Browse Topic: Big data

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Implementation of new educational technologies for training personnel for the transport industry2026-26-0241To be published on 01/16/2026
Today's challenges affect the system of training highly qualified personnel for the transport industry, which is actively transforming through the introduction of innovative educational technologies. Key areas have become digitalization of the educational process, integration of virtual simulators, augmented reality technologies (AR/VR) and platform solutions for managing educational programs. Today, transport education in Russia is focused on advanced training of specialists capable of working with autonomous transport and neural network solutions. Development prospects are associated with further integration of adaptive educational trajectories and global network platforms to ensure the competitiveness of graduates in the digital economy. Taking into account the specifics of the modern Russian transport industry, using an analytical method, the article examines solutions to some priority problems, namely: • Methods of modernizing educational programs through designing «professions of
SHISHANOV, Sergei, Kurmaev, Rinat, revenok, Svetlana
Transforming Battery Management: The Role of Artificial Intelligence and Machine Learning in Digital Twin Technologies2026-26-0382To be published on 01/16/2026
The traditional Battery Management System (BMS) faces certain limitations in fully utilizing battery capacity and performance during the long cycle life operation of Electric Vehicles (EVs). These constraints include limited real-time data collection, low processing speed, lack of predictive maintenance, and very limited accuracy in predicting health and degradation chemistry. A Battery Digital Twin (BDT) can effectively address these limitations of the BMS. Battery Digital Twins (BDT) can be viewed as a cyber-physical system comprising four key elements: virtual representation, bidirectional connection, simulation, and connection across the life cycle phases of an EV battery. The performance of a Li-ion battery largely depends on the cathode chemistry, component design, and operating conditions. The battery should be manufactured in a manner (such as cylindrical or prismatic cell) that prevents explosion, leakage, and gas generation inside the battery. To enhance the performance and
Chaturvedi, Vikash, M, Venkatesan, Lanke, Siddhi, Subramaniam, Anand, Karle, Manish, Pandit, Rugved, Gupta, Drishti, Karle, Ujjwala Shailesh
Leveraging Telematics Big Data Analytics for Real-Time Monitoring of Catalytic Converter Failures and Root Cause Identification for Emission Compliance2026-26-0591To be published on 01/16/2026
The automotive industry is continuously evolving at high pace to meet rising customer expectations, reliability, reduced maintenance, and most relevant, compliance with stringent emission norms. Traditionally, the analysis of vehicle emissions relies heavily on periodic inspections and manual checks. These conventional methods are often time-consuming, prone to human error, and lack the ability to provide real-time insights. Also, identifying Catalytic converter failures due to non-manufacturing issues require meticulous physical inspections and historical data reviews, which are not always accurate or timely. Telematics or Connected cars technology being one of the major technological innovations in recent times revolutionizes these processes by enabling real-time data exchange between vehicles and external systems. The current study presents an innovative approach to utilizing telematics data for real-time monitoring of vehicle emissions and pinpointing Catalytic converter failures
DEV, Triyambak, Prasad, Kakaraparti Agam, Kalkur, Varun, Modak, Saikat, AGARWAL, Shashank, Chandra, Animesh, Paul, Varsha, Garg, Amit, Sundararaman, Venkataraman, Bose, Sushant
Extreme Learning Machines (ELM) for Fast Instantaneous Prediction of Emission Level of an Automobile2026-26-0226To be published on 01/16/2026
Addressing climate issues is a key aspect of good global governance today. A key aspect of managing the threats caused to the environment around is to ensure a sustainable transportation system so that humans exist in peace with nature. According to sources, in 2020 alone, cars accounted for approximately 23% of global CO2 emissions. In addition, they also emit nitrogen oxides and other pollutants thus damaging the ecosystem. In line with this objective, there are emission level testing strategies in place in each country. However, we can do better for a sustainable future. On one hand, the huge volume of vehicles around the world makes it an excellent choice and source for a vast emission level dataset comprising of features such as vehicle type, fuel type, driving conditions, road type etc.. as well as the target variable representing the emission band of the vehicle. In addition to the big data available as mentioned above, there is a marked progress in the machine learning
Sridhar, Sriram, Aswani, Shelendra
Big Data Trajectory Analysis and Traffic Flow Framework Construction for Ship Manipulation Decision-Making2025-99-0134To be published on 11/11/2025
We present a novel processing approach to extract a ship traffic flow framework in order to cope with problems such as large volume, high noise levels and complexity spatio-temporal nature of AIS data. We preprocess AIS data using covariance matrix-based abnormal data filtering, develop improved Douglas-Peucker (DP) algorithm for multi-granularity trajectory compression, identify navigation hotspots and intersections using density-based spatial clustering and visualize chart overlays using Mercator projection. In experiments with AIS data from the Laotieshan waters in the Bohai Bay, we achieve compression rate up to 97% while maintaining a key trajectory feature retention error less than 0.15 nautical miles. We identify critical areas such as waterway intersections and generate traffic flow heatmap for maritime management, route planning, etc.
Kong, Xiangyu, Shao, Guoyu
2025 International Conference on Big Data, Internet of Things and Intelligent Transportation (BDIT)2025-99-0100To be published on 11/11/2025
TOC
Tobolski, Sue
Establishing a Sustainability Certification System for Aviation Fuel in China01-18-03-001211/07/2025
Amid escalating global warming challenges, the aviation industry must adopt low-carbon and green practices. China, aiming to meet its dual carbon goals, urgently requires enhanced research and development in sustainable aviation fuels (SAF), including their sustainability certification. However, China’s regulatory framework and limited research foundation in biofuels exacerbate this endeavor. This article summarizes the development status of SAF sustainability certification internationally and within China, encompassing the indicator framework, full life cycle greenhouse gas (GHG) calculation methodologies, and emission reduction thresholds. It also highlights issues encountered in the application of current international sustainability certification systems in China, such as high certification costs and inadequate data security. Advancement in domestic sustainability certification in China faces obstacles related to the incomplete foundational database, despite possessing life cycle
Zhang, Shuping, He, Yin, Jia, Quanxing, Jia, Qin, Tao, Zan, Miao, Jiahe, Shi, Yao, Zhang, Xiangping, Wang, Siyu
Digital Solution Using Immersive Technology for Field Failure2025-28-030611/06/2025
Warranty claims function as primary source of characterizing field failures across industries, wherein appropriate classification of these claims is critical for further analysis. The classification of warranty claims is a highly laborious effort, involving significant man-hours of warranty analysts. This can be highly optimized and made efficient using direct interpretation of the claim data on 3D model using unity game engine. Additionally, the color perception technique using immersive technology (AR/VR) can help to identify the vital few & drive prioritization of the field failures leading to faster problem resolution. The capabilities of UI/UX & advanced visualization are integrated to develop novel methods to classify the warranty claims & interpret it on a 3D model using immersive technology which is novel and one of its kind in industry. Unique characteristics of this tool is it focuses on the warranty claim classification by claim cost & count of claims and presents the heat
Nankery, Vivek, savadatti, Sandeep, Shete, Atul, Apkare, Sanket, Ganapathi, Poongundran
Advanced Compliance Risk Assessment Using Big Data from Fleet Intelligence Data2025-28-034311/06/2025
In-Use emission compliance regulations globally mandate that machines meet emission standards in the field, beyond dyno certification. For engine manufacturers, understanding emission compliance risks early is crucial for technology selection, calibration strategies, and validation routines. This study focuses on developing analytical and statistical methods for emission compliance risk assessment using Fleet Intelligence Data, which includes high-frequency telematics data from over 500K machines, reporting more than 1000 measures at 1Hz frequency. Traditional analytical methods are inadequate for handling such big data, necessitating advanced methods. We developed data pipelines to query measures from the Enterprise Data Lake (A Structured Data storage system), address big data challenges, and ensure data quality. Regulatory requirements were translated into software logic and applied to pre-processed data for emission compliance assessment. The resulting reports provide actionable
Arya, Satya Prakash, Shekarappa, Kiran
Key Influential Factors for Smart Logistics in Guangzhou Based on DEMATEL -AHP2025-99-002310/17/2025
Use Decision Making Trial and Evaluation Laborator (DEMATEL) and Analytic Hierarchy Process (AHP) to jointly analysis and determine the key factors of Guangzhou intelligent logistics. Through the questionnaire survey of 92 logistics enterprises in Guangzhou, it is concluded that Information infrastructure, big data, Internet of Things, artificial intelligence, Logistics dynamic updates, and Smart warehousing have a great impact on intelligent logistics. Combining practical engineering with theory to make the implementation of Guangzhou’s smart logistics project more scientific, It is characterized by a higher degree of scientificity. Moreover, it is of great warning value, which can alert relevant parties to potential issues. Meanwhile, it provides essential guidance for the implementation of the smart city project in Guangzhou, facilitating a more efficient and well - directed execution process. This study is limited to logistics business respondents in Guangzhou and may limit the
Zhang, Shuangshuang, Chen, Ning, Khaw, Khai Wah, Liu, Chenxi, Jin, Lili
Research on the Degree of Airport Intelligence Level Based on Three-Scaled Hierarchical Analysis Method2025-99-003710/17/2025
Smart airport is a key driver for the future development of civil aviation and a cornerstone of China’s ongoing “Four Airport” construction initiative. It is important to improve technology in many areas. This includes airport building, daily work, management, and making decisions. As air travel changes, using new tools like artificial intelligence, big data, and the Internet of Things (IoT) is very important. These tools help make airports more efficient, safe, and better for the environment. Because of this, building smart airports is not just a big goal but also a new way to deal with the challenges in today’s air travel systems. A key part in building smart airports is making a full evaluation system to check how well the projects are working. When a strong index system is made for smart airports, people involved can see clearly what is working well and what is not. So, chose using a three-scale hierarchical analysis method gives a clear and step-by-step way to look at different
Li, Shi-ling, Fu, Lu
Spatial-temporal Information Organization Model with All Elements in Whole Domain for Shuozhou-Huanghua Railway2025-99-007910/17/2025
In view of the complexity of railway engineering structure, the systematicness of professional collaboration and the high reliability of operation safety, this paper studied the spatial-temporal information data organization model with all elements in whole domain for Shuozhou-Huanghua Railway from the aspect of Shuozhou-Huanghua Railway spatial-temporal information security. Taking the unique spatial-temporal benchmark as the main line, the paper associated different spatial-temporal information to form an efficient organization model of Shuozhou-Huanghua Railway spatial-temporal information with all elements in the whole domain, so as to implement the effective organization of massive spatial-temporal information in various specialties and fields of Shuozhou-Huanghua Railway; By using GIS (Geographic Information System) visualization technology, spatial analysis technology and big data real-time dynamic rendering technology, it was realized the real-time dynamic visualization display
Liu, Kun, Yu, Hongsheng, Zhu, Panfeng, Liu, Wenbin, Wang, Yaoyao
Battery Datasets for Neural Network-Based State-of-Health Estimators: A Review and Implementative Workflow2025-24-013909/07/2025
Management of battery systems for electric vehicles has great importance to ensure safe and efficient operation. State-of-Charge and State-of-Health (SoH) are fundamental parameters to be taken under control even though they cannot be directly measured during vehicle operation. Some control approaches have gained increasing interest thanks to advances in sensor availability, edge computing and the development of big data. In particular, SoH estimation through machine learning (ML) and neural networks (NNs) has been thoroughly investigated due to their great flexibility and potential in mapping non-linear relations within data. The numerous studies available in the literature either employ different extracted features from data to train NNs, or directly use measurement signals as input. Additionally, many studies available in the literature are based on a limited number of publicly available datasets, which mainly encompass cylindrical battery cells with small capacity. Starting from
Chianese, Giovanni, Capasso, Clemente, Veneri, Ottorino
Development of a Test Course Selection Method for Natural Wind Data Collection on US Public Roads2025-01-878004/01/2025
The automotive aerodynamic development relies on wind tunnel testing and Computational Fluid Dynamics (CFD), where the former provides reliable values to be used for fuel economy calculations, and the latter enables the investigation of flow features responsible for improvement/degradation of the average large-scale performances in terms of aerodynamic coefficients. The abovementioned procedure overlooks a crucial factor however: natural wind. The speed and the direction of natural wind encountered while driving alters the vehicle’s effective yaw angle. Such condition implies that the minimization of the drag coefficient at zero-yaw, commonly performed through wind tunnel and CFD simulations in an industrial context, may not yield real-world optimal shapes. While it is possible to reproduce natural wind-like conditions in a wind tunnel using flaps, for example, the input signal to the flap system must be available beforehand, and such key element is the focus of the present research
Nucera, Fortunato, Onishi, Yasuyuki, Metka, Matt
Truck Overload Warning and Risk Assessment Method Based on In-Vehicle System Data2025-01-721002/21/2025
Overloading of trucks will not only damage road infrastructure, lead to exhaust pollution, and even cause serious traffic accidents, resulting in huge losses of life and property. However, most of the methods to evaluate truck overloading are limited by environmental factors, so it is impossible to monitor truck overloading in real time. In order to solve this problem, a truck overload detection method based on real-time vehicle diagnosis big data is proposed in this paper. The method comprehensively considers multiple factors affecting the actual power of trucks through mathematical modeling. It based on the effects of overload on fuel combustion efficiency, harmful gas emission, exhaust temperature, and vehicle power loss, The truck overload evaluation model is constructed to judge whether the truck is overloaded or not in real time. Based on the truck overload assessment and truck accident risk factor extraction , a real-time operation risk assessment model based on fault tree
Chen, Yuguang, Lin, Honghao, Wang, Yanan
Synthetic On Board Diagnostics Data Generation and Evaluation for Vehicle Diagnostic TestingSAE-PP-0040508/21/2024
The generation of data plays a vital role in Machine Learning (ML) techniques by providing the foundation for training and improvement of forecast models. As one application area for these models, in-vehicle systems, like vehicle diagnostics, have the potential to enhance the reliability and durability of vehicles by utilizing ML models in the testing phases. However, acquiring a high volume of quality On-Board Diagnostics (OBD) data is time-consuming and poses challenges like the risk of exposing sensitive information. To address this issue, synthetic data generation offers a promising alternative that is already in use in other domains. Thereby, synthetic data allows the exploitation of knowledge found in original data, ensuring the privacy of sensitive data, with less time costs of data acquisition. For this purpose, the research presented in this contribution investigates the use of statistical and ML-based synthetic OBD data generation methods. The models are evaluated with the
Vucinic, Veljko, Hantschel, Frank, Kotschenreuther, Thomas
Coordinated Charging and Dispatching for Large-Scale Electric Taxi Fleets Based on Bi-Level Spatiotemporal Optimization2024-01-288004/09/2024
The operation management of electric Taxi fleets requires cooperative optimization of Charging and Dispatching. The challenge is to make real-time decisions about which is the optimal charging station or passenger for each vehicle in the fleet. With the rapid advancement of Vehicle Internet of Things (VIOT) technologies, the aforementioned challenge can be readily addressed by leveraging big data analytics and machine learning algorithms, thereby contributing to smarter transportation systems. This study focuses on optimizing real-time decision-making for charging and dispatching in large-scale electric taxi fleets to improve their long-term benefits. To achieve this goal, a spatiotemporal decision framework using Bi-level optimization is proposed. Initially, a deep reinforcement learning-based model is built to estimate the value of charging and order dispatching under uncertainty. The model considers the long-term costs and benefits of different tasks and guides whether electric
Lyu, Yelin, Wang, Ning, Tian, Hangqi
Development of Classification of Customer Complaints Using Deep Learning2024-01-278904/09/2024
In recent years, the automotive industry has been making efforts to develop vehicles that satisfy customers’ emotions rather than malfunctions by improving the durability of vehicles. The durability and reliability of vehicles sold in the U.S. can be determined through the VDS (Vehicle Dependability Study) published by JD Power. The VDS is index which is the number of complaints per 100 units released by J.D. POWER in every year. It investigates customers who have used it for 3 years after purchasing a new car and consists of 177 specific problems grouped into 8 categories such as PT, ACEN, FCD, Exterior. The VDS-4 has been strengthened since the introduction of the new evaluation system VDS-5 in 2015. In order to improve the VDS index, it is important to gather various customer complaints such as internet data, warranty data, Enprecis data and clarify the problem and cause. Enprecis data is survey of customer complaints by on-line in terms of VDS. In the case of warranty and Enpreics
You, Hanmin
AI-based EV Range Prediction with Personalization in the Vast Vehicle Data2024-01-286804/09/2024
It is an important factor in electric vehicles to show customers how much they can drive with the energy of the remaining battery. If the remaining mileage is not accurate, electric vehicle drivers will have no choice but have to feel anxious about the mileage. Additionally, the potential customers have range anxiety when they consider Electric Vehicles. If the remaining mileage to drive is wrong, drivers may not be able to get to the charging station and may not be able to drive because the battery runs out. It is important to show the remaining available driving range exactly for drivers. The previous study proposed an advanced model by predicting the remaining mileage based on actual driving data and based on reflecting the pattern of customers who drive regularly. The Bayesian linear regression model was right model in previous study. In addition, in order to improve performance, the driver's regular driving pattern is recognized in advance before driving and it is reflected in the
Joo, Kihyung, kim, Lina
A Study on a Prognostics and Health Management (PHM) Based on Fracture Mechanics Using Deep Learning2024-01-224804/09/2024
This paper presents deep learning-based prognostics and health management (PHM) for predicting fractures of an electric propulsion (eP) drivetrain system using real-time CAN signals. The deep learning algorithm, based on autoencoders, resamples time-series signals and converts them into 2D images using recurrence plots (RP). Subsequently, through unsupervised learning of DeepSVDD, it detects anomalies in the converted 2D images and predicts the failure of the system in real-time. Also, reliability analysis based on fracture mechanics was performed using the detected signals and big data. In particular, the severity of the eP drivetrain system is proportional to the maximum shear stress (τmax) in terms of linear elastic fracture mechanics (LEFM) and can be calculated by summarizing the relationship between cracks (a) and the stress intensity factor (KIII). During this process, the system status can be checked by comparing the stress intensity factor and fracture toughness (KIIIc), and
Moon, Byungwoo, Lee, SangWon, Nam, DongJin, Kim, Jeonghwan, Bae, JaeWoong, Shin, JeongMin
A Percipient Analysis of Jaguar I-PACE Electric Vehicle Energy Consumption Using Big Data Analytics2024-01-287904/09/2024
Vehicle efficiency and range, along with the DC charging speed, are deemed as the most important criteria for an electric vehicle currently. The electric vehicle energy consumption is impacted by the change in temperature along with the driving style and average speed of a customer, all other factors being constant. Hence understanding the patterns and impact of different aspects of an EV range & charging speed is crucial in delivering an electric vehicle with robust efficiency across all weather conditions. In this paper we have analysed vehicle parameters of global Jaguar I-PACE customer data. We present and analyse the collated big data of around 50,000+ unique vehicles with a data aggregate of well over 482 million km. In moderate ambient conditions the analysis indicated a good correlation with 50th to 75th percentile drivers’ energy consumption to the EPA label figure. The EPA hot and cold ambient tests also compare well but the correlation is sensitive to long and short trip
Dutta, Nilabza, Evans, David, sapte, Atharva
Big Data Analytics (Telematics) for Real-Time Testing Improvements and Integration Back to the Development of Future Models/EVs2024-26-035201/16/2024
The Auto industry has relied upon traditional testing methodologies for product development and Quality testing since its inception. As technology changed, it brought a shift in customer demand for better vehicles with the highest quality standards. With the advent of EVs, OEMs are looking to reduce the going-to-market time for their products to win the EV race. Traditional testing methodologies have relied upon data received from various stakeholders and based on the same tests are planned. The data used is highly subjective and lacks variety. OEMs across the world are betting big on telematics solutions by pushing more and more vehicles with telematics devices as standard fitment. The data from such vehicles which gets generated in high levels of volume, variety and velocity can aid in the new age of vehicle testing. This live data cannot be simply simulated in test environments. The device generates hundreds of signals, frequently in a fraction of seconds. Multiple such signals can
Sahoo, Priyabrata, Singh, Saurabh, Prasad, Kakaraparti Agam
Genesis of the “Automotive Homologation 4.0” Framework for India2024-26-036001/16/2024
The term Industry 4.0 is well known in contemporary automotive landscape. It encompasses a smart integrated framework of IIoT (Internet of Things) and industrial automation with machine learning, artificial intelligence and big data analytics to arrive at optimal solutions to running the processes in a streamlined, efficient and effective manner. Industry 4.0 has assumed critical significance in the contemporary era of people working from remote locations to operate processes in order to build products, thereby ensuring business continuity. Consequently, it follows that if industry 4.0 is applied to automotive homologation activity, it will lead to a standardized evaluation, consistent fidelity of testing, accurate judgement of the product under test with regards to its certification, and most importantly, timed delivery to release in the market. The author hereby elucidates a unified Industry 4.0 Framework for Automotive homologation in India which is the need of the hour. This
S Thipse, Yogesh
Big Data and Telemetry Data Methodology for Usage Failure Mode Detection2023-36-001301/08/2024
Using current technologies, a single “entry level” vehicle has millions of electrical signals sent through dozens of modules, sensors and actuators, and those signals can be sent over the air, creating a telemetry data that can be used for several ends. One electrical device is set up to have diagnosis, in order to make maintenance feasible and support repair, plus giving improvement directions for specialists on new developments and specifications, but in several cases the diagnosis can only determine the mechanism of failure, but not the event that triggered that failure. Current evaluation method involves teardown, testing and knowledge from the involved specialized team, but this implies in recovering of failed parts, which in larger automakers with thousands of dealers/repair shops, reduces the sample for analyses when there is a systemic issue with one component. This specificity is usual in Propulsions systems, regarding electro-mechanical devices, and sensors, also in
Prazeres, Christopher, Hachyia, Afonso, Takahashi, Marcio
Implementation of Predictive Adaptive Cruise Control Strategy Based on ADAS Map2023-01-711112/31/2023
Heavy vehicles are major fuel consumers in road transportation, and the traditional way to reduce fuel consumption is to reduce weight, resistance, improve mechanical transmission efficiency, and improve engine thermal efficiency. However, European heavy-duty truck companies took the lead in realizing predictive cruise control (PCC) technology on the basis of cruise through intelligent network technology, based on ADAS maps, and achieved good fuel saving effects. In this paper, by studying the fuel consumption characteristics of trucks, designing the dynamic parameters of the load and whole vehicle, the predictive adaptive cruise control (PACC) technology is realized based on the predictive cruise strategy, and the statistics of fuel saving rate under different cruise ratio conditions are analyzed through the big data platform.
Qian, Guoping, Lu, Zhenghua, Tian, Juntao, Liu, Lianfang, Xi, Chong, Zhou, Xiaoying
Synthesis of Representative Driving Cycle for Heavy Duty Vehicle Based on Markov Chain and Big Data Considering Weight Variation2023-32-017709/29/2023
Synthesized driving cycles which can reflect the real world driving scenarios are essential for electrification and hybridization of powertrains of heavy duty logistics vehicles (HDLV). Current synthetic methods always neglected weight variation which is crucial for logistic vehicle driving scenarios. This paper proposed a method based on multi-dimensional Markov chains and big data to generate typical driving cycles with consideration of vehicle weight and slope. The validation of the synthesized driving cycle was based on a statistical analysis and the adequacy of the representative to real world driving data was demonstrated.
Liu, Zemin Eitan, Li, Yong, Tan, Guikun, Xu, Lubing, Shuai, Shijin
A Review of Intelligence-Based Vehicles Path Planning02-16-04-002207/28/2023
Numerous researchers are committed to finding solutions to the path planning problem of intelligence-based vehicles. How to select the appropriate algorithm for path planning has always been the topic of scholars. To analyze the advantages of existing path planning algorithms, the intelligence-based vehicle path planning algorithms are classified into conventional path planning methods, intelligent path planning methods, and reinforcement learning (RL) path planning methods. The currently popular RL path planning techniques are classified into two categories: model based and model free, which are more suitable for complex unknown environments. Model-based learning contains a policy iterative method and value iterative method. Model-free learning contains a time-difference algorithm, Q-learning algorithm, state-action-reward-state-action (SARSA) algorithm, and Monte Carlo (MC) algorithm. Then, the path planning method based on deep RL is introduced based on the shortcomings of RL in
Hao, Bing, Zhao, JianShuo, Wang, Qi
Probabilistic Risk Assessment of Accidental Damage to Civil Aircraft Composite Structures01-16-02-001604/26/2023
In view of the structural accidental events in the ongoing airworthiness stage of civil aircraft, it is necessary to conduct a risk assessment to ensure that the risk level is within an acceptable range. However, the existing models of risk assessment have not effectively dealt with the risk of accidental structural damage due to random failure. This article focuses on probabilistic risk assessment using the Transport Airplane Risk Assessment Methodology (TARAM) of accidental structural damage of civil aircraft. Based on the TARAM and probability reliability integral, a refined failure frequency probability calculation model is established to elaborate on composite structure failure frequency. A case study is analyzed for the outer wing plane of an aircraft having impact damage of composite materials. Finally, results of the risk assessment without correction and risk assessment with correction are presented for detailed visual inspection and general visual inspection.
Jia, Baohui, Fang, Jiachen, Lu, Xiang, Xiong, Yijie
Applying Machine Learning on automotive customer quality data to improve user experience and increase industry competitiveness2022-36-007002/10/2023
The automotive industry is going through one of its greatest restructuring, the migration from internal combustion engines to electric powered / internet connected vehicles. Adapting to a new consumer who is increasingly demanding and selective may be one of the greatest challenges of this generation, Original Equipment Manufacturers (OEM) have been struggling to keep offering a diversified variety of features to their customers while also maintaining its quality standards. The vehicles leave the factory with an embedded SIM Card and a telematics module, which is an electronic unit to enable communication between the car, data center. Connected vehicles generate tens of gigabytes of data per hour that have the potential to be transformed into valuable information for companies, especially regarding the behavior and desires of drivers. One of the techniques used to gather quality feedback from the customers is the NPS it consists of open questions focused on top-of-mind feedback. Here
Torres Fernandes Veiga, Daniel Thadeu, de Miranda Junior, Airton Wagner, Nascimento Silva, Luana, Sena Cavalcante, Mairon, dos Santos, Maria da Conceição
How the Tools of Quality 4.0 support the principles of TQC/TQM2022-36-001102/10/2023
The main purpose of this research is to identify how the established quality methodologies, known worldwide as TQC (Total Quality Control) and TQM (Total Quality Management) are supported by the tools of the Quality 4.0 concept that similarly received influence from the disruptive technologies of Industry 4.0 in the last decade. In order to crosscheck the relationship among TQC and TQM and how Quality 4.0 supports these quality systems a qualitative investigation method was adopted through a survey questionnaire applied to one of the most important worldwide automobile company, based also in Brazil, Toyota of Brazil. Based on a literature review and relationship of concepts and synergy among them it was possible analyse and find out conclusions of this research work. The main results were identified as TQC and TQM are very well established concepts of quality and Quality 4.0 concepts and tools have been implemented on a path according to the markets importance prioritization, so then
da Silva Bento, Nelson, Cavalcanti Bortoleto, William, Ibusuki, Ugo
Toward Unsupervised Test Scenario Extraction for Automated Driving Systems from Urban Naturalistic Road Traffic Data12-06-03-001702/02/2023
Scenario-based testing is a promising approach to solving the challenge of proving the safe behavior of vehicles equipped with automated driving systems (ADS). Since an infinite number of concrete scenarios can theoretically occur in real-world road traffic, the extraction of scenarios relevant in terms of the safety-related behavior of these systems is a key aspect for their successful verification and validation. Therefore, a method for extracting multimodal urban traffic scenarios from naturalistic road traffic data in an unsupervised manner, minimizing the amount of (potentially biased) prior expert knowledge, is proposed. Rather than an (elaborate) rule-based assignment by extracting concrete scenarios into predefined functional scenarios, the presented method deploys an unsupervised machine learning pipeline. The approach allows for exploring the unknown nature of the data and their interpretation as test scenarios that experts could not have anticipated. The method is evaluated
Weber, Nico, Thiem, Christoph, Konigorski, Ulrich
Engine Overheating Prediction with Machine Learning Using Gaussian Mixture Model (GMM)2022-28-000710/05/2022
The Advancement in Connected vehicles Technology in recent years has propelled the use of concepts like the Internet of Things (IoT) and big data in the automotive industry. The progressive electrification of the powertrain has led to the integration of various sensors in the vehicle. The data generated by these sensors are continuously streamed through a telematics device on the vehicle. Data analytics of this data can lead to a variety of applications. Predictive maintenance is one such area where machine learning algorithms are applied to relevant data to predict failure. Field vehicle malfunction or breakdown is costly for manufacturers’ aftermarket services. In the case of commercial vehicles, downtime is the biggest concern for the customer. The use of predictive maintenance techniques can prevent many critical failures by tending to the root cause in the early stages of failure. Engine overheating is one such problem that transpires in diesel engines. Overheating of an engine
Hiwase, Shrikant Deokrishna, JAGTAP, PRAMOD, Krishna, Dinesh
"Bosch Connected and Virtualized Development: Use of In-Vehicle Data to Optimize and Validate Braking Systems"1328109/13/2022
"Virtual development of electronic braking systems Value creation through in-vehicle data and cloud services Connected cars generate in-vehicle data from its electronic control units and sensors about how they are used, where they are, and how they feel. The amount of in-vehicle data will grow further exponentially, with progress in higher levels of driving automation, personalization and electrification. Therefore, exploiting the chances of generated in-vehicle data will become a key theme for the value creation and new business models in the automotive and mobility industry. While this will only happen in small steps, it will come quickly and will have a huge and lasting impact on both industries. Bosch connected braking systems Connected braking systems (CBS) is a new and connectivity-based approach from Bosch. It aims to enable its automotive customers and itself to transform the way of developing braking systems by exploiting in-vehicle data continuously and remotely from start-of
Nesbitt, Richard
Cloud Control Platform, from Information System to Digital TwinSAE-PP-0029509/07/2022
The move towards an automated driving system (ADS) is being driven by both potential benefits and challenges of the technology, such as telecommunication, vehicle industry, ITS and cybersecurity. Cooperative Vehicle-Infrastructure System (CVIS) is the core development of ADS while the establishment of Cloud Control Platform (CCP). The CCP integrated automation, 5G and AI technology to provide physical world, digital world and Internet of Vehicles world based on big data, algorithm and variety of traffic scenarios. The utilization of Digital Twin in CCP will cover the whole vehicle-to-cloud (V2C) and CVIS process, which will develop from fragmentation to integration and static to dynamic. It opens the door to real-time monitoring and synchronization of physical activities with the virtual reality. Digital twin, a novel digitalization paradigm of cyber physical systems, has been attracted interest over the past years. The real function of digital twin is to setup real-time link between
Wang, Jian
Biodata Compensation: An Ethical ResponsibilityTBMG-4656408/01/2022
The global big data market had a revenue of $162.6 billion in 2021.1 Data is becoming more valuable to companies than gold. However, this data has been used, historically, without contributors’ informed consent and without them seeing a penny from the discoveries the data led to. This article discusses how non-fungible tokens (NFTs) can provide a helpful tool for pharmaceutical companies to track contributed data and compensate contributors accordingly. NFTs are unique, untradable cryptographic assets that can be tracked on a blockchain. NFTs provide a unique traceable token that cannot be replicated, providing a perfect tool to store biodata. The term biodata refers to details regarding a patient’s history and behavioral patterns.
Review on Uncertainty Estimation in Deep-Learning-Based Environment Perception of Intelligent Vehicles2022-01-702606/28/2022
Deep neural network models have been widely used for environment perception of intelligent vehicles. However, due to models’ innate probabilistic property, the lack of transparency, and sensitivity to data, perception results have inevitable uncertainties. To compensate for the weakness of probabilistic models, many pieces of research have been proposed to analyze and quantify such uncertainties. For safety-critical intelligent vehicles, the uncertainty analysis of data and models for environment perception is especially important. Uncertainty estimation can be a way to quantify the risk of environment perception. In this regard, it is essential to deliver a comprehensive survey. This work presents a comprehensive overview of uncertainty estimation in deep neural networks for environment perception of intelligent vehicles. First, we provide a systematic and intuitive understanding of the classification and modeling of uncertainty and then summarize methods for uncertainty estimation in
Yin, Huilin, Chen, Zhaoru, Yan, Jun, Rigoll, Gerhard
Multivariate Analysis and Index Forecast of Influencing Factors of Shanghai Municipal Domestic Waste Generation2022-01-702206/28/2022
Traditional methods of municipal domestic waste analysis and prediction lack precision, while most data’s sample size is not suitable for many neural networks. In this paper, combining the advantage of deep learning methods with the results of association analysis, a waste production prediction method TLSTM is proposed based on long short-term memory(LSTM). It is found that the most influencing factors are population, public cost, household and GDP. Meanwhile, the garbage production in Shanghai will continue to decline in the future, indicating the policy of refuse classification is effective. The R-square index and MSE index of the model were 0.55 and 76571.73 respectively, surpassing other state-of-the-art models. In cooperation with School of Environmental Science and Engineering at Shanghai Jiao Tong University, the dataset comes from the average data of the Shanghai Household Waste Management Regulation from 1980 to 2020. This research method has a certain guiding significance to
Tu, Yun, Xiao, Zi Xin, Shen, Na
Impact of Quantum Computing in AerospaceEPR202201406/14/2022
As the complexity of systems expands with increasing emphasis for digital transformation, the aerospace industry is generating big data to meet customer requirements. The ability to that data to solve challenging problems is limited by many factors, including the capabilities of current classical computing systems. Impact of Quantum Computing in Aerospace discusses how quantum computing systems offer (possibly quadratic to exponentially) greater computational power over classical computers. The power of quantum computing is tremendous and has many potential impacts on the aerospace industry; however, there are also many unsettled topics surrounding the future of the technology. Click here to access the full SAE EDGETM Research Report portfolio.
Walthall, Rhonda, Dixit, Sunil
Aftermarket augments off-highway autonomous efforts22TOFHP06_0306/01/2022
Kontron and Intel experts explain how rugged, modular COM Express solutions reduce complexity and allow retrofit of autonomous systems on heavy mobile equipment. Continually transformed with more than a century's advances in capabilities, hydraulics and fuel efficiency, today's heavy mobile equipment must also become more intelligent and better connected. Technologies such as artificial intelligence (AI), deep learning, big data, GPS, 5G and computer vision are proving their mettle - empowering far more efficient ways of carrying out unique and demanding tasks via advanced telematics, advanced driver assistance systems (ADAS) or varying levels of autonomy. Heavy mobile equipment (HME) that can gather and apply data in real time operates and makes decisions in ways that humans cannot. This evolution toward automation promises not only leadership for manufacturers of more advanced systems, but also increased safety, economy, efficiency and ecological compatibility.
London, Jack, Thomas, Andrea
LAVA: A Methodology for Leveraging Aggregated Vehicle Analytics12-06-01-000304/18/2022
Driver Assist Technologies are complex systems for which it can be difficult to objectively estimate customer experience in a repeatable and quantitative manner. We must assess the designed feature operation at a massive scale to better understand the eventual customer impact and cost of a variety of engineering decisions. We will present the Leveraging Aggregated Vehicle Analytics (LAVA) methodology for improved understanding of the impact of these dynamic and subjective problems by utilizing connected vehicle (CV) data. Several examples of the LAVA methodology will be discussed and examined in detail. Using the LAVA methodology, minimal and anonymized data collected from CVs can be used to answer many engineering decision questions with high confidence in a controlled and scientific manner.
Lerner, Jeremy, Tayim, Dina, Pervez, Nahid, Zwicky, Timothy
Navigating Deep Learning to Improve ADAS22AVEP04_0404/01/2022
How edge computing drives real-time decision making in AI for smarter, safer automated vehicles. Automotive ‘big data’ is here. The immense scope of data generated by automated or ADAS-integrated vehicles spans the five SAE levels of autonomous driving, with reliance on high-resolution cameras, radar, lidar, ultrasonic sensors, GPS and other sensors for vehicles to see or perceive their surroundings. Ultimately, this sensory information - massive amounts of data - is used to navigate, avoid obstacles and read road markers necessary for safe driving. Artificial intelligence (AI) is at the heart of these operations, grounded in software algorithms and fueled by deep-learning training and deep-learning inference models that are essential to faultless performance. Enabling these vital and instantaneous processes requires AI algorithms to be trained and then deployed on-vehicle. It's a process that has developers tapping into both sophisticated software design and smart hardware strategies
Seetoo, Dustin
Data Transfer ServiceTBMG-4555004/01/2022
The emergence of distributed, extreme-scale science applications is generating significant challenges for data transfer. Ideally, high-performance data transfer should reach terabit/s throughput to make full use of the underlying networks and provide real-time and deadline-bound data transfer.
Research on Vehicle State Segmentation and Failure Prediction Based on Big Data2022-01-022303/29/2022
Vehicle failure prediction technology is an important part of PHM (Prognostic and Health Management) technology, which is of great significance to the safety of vehicles and to improve driving safety. Based on the vehicle operating data collected by the on-board terminal (T-box) of the telematics system, the research on the state of vehicle failure is conducted. First, this paper conducts statistical analysis on vehicle historical fault data. Preprocessing procedures such as cleaning, integration, and protocol are performed to group the data set. Then, three indexes including recency (R) frequency (F), and days (D) are selected to construct a vehicle security status subdivision system, and K -Means algorithm is utilized to divide different vehicle categories from the perspective of vehicle value. Labeled information of vehicles in different security status are further established. Moreover, taking engine faults as an example, this paper uses gray correlation analysis method to extract
Lu, Zheng, Liu, Jingxing, Zou, Xiaojun, Zhong, Hong, Zhang, Ailei, Wang, Liangmo
Discovering Effective Factors for Big Data-Based Fuel Cell Durability2022-01-068403/29/2022
As data emerges as the most valuable resource in the world, the evolution of the related data industry is progressing faster. In this study, we tried to discover effective factors for fuel cell durability by using big data analysis techniques with accumulated vehicle actual road data (de-identified Blue Link Data). Basic analysis is performed assuming factors that are expected to have a significant impact on the fuel cell durability performance, and durability factor modeling according to the clustering between driving patterns and durability performance is used to determine. Now can see the change in durability performance. By analyzing the correlation between each driving pattern and durability performance, it is possible to know the weight of the effective factor affecting the durability. If the effective factor with high weight is improved in the actual vehicle unit, the durability performance is expected to increase, and the effect will be verified through real road operation.
Jin, Youngpin
A Study on the Improvement of EV One-Pedal Driving System Interface and Cost Reduction2022-01-064503/29/2022
In this study it will show, big data analysis and user survey of driving records were conducted to investigate frequency of use and ease of operation of the regen paddle to control one-pedal driving system in electric vehicle. According to 3.8 million driving record big data analysis result, it was found that the driver manipulates 3.31 times on average during a single trip, mainly during the early stages of driving. According to user observation research result in 41.8% of participants did not used or used less than 5 time of regen paddle during one single trip. Also 336 participants, which occupy 83%, responded that the regen paddle manipulation for one-pedal driving was inconvenient. In conclusion, because of the use frequency of the regen paddle is low and the operation of regen paddle is inconvenient. It seems necessary to change the design of the regen paddle.
Won, Myung Won
Customer Complaints Analysis Using Textmining Method2022-01-013103/29/2022
In recent years, the automobile industry has been making efforts to develop vehicles that satisfy customers' emotions rather than malfunctions. The Vehicle Dependability Study(VDS) has been strengthened emotion items since the introduction of the new evaluation system VDS3 from 2015. The ratio of emotion items increased from 11% to 25%. In order to clarify the problem and cause of emotion items, we analyzed verbatim which is the customers' complaint data provided by J.D power every year, but it was difficult to extract customers' intention because the number of verbatim is small and expressed in terms of customer’s term rather than engineer’s term. To solve the problem, we are additionally colleting big data such as internet, warranty, online survey. Since the amount of data is very large, we developed textmining techniques such as dictionary, topic, Support Vector Machine(SVM), n-gram to improve process. And we developed the Internet Data Search(IDS) program that everyone in the
You, Hanmin
Multivariate Analysis and Index Forecast of Influencing Factors of Shanghai Municipal Domestic Waste GenerationSAE-PP-0023302/04/2022
Traditional methords of municipal domestic waste analysis and prediction lack precision,while most data's sample size is not suitable for many neural networks.In this paper, combining the advantage of deep learning methords with the results of association analysis, a waste production prediction method TLSTM is proposed based on long short-term memory(LSTM).It is found that the most influencing factors are population, public cost, household and GDP.Meanwhile,the garbage production in Shanghai will continue to decline in the future, indicating the policy of refuse classification is effective.The R-square index and MSE index of the model were 0.55 and 76571.73 respectively, surpassing other state-of-the-art models.In cooperation with School of Environmental Science and Engineering at Shanghai Jiao Tong University, the dataset comes from the average data of the Shanghai Household Waste Management Regulation from 1980 to 2020.This research method has a certain guiding significance to both
Yun, Tu
Application of AI for Predicting Test Cycles of Drivetrain Component2022-32-001401/09/2022
Industries are currently going through “The Fourth Industrial Revolution,” as professionals have called it “Industry 4.0” (I4.0). Integration of physical and digital systems for the product life cycle mainly concerns Industry 4.0. With the appearance of I4.0, the concept of prediction management has become an unavoidable tendency in the framework of big data and smart manufacturing. At the same time, it offers a reliable solution for handling test fatigue failures. AI and its key technologies play an essential role - 1 to make industrial systems autonomous like predicting test failures 2 to make possible the automatized data collection from industrial machines/components. Based on these collected data types, machine learning algorithms can be applied for automated failure detection and diagnosis. However, it is a bit difficult to select appropriate machine learning (ML) techniques, type of data, data size, and equipment to apply ML in industrial systems. Selection of inappropriate
A Measured, Agile Response to Supply Chain DisruptionsTBMG-4057001/01/2022
After so many supply chain and logistics challenges since the start of the COVID-19 pandemic, we’ve seen numerous headlines proclaiming a new era of reshoring and the end of lean production and just-in-time manufacturing strategies.
Traffic Flow Velocity Prediction Based on Real Data LSTM Model2021-01-701412/31/2021
In order to improve the energy efficiency of hybrid electric vehicles and to improve the effectiveness of energy management algorithms, it is very important to predict the future changes of traffic parameters based on traffic big data, so as to predict the future vehicle speed change and to reduce the friction brake. Under the framework of deep learning, this paper establishes a Long Short-Term Memory (LSTM) artificial neural network traffic flow parameter prediction model based on time series through keras library to predict the future state of traffic flow. The comparison experiment between Long Short-Term Memory (LSTM) artificial neural network model and Gate Recurrent Unit (GRU) model using US-101 data set shows that LSTM has higher accuracy in predicting traffic flow velocity.
Wang, Jiaze, Li, Lin
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