Car Network Research Articles

Resilience evaluation and enhancing for China’s electric vehicle supply chain in the presence of attacks: A complex network analysis approach

Over the past decade, the electric vehicle (EV) industry has experienced rapid growth, necessitating a deeper understanding of its supply chain network’s structure and resilience. This study presents an analysis into China’s EV supply chain network through a two-tier network, employing complex network approach to explore its topological characteristics and network’s resilience. The results reveal that the nodes within China’s EV network follow a power-law distribution, with shorter average path lengths compared to other automobile networks, indicating a concentration of connections upstream. The resilience assessment shows that while the network is resilient against random attacks, it is significantly vulnerable to targeted attacks. Importantly, the High-Betweenness Addition (HBA) strategy is identified as a better strategy for improving the network’s overall resilience. This strategy of reinforcing the centrality of core nodes within the supply chain is highlighted as a crucial approach for enhancing the network’s resilience. Our findings underscore the importance of developing strategic connections among high-betweenness nodes to distribute these enhancements evenly, aiming to add one or two edges for enhancing network resilience. This study offers a lucid roadmap for enhancing the resilience of China’s EV supply chain network in the face of emerging challenges.

Enhancing intrusion detection: a hybrid machine and deep learning approach

The volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile networks. The network systems transmit diverse and heterogeneous data in dispersed environments as communication technology develops. The communications using these networks and daily interactions depend on network security systems to provide secure and reliable information. On the other hand, attackers have increased their efforts to render systems on networks susceptible. An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. This paper implements a hybrid model for Intrusion Detection (ID) with Machine Learning (ML) and Deep Learning (DL) techniques to tackle these limitations. The proposed model makes use of Extreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN) for feature extraction and then combines each of these with long short-term memory networks (LSTM) for classification. Four benchmark datasets CIC IDS 2017, UNSW NB15, NSL KDD, and WSN DS were used to train the model for binary and multi-class classification. With the increase in feature dimensions, current intrusion detection systems have trouble identifying new threats due to low test accuracy scores. To narrow down each dataset’s feature space, XGBoost, and CNN feature selection algorithms are used in this work for each separate model. The experimental findings demonstrate a high detection rate and good accuracy with a relatively low False Acceptance Rate (FAR) to prove the usefulness of the proposed hybrid model.

An Improved Deep Network and Handcrafted Feature-Based Scene Classification Convolutional Model for Self-Driving Cars

An Improved Deep Network and Handcrafted Feature-Based Scene Classification Convolutional Model for Self-Driving Cars

Optimization on land development intensity of new industrial towns based on carrying capacity of multi-modal traffic network

ABSTRACT In the context of China’s new urbanization, the establishment of ‘new industrial towns (NITs)’ has emerged as a pivotal strategy for facilitating urban development. However, the current construction of NITs confronts numerous challenges, including issues such as unreasonable land development intensity (LDI) and overburdened transportation infrastructure. Urgent attention and improvement are required to address the coordination gaps between urban land use and transportation systems. This research explores the traffic carrying capacity (TCC) of NITs at a multi-modal super-network level, developing a bi-level programming model for LDI optimization and proposing an improved genetic algorithm (GA) to solve it. Taking the NIT in Laiwu District, Jinan, China as a case study area, the results demonstrate that our approach maximizes the utilization of space–time resources in both bus and car networks, particularly benefiting the latter during morning peak hours, thereby achieving a more balanced supply-demand traffic equilibrium.

A Moving Metaverse: QoE challenges and standards requirements for immersive media consumption in autonomous vehicles

The evolution of Autonomous Vehicles (AVs) has blurred the distinction between drivers and passengers, resulting in increased demand for in-car entertainment. Simultaneously, social networking platforms are evolving to adopt AR (Augmented Reality)/VR (Virtual Reality)-centric metaverses. Self-driving cars will free up the drivers and passengers to engage in leisure activities such as watching VR videos and playing games while travelling. Before driverless cars can become a “third living space”, open challenges confront the provision of immersive content (e.g., VR videos and gaming) with a high level of Quality of Experience (QoE). Current autonomous vehicular network technologies focus on navigation and safety and rely on ubiquitous high-speed 5G/6G network connectivity to provide a seamless streaming experience in AVs. The compounded risks of cyber- and motion-sickness are identified as barriers to the enjoyment of in-car VR entertainment technologies. The influence of AV car networks in different traffic conditions is identified as a key factor to minimize a person’s risk of sickness and increase overall QoE. However, assessing the QoE of immersive media consumption in AV to its full potential is a difficult task that requires proper standard guidelines and directions. Existing surveys focus only on a specific metaverse or transportation system. These surveys lack a comprehensive view of the entire immersive media consumption process, QoE factors and aspects, and QoE standards for assessing immersive content in vehicular networks. To address these issues, we present an overview of the research landscape in the context of QoE and network standards that highlights the need for experimental studies to inform and consolidate the methodologies and recommendations for standardization of immersive content QoE assessment in AVs. Research and user studies have not yet yielded standards or recommendations describing assessment methodologies to evaluate immersive systems, such as exist for traditional video and images. A framework is outlined to provide a comprehensive understanding of the QoE influencing factors and aspects that will assist in adapting, augmenting, and producing standards for immersive entertainment in autonomous vehicles.

Development of transport logistics in tourism

The purpose of the proposed article is a theoretical study of the current state and prospects for the development of transport logistics in Ukraine, determination of the list of factors and limitations that determine the specificity of logistics in tourism. The methodology is based on scientific and special research methods. Methods of analysis, synthesis, systematization, classification, generalization of economic and methodical literature, scientific publications were used. Scientific novely. Research on the development of theoretical and methodological foundations of international transport and, especially, automobile networks in the conditions of world globalization and the substantiation of practical recommendations and approaches for improving the logistics of international road transportation in tourism. Conclusions.. The article deals with important problems of transport logistics in tourism. An analysis of the real state of development of transport logistics in Ukraine and its place in tourism has been carried out. It was determined that the prospects for the development of the logistics services market in Ukraine directly depend on the state of the country’s economy. It has been proven that the use of logistic principles and techniques in the management of tourist firms makes it possible to significantly increase the profitability of the tourist business by reducing costs and increasing the level of logistical coordination of all tourist service operations. The complex of components of the tourist service includes means of accommodation and food for tourists, means of their transportation, software and excursion support, informational and financial services, etc.

Driving the Future: Design and Implementation of an Advanced Smart Car Network Simulation Platform for Education

— Incorporating smart car network education into an academic setting often faces a substantial cost barrier. Traditionally, creating a realistic learning environment involved significant investments, such as procuring an actual smart car or specialized automotive-grade testing equipment. However, these approaches pose challenges, especially in resource-constrained educational contexts. This project addresses these challenges by introducing an innovative solution—a smart car education platform. Our objective is to design and implement this platform using a limited number of microcontrollers and components extracted from modern vehicles, enabling the creation of a lifelike simulation of genuine smart car networks. By adopting this approach, we enhance accessibility for students interested in gaining practical insights into automotive networks, offering a more pragmatic and cost-effective educational pathway.

Design and Simulation of Efficient Dynamic Data Forwarding Protocol towards Route Optimization in VANET Infrastructure

The VANET, which consists of the Internet and the Internet of Things (IoT), has recently been implemented with the Internet of Vehicles (IoV). Through the use of a car network and an AdHoc approach, it provides the user with environmental traffic information. Combining the networks of cars, known as VANETs, allows for infrastructure-free data transfer, particularly for entertainment and emergency vehicle notification. The data or packet delivery ratio, data communication latency, data transmission faults, and details of vehicle collisions are among the traffic information provided by the internet of vehicles. In this work, an unmanned aerial vehicle (UAV) is used to solve the problem of route optimisation in VANET topologies for effective data transfer. Many routing protocols are used to improve the effectiveness of the packet or data routing process. In order to alleviate the routing issue and generate an effective way for efficient data transfer with strong network stability, the Optimised Link State Routing (OLSR) Protocol is used in this study with energy-saving techniques on many parameters. To evaluate the efficacy of the suggested routing technique on various network sizes, extensive simulations are carried out on the NS2 simulator with various network parameters. When compared to traditional routing protocols along UAV-based protocols for data transmission, the performance results of the suggested data routing solution employing OLSR protocol achieve higher efficient pathways. Ultimately, the suggested routing system produces superior outcomes in terms of message throughput, message delivery ratio, latency and network overhead on comparing with existing routing protocols.

Оценка транспортной связности Европейского Севера России

Transport connectivity has been and remains one of the key endogenous factors in the socioeconomic development of the Russian Federation. At the same time, in relation to the northern regions of Russia, connectivity is a factor not so much in terms of development as retention and preservation of the development of their territories, which is strategically important for the country due to the existing natural resource potential. The article analyzes the trends in the development of the transport system of the European North of Russia based on official statistics. As a result, its strengths have been identified, including the presence of all types of transport and related infrastructure on the territory, international transport corridors, etc., as well as its weaknesses, including a low density of highways, a high degree of depreciation of fixed assets, etc. The assessment of the transport connectivity of the largest settlement nodes in the European North has been carried out. As a result, it was found that the existing configurations of road and rail networks between them are significantly inferior to “ideal” options. Thus, the routes within the automobile network are about 51% longer than the shortest possible ones, and the railway is 1.7 times longer. It is shown that weak transport connectivity is a consequence not only of natural limitations but also of the weak efficiency of mechanisms for the development of the transport system.

Electromobility in BiH

Abstract In this paper an overview of the current state of electromobility in Bosnia and Herzegovina (BiH) and a prediction of the future state of electromobility in BiH according to models from the European Union (EU) and the state expected in the future is given. Based on the reference state, an analysis of energy consumption and energy savings due to the increase in the number of EVs in the future was performed. The main goal of this paper is to provide predictable data on the number of EVs expected in the future, according to models from the EU. These data can be used when planning the development of the network for electric cars, mainly charging stations. An analysis of the current state of the number and locations of charging stations for electric vehicles (EVs) was made.

Hierarchical Clustering Based on Dendrogram in Sustainable Transportation Systems

Each group in a data-driven automobile network has its cluster head. A group can communicate with each other and members of other groups once it has been founded. Vehicles belonging to each group near the other group allow intergroup communication. Because nodes in automotive networks move so quickly, routing in these networks is a complex problem to solve. Each cluster in hierarchical clustering can be partitioned into multiple sub-clusters. Put another way, and the data is stored in a cluster, which is then divided into more clusters. The data is stored directly in separate clusters in non-hierarchical approaches. A dendrogram is a type of hierarchical tree. We anticipate increasing information sharing in clusters by properly clustering vehicles on the road and establishing clusters of the desired size in the relevant dendrogram. We can select clusters of the necessary extent and compare the Quality of Service (QoS) network’s outcomes by breaking the dendrogram at different levels. The findings reveal that the suggested method outperforms AIVISN in delay, PDR, overhead, and Drooped packets compared to AIVISN, 7.12%, 12.21%,8.32%, and 7.34%, respectively.

Retracted: Innovative Research of Trajectory Prediction Algorithm Based on Deep Learning in Car Network Collision Detection and Early Warning System

Retracted: Innovative Research of Trajectory Prediction Algorithm Based on Deep Learning in Car Network Collision Detection and Early Warning System

Assistive Self-Driving Car Networks to Provide Safe Road Ecosystems for Disabled Road Users

Disabled pedestrians are among the most vulnerable groups in road traffic. Using technology to assist this vulnerable group could be instrumental in reducing the mobility challenges they face daily. On the one hand, the automotive industry is focusing its efforts on car automation. On the other hand, in recent years, assistive technology has been promoted as a tool for consolidating the functional independence of people with disabilities. However, the success of these technologies depends on how well they help self-driving cars interact with disabled pedestrians. This paper proposes an architecture to facilitate interaction between disabled pedestrians and self-driving cars based on deep learning and 802.11p wireless technology. Through the application of assistive technology, we can locate the pedestrian with a disability within the road traffic ecosystem, and we define a set of functionalities for the identification of hand gestures of people with disabilities. These functions enable pedestrians with disabilities to express their intentions, improving their confidence and safety level in tasks within the road ecosystem, such as crossing the street.

PARAMETERS OF THE COMPONENTS OF THE HIGH VOLTAGE NETWORK OF ELECTRIC CARS
 AND THEIR CALCULATION

The article considers the components of high-voltage networks of electric cars and their relationship with
 the components of low-voltage networks, as well as the methods of their conversion. The dependence of the weight and
 size parameters on the voltage of the electric car high-voltage network is shown. The calculations showed the
 possibility of using the "voltage drop" parameter in the wires to control the current strength without using additional
 shunts. The distinctive features of the output power switches and the inverter driver circuit are shown. The operation
 of a single-phase bridge circuit with four different switching states is analyzed. The principle of organizing a sinusoidal
 form using pulse-width modulation is shown, with a given oscillogram of the generated signal. The synthesis of a threephase
 alternating current system is shown. The parameters of the absolute and relative slip of the motor rotor are
 determined by reading the rotational speed and position of the motor rotor and the rotating magnetic field of the stator
 when reading from the output stages of the inverter.

Balancing Security and Energy-Efficiency in Vehicular Ad-Hoc Sensor Networks Using EHDSORP

Automobile networks have been the focus of growing amounts of research from scholarly community and organization as a method to improve traffic safety and provide drivers and passengers with real-time data. Beyond that, however, what is driving the expansion of VANETs is the need for a safe and secure network in moving vehicles. Ad hoc vehicular networks are made up of intelligent, on-the-road cars that can exchange data with one other and with permanent roadside infrastructure. In order to provide an energy-efficient and secure routing for ad-hoc sensor networks, a balancing security-based energy-aware routing leveraging the hybrid Fuzzy logic technique with dove swarm optimization-based routing (HFLDSOR) protocol is proposed in this study. The sensor nodes in this balancing security-based energy-aware routing are first set up in the ad hoc network. The clustering is carried out using an estimate of the distance between each node, and the cluster head (CH) is chosen using a fuzzy logic method. After the selection of the CH, the distance between each node, the quantity of energy left, and the number of neighbours for each node are evaluated as fuzzy logic criterion. In order to choose the best routing route for secured communication, the trustworthy nodes are forwarded. Balancing security-based hybrid vehicles that use less energy The optimum paths to the target are chosen using fuzzy logic method with dove swarm optimization-based routing (SE- HFLDSOR) protocol approach based on the parameters such as distance delay and energy objective function. Consequently, the SE- HFLDSOR protocol was used for safe and energy-efficient routing. The suggested model outperforms the comparable current procedures, according to the assessment performed using the MATLAB simulation tool. Hence, the SE- HFLDSOR protocol was appropriate for real-time applications.

РАЗРАБОТКА РАЗЛИЧНЫХ ВИДОВ ЦЕМЕНТОВ ДЛЯ УСТРОЙСТВА КОНСТРУКТИВНЫХ СЛОЕВ ДОРОЖНОЙ ОДЕЖДЫ НА ОСНОВЕ АСФАЛЬТОГРАНУЛОБЕТОННЫХ СМЕСЕЙ

Today, the highway is not just a construction site, but also an important component in the structure of the country's economic development. Recently, transport loads on the structural layer of pavement have increased many times over as a result of an increase in freight turnover on the roads of the automobile network. To maintain the operational reliability of roads and extend their life cycle, high-quality and innovative materials are needed, technological modes of work adapted to them, as well as timely laboratory research, quality control and monitoring of materials being developed. This will improve the physical and mechanical properties of materials used in the construction of pavement structural layers. One effective approach is to reinforce the pavement base using cement based asphalt granular concrete mix (AGC), which involves the recycling of local materials instead of expensive stone materials. In the work, studies were carried out on the effect of a binder based on NLMK PJSC granulated blast-furnace slag, modified with various chemical additives, on the physical and mechanical properties of the asphalt granular concrete mixture. The effectiveness of the developed compositions lies in the fact that the slag becomes more hydraulically reactive not as a result of activation with caustic alkali or other low-modulus soluble silicates, but due to the use of special reagents that do not adversely affect process equipment and workers. Moreover, the annual growth of carbon dioxide emissions into the atmosphere in the production of cement expands the need for the use of alternative types of binders in construction. It has been established that samples of AGBS-K type asphalt-granular concrete mixture based on the developed compositions of an inorganic binder exceed the strength characteristics of composites using cement.

Hierarchical federated learning with mobile edge computing in the Internet of Vehicles

Federated Learning is a distributed machine learning framework, which can be used in the Internet of Vehicles to train deep learning models without directly accessing the original data of mobile edge vehicle nodes. ECS can access massive data, but it has the characteristics of high latency and high communication overhead. However, mobile edge computing (MEC) platform can directly and efficiently communicate with mobile edge vehicle nodes. Combining the advantages of the two, a three-layer federated learning system of edge car network edge server cloud server is used. This system is supported by the HierFedProx algorithm and aggregates the model output of the edge car to the edge server to improve the model learning efficiency and reduce the global communication frequency. The experimental results show that the system can reduce the training time and improve the accuracy of the model compared with the federated learning without introducing the edge server.

CRITICAL ANALYSIS OF THE ENVIRONMENTAL IMPACT OF FUELS USED IN VEHICLES

In the article, in the modern socio-economic and political development of our republic, one of the most important areas for the development of the automotive industry and the transport system and the development of transport infrastructure is the development of an automobile network, the organization of a fuel supply reserve for these vehicles in the near future and the use of other types of fuel, recommendations are given.

An Intelligent Channel Estimation Algorithm Based on Extended Model for 5G-V2X.

Car networking systems based on 5G-V2X (vehicle-to-everything) have high requirements for reliability and low-latency communication to further improve communication performance. In the V2X scenario, this article establishes an extended model (basic expansion model) suitable for high-speed mobile scenarios based on the sparsity of the channel impulse response. And propose a channel estimation algorithm based on deep learning, the method designed a multilayer convolutional neural network to complete frequency domain interpolation. A two-way control cycle gating unit (bidirectional gated recurrent unit) is designed to predict the state in the time domain. And introduce speed parameters and multipath parameters to accurately train channel data under different moving speed environments. System simulation shows that the proposed algorithm can accurately train the number of channels. Compared with the traditional car networking channel estimation algorithm, the proposed algorithm improves the accuracy of channel estimation and effectively reduces the bit error rate.

Practice and Exploration of Cyber Security Management System

With the rapid development of the field of intelligent network connected vehicles, the level of automobile informatization has been greatly improved. The scene of information interaction is more and more complex, the type of information is more and more diverse, at the same time, the amount of information is more and more huge. Therefore, automobile enterprises are facing more and more security risks in network security. Under the background of continuous improvement of relevant standards and regulations at home and abroad and gradual deepening of supervision by relevant departments, this paper designs and develops a set of automobile network security management system, based on relevant theoretical knowledge in this field and the practical experience of mainstream automobile enterprises and parts suppliers. The system analyzes and explores the construction of automobile network security management system, and clarifies the necessity of system construction, aiming to provide construction ideas for relevant enterprises in the upstream and downstream of automobile industry chain to build automobile network security management system, so as to systematically resolve network security risks. This system helps automobile enterprises to meet compliance related requirements, while strengthening their own internal control mechanism, ensuring business continuity, and developing high-quality in the fast lane of intelligent networking.