030036.
Повний текст джерелаАнотація:
Travel behavior can be determined by its spatial context. If there are many shops and restaurants in close proximity, various activities can be done by walking or cycling, and a car is not needed. It is also more difficult (e.g., parking space, traffic jams) to use a car in high-density areas. Overall, travel behavior and dependencies on travel behavior are influenced by urbanity. These relationships have so far only been examined very selectively (e.g., at city level) and not in international comparison. In this study we define an Urbanity Index (UI) at zip code level, which considers factors influencing mobility, international comparability, reproducibility as well as practical application and the development of a scalable methodology. In order to describe urbanity, data were collected regarding spatial structure, population, land use, and public transport. We developed the UI using a supervised machine learning technique which divides zip codes into four area types: (1) super-urban, (2) urban, (3) suburban/small town, (4) rural. To train the model, the perception from experts in known zip codes concerning urbanity and mobility was set as ground truth. With the UI, it is possible to compare countries (Germany and France) with a uniform definition and comparable datasets.
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Vu Dinh, Tuan, Nhat-Duc Hoang, and Xuan-Linh Tran. "Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam." Applied and Environmental Soil Science 2021 (April 2, 2021): 1–14. http://dx.doi.org/10.1155/2021/6665485.
Повний текст джерелаАнотація:
Soil erosion induced by rainfall under prevailing conditions is a prominent problem to farmers in tropical sloping lands of Northeast Vietnam. This study evaluates possibility of predicting erosion status by machine learning models, including fuzzy k-nearest neighbor (FKNN), artificial neural network (ANN), support vector machine (SVM), least squares support vector machine (LSSVM), and relevance vector machine (RVM). Model evaluation employed a historical dataset consisting of ten explanatory variables and soil erosion featured four different land use managements on hillslopes in Northwest Vietnam. All 236 data samples representing soil erosion/nonerosion events were randomly prepared (80% for training and 20% for testing) to assess the robustness of the five models. This subsampling process was repeatedly carried out by 30 rounds to eliminate the issue of randomness in data selection. Classification accuracy rate (CAR) and area under receiver operating characteristic (AUC) were used to evaluate performance of the five models. Significant difference between different algorithms was verified by the Wilcoxon test. Results of the study showed that RVM model achieves the best outcomes in both training (CAR = 92.22% and AUC = 0.98) and testing phases (CAR = 91.94% and AUC = 0.97). Four other learning algorithms also demonstrated good performance as indicated by their CAR values surpassing 80% and AUC values greater than 0.9. Hence, these results strongly confirm the efficacy of applying machine learning models for soil erosion prediction.
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Корнеев, Алексей, Aleksey Korneev, Александр Ермаков, Aleksandr Ermakov, Марина Руднева, and Marina Rudneva. "Development of a model of car tourism in moscow region." Services in Russia and abroad 8, no. 7 (December 10, 2014): 0. http://dx.doi.org/10.12737/7472.
Повний текст джерелаАнотація:
The paper attempts to develop a model of automobile tourism in Moscow region. The authors consider the theoretical basis for the development of automobile tourism in the Russian Federation. It is noted that the car tourism can be considered as the most promising for the organization of travel inside the home region or in neighboring regions. Based on review of the literature on this subject the classification of types of automobile tourism is suggested, indicating that this type of tourism fits a wide range of population. The possibility of an active development of automobile tourism in Moscow region is studied. Analyzed are tourist resources such as transport infrastructure, accommodation facilities, cultural, historical and natural sites. On the basis of this analysis the authors creat an enlarged model of automobile tourism in Moscow region. The authors suggest measures that need to be implemented in Moscow Region by Government to create favorable conditions for the development of automobile tourism. The results of the public opinion poll on the topic "The main factors influencing the development of automobile tourism in the Moscow region" are highlighted, showing the importance of the development of road infrastructure and the development of models for road routes in Moscow region. In conclusion, the authors conclude that the Moscow region is a promising area for the development of automobile tourism.
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Aljasim, Mustafa, and Rasha Kashef. "E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model." Sensors 22, no. 5 (February 26, 2022): 1858. http://dx.doi.org/10.3390/s22051858.
Повний текст джерелаАнотація:
The increasing number of car accidents is a significant issue in current transportation systems. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. A lot of efforts have been made to tackle the problem of driver distraction; however, no optimal solution is provided. A practical approach to solving this problem is implementing quantitative measures for driver activities and designing a classification system that detects distracting actions. In this paper, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize the level of distractions and increase in-car awareness for improved safety. This paper proposes E2DR, a new scalable model that uses stacking ensemble methods to combine two or more deep learning models to improve accuracy, enhance generalization, and reduce overfitting, with real-time recommendations. The highest performing E2DR variant, which included the ResNet50 and VGG16 models, achieved a test accuracy of 92% as applied to state-of-the-art datasets, including the State Farm Distracted Drivers dataset, using novel data splitting strategies.
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Wang, Wenjing, Yanyan Chen, Haodong Sun, and Yusen Chen. "Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data." Sustainability 13, no. 21 (November 8, 2021): 12298. http://dx.doi.org/10.3390/su132112298.
Повний текст джерелаАнотація:
Observing and analyzing travel behavior is important, requiring understanding detailed individual trip chains. Existing studies on identifying travel modes have mainly used some travel features based on GPS and survey data from a small number of users. However, few studies have focused on evaluating the effectiveness of these models on large-scale location data. This paper proposes to use travel location data from an Internet company and travel data from transport department to identify travel modes. A multiple binary classification model based on data fusion is used to find out the relationship between travel mode and different features. Firstly, we enlisted volunteers to collect travel data and record their travel trip process using a custom-developed WeChat program. Secondly, we have developed three binary classification models to explain how different attributes can be used to model travel mode. Compared with one multi-classification model, the accuracy of our model improved significantly, with prediction accuracies of 0.839, 0.899, 0.742, 0.799, and 0.799 for walk, metro, bike, bus, and car, respectively. This suggests that the model could be applied not only in engineering practice to identify the trip chain from Internet location data but also in decision support for transportation planners.
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Chikara, Rupesh Kumar, and Li-Wei Ko. "Neural Activities Classification of Human Inhibitory Control Using Hierarchical Model." Sensors 19, no. 17 (September 1, 2019): 3791. http://dx.doi.org/10.3390/s19173791.
Повний текст джерелаАнотація:
Human inhibitory control refers to the suppression of behavioral response in real environments, such as when driving a car or riding a motorcycle, playing a game and operating a machine. The P300 wave is a neural marker of human inhibitory control, and it can be used to recognize the symptoms of attention deficit hyperactivity disorder (ADHD) in human. In addition, the P300 neural marker can be considered as a stop command in the brain-computer interface (BCI) technologies. Therefore, the present study of electroencephalography (EEG) recognizes the mindset of human inhibition by observing the brain dynamics, like P300 wave in the frontal lobe, supplementary motor area, and in the right temporoparietal junction of the brain, all of them have been associated with response inhibition. Our work developed a hierarchical classification model to identify the neural activities of human inhibition. To accomplish this goal phase-locking value (PLV) method was used to select coupled brain regions related to inhibition because this method has demonstrated the best performance of the classification system. The PLVs were used with pattern recognition algorithms to classify a successful-stop versus a failed-stop in left-and right-hand inhibitions. The results demonstrate that quadratic discriminant analysis (QDA) yielded an average classification accuracy of 94.44%. These findings implicate the neural activities of human inhibition can be utilized as a stop command in BCI technologies, as well as to identify the symptoms of ADHD patients in clinical research.
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Sean, Brown, Hall Caitlin, Galliera Raffaele, and Bagui Sikha. "Object Detection and Ship Classification Using YOLOv5." BOHR International Journal of Computer Science 1, no. 1 (2021): 124–33. http://dx.doi.org/10.54646/bijcs.017.
Повний текст джерелаАнотація:
Using a public dataset of images of maritime vessels provided by Analytics Vidhya, manual annotations were made on a subsample of images with Roboflow using the ground truth classifications provided by the dataset. YOLOv5, a prominent open source family of object detection models that comes with an out-of-the-box pre-training on the Common Objects in Context (COCO) dataset, was used to train on annotations of subclassifications of maritime vessels. YOLOv5 provides significant results in detecting a boat. The training, validation, and test set of images trained YOLOv5 in the cloud using Google Colab. Three of our five subclasses, namely, cruise ships, ROROs (Roll On Roll Off, typically car carriers), and military ships, have very distinct shapes and features and yielded positive results. Two of our subclasses, namely, the tanker and cargo ship, have similar characteristics when the cargo ship is unloaded and not carrying any cargo containers. This yielded interesting misclassifications that could be improved in future work. Our trained model resulted in the validation metric of mean Average Precision (mAP@.5) of 0.932 across all subclassification of ships.
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Dayal, Aveen, Sreenivasa Reddy Yeduri, Balu Harshavardan Koduru, Rahul Kumar Jaiswal, J. Soumya, M. B. Srinivas, Om Jee Pandey, and Linga Reddy Cenkeramaddi. "Lightweight deep convolutional neural network for background sound classification in speech signals." Journal of the Acoustical Society of America 151, no. 4 (April 2022): 2773–86. http://dx.doi.org/10.1121/10.0010257.
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Recognizing background information in human speech signals is a task that is extremely useful in a wide range of practical applications, and many articles on background sound classification have been published. It has not, however, been addressed with background embedded in real-world human speech signals. Thus, this work proposes a lightweight deep convolutional neural network (CNN) in conjunction with spectrograms for an efficient background sound classification with practical human speech signals. The proposed model classifies 11 different background sounds such as airplane, airport, babble, car, drone, exhibition, helicopter, restaurant, station, street, and train sounds embedded in human speech signals. The proposed deep CNN model consists of four convolution layers, four max-pooling layers, and one fully connected layer. The model is tested on human speech signals with varying signal-to-noise ratios (SNRs). Based on the results, the proposed deep CNN model utilizing spectrograms achieves an overall background sound classification accuracy of 95.2% using the human speech signals with a wide range of SNRs. It is also observed that the proposed model outperforms the benchmark models in terms of both accuracy and inference time when evaluated on edge computing devices.
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Avikal, Shwetank, Rohit Singh, and Rashmi Rashmi. "QFD and Fuzzy Kano model based approach for classification of aesthetic attributes of SUV car profile." Journal of Intelligent Manufacturing 31, no. 2 (September 15, 2018): 271–84. http://dx.doi.org/10.1007/s10845-018-1444-5.
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James, Rachel M., and Britton E. Hammit. "Identifying Contributory Factors to Heterogeneity in Driving Behavior: Clustering and Classification Approach." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 10 (May 18, 2019): 343–53. http://dx.doi.org/10.1177/0361198119849404.
Повний текст джерелаАнотація:
Previous research efforts using aerially collected trajectory-level data have confirmed the existence of inter-driver heterogeneity, where different car-following model (CFM) specifications and calibrated parameter sets are required to adequately capture drivers’ driving behavior. This research hypothesizes that there also exist clusters of drivers whose behavior is sufficiently similar to be considered a homogeneous group. To test this hypothesis, this study applies a 664-trip sample of trajectory-level data from the SHRP2 Naturalistic Driving Study to calibrate the Gipps, Intelligent Driver Model, and Wiedemann 99 CFMs. Using the calibrated parameter coefficients, this research provides evidence of the existence of homogeneous groups of driving behavior using the expectation maximization clustering algorithm. Four classification algorithms are then applied to classify the trip’s cluster ID according to driver demographics. Driver age, income, and marital status were most commonly identified as important classification attributes, while gender, work status, and living status appear less significant. The classification algorithms, which sought to classify a trip’s behavioral cluster ID by the driver-specific attributes, achieved the highest accuracy rate when predicting the desired velocity car-following parameter clusters. This effort illustrates that some drivers drive sufficiently alike to form a cluster of similar behavior; moreover, it was confirmed that driver-specific attributes can be utilized to classify drivers into these homogeneous driver groups.
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Pandey, Manish Kumar, Anu Saini, Karthikeyan Subbiah, Nalini Chintalapudi, and Gopi Battineni. "Improved Carpooling Experience through Improved GPS Trajectory Classification Using Machine Learning Algorithms." Information 13, no. 8 (August 3, 2022): 369. http://dx.doi.org/10.3390/info13080369.
Повний текст джерелаАнотація:
Globally, smart cities, infrastructure, and transportation have led to a rise in vehicle numbers, resulting in an increasing number of problems. This includes problems such as air pollution, noise pollution, high energy consumption, and people’s health. A viable solution to these problems is carpooling, which involves sharing vehicles between people going to the same location. As carpooling solutions become more popular, they need to be implemented efficiently. Data analytics can help people make informed decisions when selecting a ride (Car or Bus). We applied machine learning algorithms to select the desired ride (Car or Bus) and used feature ranking algorithms to identify the foremost traits for selecting the desired ride. Based on the performance evaluation metric, 11 classifiers were used for the experiment. In terms of selecting the desired ride, Random Forest performs best. Using ten-fold cross-validation, we obtained a sensitivity of 87.4%, a specificity of 73.7%, an accuracy of 81.0%, a sensitivity of 90.8%, a specificity of 77.6%, and an accuracy of 84.7% using leave-one-out cross-validation. To identify the most favorable characteristics of the Ride (Car or Bus), the recursive elimination of features algorithm was applied. By identifying the factors contributing to users’ experience, the service providers will be able to rectify those factors to increase business. It has been determined that the weather can make or break the user experience. This model will be used to quantify and map intrinsic and extrinsic sentiments of the people and their interactions with locality, socio-economic conditions, climate, and environment.
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Liu, Guichi, Lei Gao, and Lin Qi. "Hyperspectral Image Classification via Multi-Feature-Based Correlation Adaptive Representation." Remote Sensing 13, no. 7 (March 25, 2021): 1253. http://dx.doi.org/10.3390/rs13071253.
Повний текст джерелаАнотація:
In recent years, representation-based methods have attracted more attention in the hyperspectral image (HSI) classification. Among them, sparse representation-based classifier (SRC) and collaborative representation-based classifier (CRC) are the two representative methods. However, SRC only focuses on sparsity but ignores the data correlation information. While CRC encourages grouping correlated variables together but lacks the ability of variable selection. As a result, SRC and CRC are incapable of producing satisfied performance. To address these issues, in this work, a correlation adaptive representation (CAR) is proposed, enabling a CAR-based classifier (CARC). Specifically, the proposed CARC is able to explore sparsity and data correlation information jointly, generating a novel representation model that is adaptive to the structure of the dictionary. To further exploit the correlation between the test samples and the training samples effectively, a distance-weighted Tikhonov regularization is integrated into the proposed CARC. Furthermore, to handle the small training sample problem in the HSI classification, a multi-feature correlation adaptive representation-based classifier (MFCARC) and MFCARC with Tikhonov regularization (MFCART) are presented to improve the classification performance by exploring the complementary information across multiple features. The experimental results show the superiority of the proposed methods over state-of-the-art algorithms.
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Kamal, Ilias, Khalid Housni, and Youssef Hadi. "Online dictionary learning for car recognition using sparse coding and LARS." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 1 (March 1, 2020): 164. http://dx.doi.org/10.11591/ijai.v9.i1.pp164-174.
Повний текст джерелаАнотація:
<p>The bag of feature method coupled with online dictionary learning is the basis of our car make and model recognition algorithm. By using a sparse coding computing technique named LARS (Least Angle Regression) we learn a dictionary of codewords over a dataset of Square Mapped Gradient feature vectors obtained from a densely sampled narrow patch of the front part of vehicles. We then apply SVMs (Support Vector Machines) and KMeans supervised classification to obtain some promising results.</p>
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Junoh, Ahmad Kadri, Zulkifli Mohd Nopiah, and Ahmad Kamal Ariffin. "Application of Feed-Forward Neural Networks for Classifying Acoustics Levels in Vehicle Cabin." Applied Mechanics and Materials 471 (December 2013): 40–44. http://dx.doi.org/10.4028/www.scientific.net/amm.471.40.
Повний текст джерелаАнотація:
Vehicle acoustical comfort and vibration in a passenger car cabin are the main factors that attract a buyer in car purchase. Numerous studies have been carried out by automotive researchers to identify and classify the acoustics level in the vehicle cabin. The objective is to form a special benchmark for acoustics level that may be referred for any acoustics improvement purpose. This study is focused on the sound quality change over the engine speed [rp to recognize the noise pattern experienced in the vehicle cabin. Since it is difficult for a passenger to express, and to evaluate the noise experienced or heard in a numerical scale, a neural network optimization approach is used to classify the acoustics levels into groups of noise annoyance levels. A feed forward neural network technique is applied for classification algorithm, where it can be divided into two phases: Learning Phase and Classification Phase. The developed model is able to classify the acoustics level into numerical scales which are meaningful for evaluation purposes.
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Lu, Eric Hsueh-Chan, Michal Gozdzikiewicz, Kuei-Hua Chang, and Jing-Mei Ciou. "A Hierarchical Approach for Traffic Sign Recognition Based on Shape Detection and Image Classification." Sensors 22, no. 13 (June 24, 2022): 4768. http://dx.doi.org/10.3390/s22134768.
Повний текст джерелаАнотація:
In recent years, the development of self-driving cars and their inclusion in our daily life has rapidly transformed from an idea into a reality. One of the main issues that autonomous vehicles must face is the problem of traffic sign detection and recognition. Most works focusing on this problem utilize a two-phase approach. However, a fast-moving car has to quickly detect the sign as seen by humans and recognize the image it contains. In this paper, we chose to utilize two different solutions to solve tasks of detection and classification separately and compare the results of our method with a novel state-of-the-art detector, YOLOv5. Our approach utilizes the Mask R-CNN deep learning model in the first phase, which aims to detect traffic signs based on their shapes. The second phase uses the Xception model for the task of traffic sign classification. The dataset used in this work is a manually collected dataset of 11,074 Taiwanese traffic signs collected using mobile phone cameras and a GoPro camera mounted inside a car. It consists of 23 classes divided into 3 subclasses based on their shape. The conducted experiments utilized both versions of the dataset, class-based and shape-based. The experimental result shows that the precision, recall and mAP can be significantly improved for our proposed approach.
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Min, Dongsoon, Trevor Waite, and Birsen Donmez. "Collision Risk Assessment Using Naturalistic Data from a Rent-A-Car Fleet." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 2030. http://dx.doi.org/10.1177/1071181319631481.
Повний текст джерелаАнотація:
We analyzed commercial fleet operations data collected by a South Korean rent-a-car company, SK Networks Co. Ltd., to evaluate the differences between collision-free and collision-involved drivers with the ultimate goal of predicting driver collision risk. The first objective was to identify critical variables related to collision risk. The second objective was to build and compare classification models to predict the colli-sion involvement of a driver. Data used in the analysis were collected through Long-Range (LoRa) Internet of Things (IoT) modem-Fleet management system (FMS) devices, a first commercial implementation of LoRa modems in the vehicle. These devices have five main built-in modules, i.e., On-Board Diagnostics (OBD-II) Connector, GPS, LoRa modem, Gravity sensor, and Bluetooth. They can communicate with the vehi-cle, the driver’s smartphone, and the host server. Data from 3,854 drivers with a total of 2.19 million trips recorded in 2018 were explored. Out of these 3,854 drivers, 514 (13.3%) were involved in at least one collision. Predictor variables were selected based on previous research that uti-lized naturalistic data to identify factors affecting collision risk (Dingus et al., 2016; Tselentis, Yannis, & Vlahogianni, 2016; Bian, Yang, Zhao, & Liang, 2018; Jin et al., 2018). Forty-eight predictor variables that may affect collision risk were selected, which can be categorized into two groups: 27 variables were related to the business and the environment, characterized by how much drivers traveled, in what type of vehicles, on what types of roads, and during what times of the day; the other 22 variables were driving behavior-related variables, capturing overspeeding, potential fatigue, rapid speed changes, and counts of traffic regulation violations. After a feature selection phase based on univariate analysis, nine variables were select-ed to be used in the classification models. These selected vari-ables are running driving time (driving time excluding idling time), trip frequency per thousand kilometer driving, accumu-lated count of violation, accumulated amount of fine, the per-centage of trips driving a compact car (<1,000 cc), the per-centage of trips driving older than a 2016 car model, the per-centage of trips during 6 a.m.-9 a.m., the percentage of trips that ended during 2 a.m.-7 a.m., and the sum of rapid accelera-tion and deceleration frequencies per kilometer. A total of twenty classification models were built and compared to classify collision-involved and non-collision in-volved drivers: 5 classification modeling techniques (Logistic Regression (LR), Random Forest (RF), k-Nearest Neighbor (kNN), Support Vector Machine (SVM) and Gradient boosted trees (GBT)) x 4 sampling methods (Up, Down, Smote, and No-sampling). The GBT-down sampled model showed the best classification performance according to Area under the Curve (0.804) and Area under the Precision and Recall Curve (0.406) statistics. Comparing relative variable importance val-ues for the best three classification models (GBT, RF, and LR), both running driving time and violation count were found to be the most influential variables, followed by the sum of rapid acceleration and deceleration frequencies, accumulated amount of fine, trip frequency per thousand kilometer driving, and the percentage of trips driving a compact car. These re-sults agree with the results of previous naturalistic studies: driver behavior-related variables are highly related to collision likelihood, although running driving time in our dataset was likely dictated by businesses. This dataset provided us with a unique opportunity to take an in-depth look at the relationship between collisions and business, environment, and driving behavior-related variables by using naturalistic data from newly-invented LoRa IoT-FMS devices. To the best of our knowledge, this study was the first naturalistic study connecting both driving data and various types of traffic violations (e.g., overspeeding, lane, sign, park-ing, toll fees, and fine amount). Interestingly, non-driving-related violation types such as parking or toll-fee violation counts were also strongly correlated with collision involve-ment; suggesting that collision-involvement is likely not just a skill issue but also an attitude issue regarding the law. In terms of industrial applications, this study suggests multiple oppor-tunities. Through a better understanding of the influential vari-ables related to collision-involvement (e.g., accumulated vio-lations), fleet operators can build policies to enhance their fleet safety, reducing collision rates and the associated costs. Further, in the long-term, this study can provide a framework for developing a Usage-Based Rent-a-car (UBR) service for car rental field, similar to Usage-Based Insurance (UBI), which can reduce drivers’ rental fees based on their driving behaviors.
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Zhou, Zhouzhou, Anmin Gong, Qian Qian, Lei Su, Lei Zhao, and Yunfa Fu. "A novel strategy for driving car brain–computer interfaces: Discrimination of EEG-based visual-motor imagery." Translational Neuroscience 12, no. 1 (January 1, 2021): 482–93. http://dx.doi.org/10.1515/tnsci-2020-0199.
Повний текст джерелаАнотація:
Abstract A brain–computer interface (BCI) based on kinesthetic motor imagery has a potential of becoming a groundbreaking technology in a clinical setting. However, few studies focus on a visual-motor imagery (VMI) paradigm driving BCI. The VMI-BCI feature extraction methods are yet to be explored in depth. In this study, a novel VMI-BCI paradigm is proposed to execute four VMI tasks: imagining a car moving forward, reversing, turning left, and turning right. These mental strategies can naturally control a car or robot to move forward, backward, left, and right. Electroencephalogram (EEG) data from 25 subjects were collected. After the raw EEG signal baseline was corrected, the alpha band was extracted using bandpass filtering. The artifacts were removed by independent component analysis. Then, the EEG average instantaneous energy induced by VMI (VMI-EEG) was calculated using the Hilbert–Huang transform (HHT). The autoregressive model was extracted to construct a 12-dimensional feature vector to a support vector machine suitable for small sample classification. This was classified into two-class tasks: visual imagination of driving the car forward versus reversing, driving forward versus turning left, driving forward versus turning right, reversing versus turning left, reversing versus turning right, and turning left versus turning right. The results showed that the average classification accuracy of these two-class tasks was 62.68 ± 5.08%, and the highest classification accuracy was 73.66 ± 6.80%. The study showed that EEG features of O1 and O2 electrodes in the occipital region extracted by HHT were separable for these VMI tasks.
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Zhao, Chen, Xia Zhao, Zhao Li, and Qiong Zhang. "XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway." Sustainability 14, no. 11 (June 2, 2022): 6829. http://dx.doi.org/10.3390/su14116829.
Повний текст джерелаАнотація:
This study is conducted on a real live highway to investigate the driver’s performance in estimating the speed and distance of vehicles behind the target lane during lane changes. Data on the participants’ estimated and actual data on the rear car were collected in the experiment. Ridge regression is used to analyze the effects of both the driver’s features, as well as the relative and absolute motion characteristics between the target vehicle and the subject vehicle, on the driver’s estimation outcomes. Finally, a mixed algorithm of extreme gradient boosting (XGBoost) and deep neural network (DNN) was proposed in this paper for establishing driver’s speed estimation and distance prediction models. Compared with other machine learning models, the XGBoost-DNN prediction model performs more accurate prediction performance in both classification scenarios. It is worth mentioning that the XGBoost-DNN mixed model exhibits a prediction accuracy approximately two percentage points higher than that of the XGBoost model. In the two-classification scenarios, the accuracy estimations of XGBoost-DNN speed and distance prediction models are 91.03% and 92.46%, respectively. In the three-classification scenarios, the accuracy estimations of XGBoost-DNN speed and distance prediction models are 87.18% and 87.59%, respectively. This study can provide a theoretical basis for the development of warning rules for lane-change warning systems as well as insights for understanding lane-change decision failures.
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Bi, Jun, Ru Zhi, Dong-Fan Xie, Xiao-Mei Zhao, and Jun Zhang. "Capturing the Characteristics of Car-Sharing Users: Data-Driven Analysis and Prediction Based on Classification." Journal of Advanced Transportation 2020 (March 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/4680959.
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This work explores the characteristics of the usage behaviour of station-based car-sharing users based on the actual operation data from a car-sharing company in Gansu, China. We analyse the characteristics of the users’ demands, such as usage frequency and order quantity, for a day with 24 1 h time intervals. Results show that most car-sharing users are young and middle-aged men with a low reuse rate. The distribution of users’ usage during weekdays shows noticeable morning and evening peaks. We define two attributes, namely, the latent ratio and persistence ratio, as classification indicators to understand the user diversity and heterogeneity thoroughly. We apply the k-means clustering algorithm to group the users into four categories, namely, lost, early loyal, late loyal, and motivated users. The usage characteristics of lost users, including maximum rental time and travel distance, minimum percentage of same pickup and return station, and low percentage of locals, have noticeable differences from those of the other users. Late loyal users have lower rental time and travel distance than those of the other users. This manifestation is in line with the short-term lease of shared cars to complete short- and medium-distance travel design concepts. We also propose a model that predicts the driver cluster based on the decision tree. Numerical tests indicate that the accuracy is 91.61% when the user category is predicted four months in advance using the observation-to-judgment period ratio of 3 : 1. The results in this study can support enterprises in user management.
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Alghamdi, Ahmed S., Ammar Saeed, Muhammad Kamran, Khalid T. Mursi, and Wafa Sulaiman Almukadi. "Vehicle Classification Using Deep Feature Fusion and Genetic Algorithms." Electronics 12, no. 2 (January 5, 2023): 280. http://dx.doi.org/10.3390/electronics12020280.
Повний текст джерелаАнотація:
Vehicle classification is a challenging task in the area of image processing. It involves the classification of various vehicles based on their color, model, and make. A distinctive variety of vehicles belonging to various model categories have been developed in the automobile industry, which has made it necessary to establish a compact system that can classify vehicles within a complex model group. A well-established vehicle classification system has applications in security, vehicle monitoring in traffic cameras, route analysis in autonomous vehicles, and traffic control systems. In this paper, a hybrid model based on the integration of a pre-trained Convolutional Neural Network (CNN) and an evolutionary feature selection model is proposed for vehicle classification. The proposed model performs classification of eight different vehicle categories including sports cars, luxury cars and hybrid power-house SUVs. The used in this work is derived from Stanford car dataset that contains almost 196 cars and vehicle classes. After performing appropriate data preparation and preprocessing steps, feature learning and extraction is carried out using pre-trained VGG16 first that learns and extracts deep features from the set of input images. These features are then taken out of the last fully connected layer of VGG16, and feature optimization phase is carried out using evolution-based nature-inspired optimization model Genetic Algorithm (GA). The classification is performed using numerous SVM kernels where Cubic SVM achieves an accuracy of 99.7% and outperforms other kernels as well as excels in terns of performance as compared to the existing works.
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Beliakov, Sergei. "Substantiation of organizational and technological solutions for construction within the “car-free city” concept." MATEC Web of Conferences 193 (2018): 01005. http://dx.doi.org/10.1051/matecconf/201819301005.
Повний текст джерелаАнотація:
In the article, the author developed the classification of the main models of implementation of the “car-free city” concept depending on organizational and technological solutions in the construction and reconstruction of the urban environment. The author determined that the problem of selecting the optimal model should be decided on the basis of a comprehensive analysis of various factors that determine the specific features and problems of a specific urban situation (social, technical, organizational, environmental, cultural, economic, climatic, etc). The developed solution should meet the criteria of ensuring the comfort of the urban environment and investment attractiveness. The author developed the basic algorithm of choosing the optimal organizational and technological solutions for the development of urban development within the “car-free city” concept, the use of which will contribute to maximizing socio-economic, environmental and other effects while ensuring the investment attractiveness and competitiveness of construction project decisions.
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Cho, Chiwoo, Wooyeol Choi, and Taewoon Kim. "Leveraging Uncertainties in Softmax Decision-Making Models for Low-Power IoT Devices." Sensors 20, no. 16 (August 16, 2020): 4603. http://dx.doi.org/10.3390/s20164603.
Повний текст джерелаАнотація:
Internet of Things (IoT) devices bring us rich sensor data, such as images capturing the environment. One prominent approach to understanding and utilizing such data is image classification which can be effectively solved by deep learning (DL). Combined with cross-entropy loss, softmax has been widely used for classification problems, despite its limitations. Many efforts have been made to enhance the performance of softmax decision-making models. However, they require complex computations and/or re-training the model, which is computationally prohibited on low-power IoT devices. In this paper, we propose a light-weight framework to enhance the performance of softmax decision-making models for DL. The proposed framework operates with a pre-trained DL model using softmax, without requiring any modification to the model. First, it computes the level of uncertainty as to the model’s prediction, with which misclassified samples are detected. Then, it makes a probabilistic control decision to enhance the decision performance of the given model. We validated the proposed framework by conducting an experiment for IoT car control. The proposed model successfully reduced the control decision errors by up to 96.77% compared to the given DL model, and that suggests the feasibility of building DL-based IoT applications with high accuracy and low complexity.
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García-Domínguez, Antonio, Carlos E. Galvan-Tejada, Laura A. Zanella-Calzada, Hamurabi Gamboa, Jorge I. Galván-Tejada, José María Celaya Padilla, Huizilopoztli Luna-García, Jose G. Arceo-Olague, and Rafael Magallanes-Quintanar. "Deep artificial neural network based on environmental sound data for the generation of a children activity classification model." PeerJ Computer Science 6 (November 9, 2020): e308. http://dx.doi.org/10.7717/peerj-cs.308.
Повний текст джерелаАнотація:
Children activity recognition (CAR) is a subject for which numerous works have been developed in recent years, most of them focused on monitoring and safety. Commonly, these works use as data source different types of sensors that can interfere with the natural behavior of children, since these sensors are embedded in their clothes. This article proposes the use of environmental sound data for the creation of a children activity classification model, through the development of a deep artificial neural network (ANN). Initially, the ANN architecture is proposed, specifying its parameters and defining the necessary values for the creation of the classification model. The ANN is trained and tested in two ways: using a 70–30 approach (70% of the data for training and 30% for testing) and with a k-fold cross-validation approach. According to the results obtained in the two validation processes (70–30 splitting and k-fold cross validation), the ANN with the proposed architecture achieves an accuracy of 94.51% and 94.19%, respectively, which allows to conclude that the developed model using the ANN and its proposed architecture achieves significant accuracy in the children activity classification by analyzing environmental sound.
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Nayak, Suraj Kumar, Maciej Jarzębski, Anna Gramza-Michałowska, and Kunal Pal. "Automated Detection of Cannabis-Induced Alteration in Cardiac Autonomic Regulation of the Indian Paddy-Field Workers Using Empirical Mode Decomposition, Discrete Wavelet Transform and Wavelet Packet Decomposition Techniques with HRV Signals." Applied Sciences 12, no. 20 (October 14, 2022): 10371. http://dx.doi.org/10.3390/app122010371.
Повний текст джерелаАнотація:
Early detection of the dysfunction of the cardiac autonomic regulation (CAR) may help in reducing cannabis-related cardiovascular morbidities. The current study examined the occurrence of changes in the CAR activity that is associated with the consumption of bhang, a cannabis-based product. For this purpose, the heart rate variability (HRV) signals of 200 Indian male volunteers, who were categorized into cannabis consumers and non-consumers, were decomposed by Empirical Mode Decomposition (EMD), Discrete Wavelet transform (DWT), and Wavelet Packet Decomposition (WPD) at different levels. The entropy-based parameters were computed from all the decomposed signals. The statistical significance of the parameters was examined using the Mann–Whitney test and t-test. The results revealed a significant variation in the HRV signals among the two groups. Herein, we proposed the development of machine learning (ML) models for the automatic classification of cannabis consumers and non-consumers. The selection of suitable input parameters for the ML models was performed by employing weight-based parameter ranking and dimension reduction methods. The performance indices of the ML models were compared. The results recommended the Naïve Bayes (NB) model developed from WPD processing (level 8, db02 mother wavelet) of the HRV signals as the most suitable ML model for automatic identification of cannabis users.
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Zhang, Guangchao, and Junrong Liu. "Intelligent vehicle modeling design based on image processing." International Journal of Advanced Robotic Systems 18, no. 1 (January 1, 2021): 172988142199334. http://dx.doi.org/10.1177/1729881421993347.
Повний текст джерелаАнотація:
With the urgent demand of consumers for diversified automobile modeling, simple, efficient, and intelligent automobile modeling analysis and modeling method is an urgent problem to be solved in current automobile modeling design. The purpose of this article is to analyze the modeling preference and trend of the current automobile market in time, which can assist the modeling design of new models of automobile main engine factories and strengthen their branding family. Intelligent rapid modeling shortens the current modeling design cycle, so that the product rapid iteration is to occupy an active position in the automotive market. In this article, aiming at the family analysis of automobile front face, the image database of automobile front face modeling analysis was created. The database included two data sets of vehicle signs and no vehicle signs, and the image data of vehicle front face modeling of most models of 22 domestic mainstream brands were collected. Then, this article adopts the image classification processing method in computer vision to conduct car brand classification training on the database. Based on ResNet-8 and other model architectures, it trains and classifies the intelligent vehicle brand classification database with and without vehicle label. Finally, based on the shape coefficient, a 3D wireframe model and a curved surface model are obtained. The experimental results show that the 3D curve model can be obtained based on a single image from any angle, which greatly shortens the modeling period by 92%.
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Sun, Bohua, Yang Zhai, Yaxin Li, Weiwen Deng, and Shuai Zhao. "Driving Capability, a Unified Driver Model for ADAS." Journal of Physics: Conference Series 2185, no. 1 (January 1, 2022): 012037. http://dx.doi.org/10.1088/1742-6596/2185/1/012037.
Повний текст джерелаАнотація:
Abstract To allocate driving privilege in a reasonable way in shared control for intelligent vehicle, the study on driving capability, the unified driver model for ADAS in the longitudinal and lateral scenarios was proposed, which can improve the safety and comfort for intelligent vehicles as well. Driving capability is defined and analyzed and car-following stimulate in longitudinal scenario and moving double lane change stimulate in lateral scenario were designed. Data collection was conducted in Driver-In-the-Loop Intelligent Simulation Platform (DILISP). Driving capability identification model was established basing on Hammerstein process and Principal Component Analysis (PCA) was used to decouple and reduce the dimension for the key parameters in Hammerstein identification model. The classification is done basing on the particle clustering algorithm and the evaluation equation for driving capability was calculated by Multiple Linear Regression (MLR). Results show that the proposed evaluation method for driving capability in the longitudinal and lateral scenarios can achieve accurate and reliable evaluation results.