Car Network Research Articles

A Model for the Safety Risk Evaluation of Connected Car Network

The automobile industry is preparing for what is now anticipated to be a big transition toward linked and autonomous vehicles. This introduces significant dangers both to individuals' privacy and to the intricate workings of electrical and electronic systems. Currently, the automotive industry has well-established and government-mandated safety risk management methods. They present an improved risk assessment methodology and demonstrate its validity with a real-world use case to facilitate the derivation of safety procedures and safety solutions for automotive integrated devices. This should make it simpler to ensure the safety of integrated vehicles. Some of the significant factors are the accessibility of functional safety to individuals who are not trained in safety and the appropriateness of existing techniques for said functional safety. The methodology that is used to assess the risks posed by properties and capacities includes a risk analysis as an essential component. The next step in the process of managing risks involves determining both the degree of risk and the magnitude of the potential impact. In this step, various different rules and requirements that are already in place are utilized to make any necessary adjustments. After much deliberation, a level of security has been settled on in order to facilitate the formulation of high-level security objectives. Due to the fact that this design has a good convergence with contemporary principles and criteria, it ought to be capable of being adequately suited to the prerequisites of the automotive sector.

Internet of Vehicles Information Processing Method with Vehicle-Mounted Cloud Grid as the Underlying Data Fusion Structure

With the development of electronic information network technology, large car networking systems can produce all kinds of data such as text, images, and videos, a large number of heterogeneous data, different features of heterogeneous data, and different data structures. In the Internet of vehicles, the beacon message generation strategy needs to be researched and designed on the premise of meeting the requirements of vehicle location accuracy and wireless communication performance. According to the Kalman filter differential prediction equation, the message generation model of Kalman filter beacon is established. In the deep learning research on the underlying data fusion algorithm, the most effective way to solve the problem of insufficient integration degree between the underlying data is to improve the data quality and ensure data sharing and reuse between multisource heterogeneous data. Therefore, the D-S evidence theory fusion model and rough set underlying model are proposed in the vehicle-mounted cloud network. Among them, the D-S evidence theory fusion model ensures the improvement of underlying data quality, forms effective rule combination, and reduces conflicts between rules through filtering evidence theory. The rough set underlying data fusion model optimizes the underlying data of each device by improving the rough set attribute reduction method of particle swarm optimization algorithm.

A novel depth search based light weight CAR network for the segmentation of brain tumour from MR images

In this modern era, brain tumour is one of the dreadful diseases that occur due to the growth of abnormal cells or by the accumulation of dead cells in the brain. If these abnormalities are not detected in the early stages, they lead to severe conditions and may cause death to the patients. With the advancement of medical imaging, Magnetic Resonance Images (MRI) are developed to analyze the patients manually. However, this manual screening is prone to errors. To overcome this, a novel depth search-based network termed light weight channel attention and residual network (LWCAR-Net) is proposed by integrating with a novel depth search block (DSB) and a CAR module. The depth search block extracts the pertinent features by performing a series of convolution operations enabling the network to restore low-level information at every stage. On other hand, CAR module in decoding path refines the feature maps to increase the representation and generalization abilities of the network. This allows the network to locate the brain tumor pixels from MRI images more precisely. The performance of the depth search based LWCAR-Net is estimated by testing on different globally available datasets like BraTs 2020 and Kaggle LGG dataset. This method achieved a sensitivity of 95%, specificity of 99%, the accuracy of 99.97%, and dice coefficient of 95% respectively. Furthermore, the proposed model outperformed the existing state-of-the-art models like U-Net++, SegNet, etc by achieving an AUC of 98% in segmenting the brain tumour cells.

Security and Privacy Protection of Car Networking Consistent Algorithm Based on Smart Multi-Sensor

With the rapid development of the Internet and wireless communications, smart multi-sensor networks are gradually being used in various fields, and the Internet of vehicles technology combined with smart multi-sensor network came into being. However, although the Internet of vehicles technology has greatly facilitated people’s travel in traffic, its problems in security and privacy protection have also been amplified and became a major threat to the Internet of vehicles technology. Consistency algorithms are an important solution to the trust problem and are a prominent representative of distributed algorithms. In order to solve the problem of security and privacy protection of smart multi-sensors in car networking technology, in this paper, consistent algorithm and group signature verification technology are used to study the security and privacy protection performance of the Internet of vehicles in a connected random network composed of 300 nodes through artificially synthesized simulation data. Using the method in this paper, collect and analyze the experimental data of the number of messages, verification delay, traffic load, and message loss rate of randomly generated vehicles within 200 seconds and evaluate the performance of privacy protection in the maximum consistent algorithm; the results show that the verification delay will increase with the increase of the number of messages, and the more traffic load, the higher the message loss and verification delay, and the maximum consistent algorithm has good privacy protection performance. In terms of data, the RSU-based auxiliary scheme consumes 25.59% of the communication of the KPI-based signature scheme and 25.64% of the swarm scheme.

Analysis of Malicious Node Identification Algorithm of Internet of Vehicles under Blockchain Technology: A Case Study of Intelligent Technology in Automotive Engineering

False messages sent by malicious or selfish vehicle nodes will reduce the operation efficiency of the Internet of Vehicles, and can even endanger drivers in serious cases. Therefore, it is very important to detect malicious vehicle nodes in the network in a timely manner. At present, the existing research on detecting malicious vehicle nodes in the Internet of Vehicles has some problems, such as difficulties with identification and a low detection efficiency. Blockchain technology cannot be tampered with or deleted and has open and transparent characteristics. Therefore, as a shared distributed ledger in decentralized networking, blockchain can promote collaboration between transactions, processing and interaction equipment, and help to establish a scalable, universal, private, secure and reliable car networking system. This paper puts forward a block-network-based malicious node detection mechanism. Using blockchain technology in a car network for malicious node identification algorithm could create a security scheme that can ensure smooth communication between network vehicles. A consensus on legal vehicle identification, message integrity verification, false message identification and malicious vehicle node identification form the four parts of the security scheme. Based on the public–private key mechanism and RSA encryption algorithm, combined with the malicious node identification algorithm in the Internet of Vehicles, the authenticity of the vehicle’s identity and message is determined to protect the vehicle’s security and privacy. First, a blockchain-based, malicious node detection architecture is constructed for the Internet of vehicles. We propose a malicious node identification algorithm based on the blockchain consensus mechanism. Combined the above detection architecture with the consensus mechanism, a comprehensive and accurate verification of vehicle identity and message authenticity is ensured, looking at the four aspects of vehicle identification, accounting node selection, verification of transmission message integrity and identification of the authenticity of transmission messages. Subsequently, the verification results will be globally broadcast in the Internet of Vehicles to suppress malicious behavior, further ensure that reliable event messages are provided for the driver, improve the VANET operation environment, and improve the operation efficiency of the Internet of Vehicles. Comparing the proposed detection mechanism using simulation software, the simulation results show that the proposed blockchain-based trust detection mechanism can effectively improve the accuracy of vehicle node authentication and identification of false messages, and improve network transmission performance in the Internet of Vehicles environment.

Медицинская эвакуация железнодорожным транспортом пострадавших в чрезвычайных ситуациях: история и перспективы

The aims of the article are to define the world tendencies of medical evacuation by railway transport in the 21st century on the basis of analysis of scientific publications, Internet articles and mass media reports, including those carried out during SARS-COV-2 pandemic; to specify the problems and prospects of development of medical evacuation by railway in Russia taking into account global trends and local specifics. Materials and research methods. A content analysis of 15 scientific articles, 20 Internet articles, and 31 press communications devoted to individual issues of medical evacuation by railway was performed using the method of nonrepeat sampling. Selection criteria were: message time — later than January 1, 2001; article should have contained the description of a case/evidence of medical evacuation by railway or the discussion of the possibilities of application of the railway transport for medical evacuation in the 21st century. Results of the study and their analysis. The world has been conducting medical evacuation by railway for over 150 years. Traditionally, the need for medical evacuation by railway has arisen in case of armed conflicts or large-scale natural disasters, which result in great medical and sanitary losses. In the second half of the 20th century the priority importance of medical evacuation by railway was lost due to the development of automobile roads and aerodrome network and the accompanying development of automobile and air medical transport. The occurrence of emergencies in hard-to-reach areas or the inability of public health systems to organize medical care with a sharp increase in the number of victims in emergencies, including those of infectious nature, determine the need to develop additional mechanisms of medical evacuation. The use of medical evacuation by railway can become this compensatory mechanism. In this connection there has been a shift in the world’s focus in the field of medical evacuation by railway from the use of sanitary trains and flying cars to the formation of mobile medical detachments on the basis of re-equipped passenger trains. The use of re-equipped high-speed electric trains for this purpose has its advantages — it makes possible to avoid organizational difficulties and complications in patients, which are typical for sanitary aviation evacuation, as well as to provide mass medical evacuation of victims without overloading highways with sanitary vehicles. An alternative trend in the development of the medical evacuation by railway is the creation of modern civil sanitary trains and medical carriages. It has been noted that so far Russia does not have technical facilities capable to ensure the up-to-date level of the medical evacuation by railway. The future will show in what way the organization of medical evacuation by railway will develop in our country.

High-Efficiency Encryption and Authentication Network Security for Automotive Ethernet

Automotive ethernet has the advantages of high bandwidth, low latency, and strong compatibility, which meets the needs of new energy vehicles for the development of network integration. Automotive ethernet can not only solve the problem of increased wiring harness and complicated wiring in the intelligentization of automotive electronics, but also can improve the comfort, reliability and multiple safety of the car. Although the car is connected to the smart phone, Bluetooth, Internet and other network systems to improve the driving pleasure for the driver, but it brings hacker attacks, security loopholes and other car network security problems that cannot be ignored, which seriously affects the safe driving of the car, personal privacy, and even endanger public safety. In this paper, we focus on the need for the network security of automotive ethernet, and analyses problem in encryption authentication algorithm. An improved AES-128 encryption algorithm and an improved MD5 authentication algorithm are proposed innovatively. Through the experimental simulation of CANoe.Ethernet, the improved AES-128 encryption algorithm proposed in this paper is 15% more efficient than the traditional encryption algorithm, and the improved MD5 authentication algorithm is 4 times faster than the traditional authentication algorithm. Thus, the active network security performance of automotive ethernet is further improved.

Distributed Deep CNN-LSTM Model for Intrusion Detection Method in IoT-Based Vehicles

As 5G and other technologies are widely used in the Internet of Vehicles, intrusion detection plays an increasingly important role as a vital detection tool for information security. However, due to the rapid changes in the structure of the Internet of Vehicles, the large data flow, and the complex and diverse forms of intrusion, traditional detection methods cannot ensure their accuracy and real-time requirements and cannot be directly applied to the Internet of Vehicles. A new AA distributed combined deep learning intrusion detection method for the Internet of Vehicles based on the Apache Spark framework is proposed in response to these problems. The cluster combines deep-learning convolutional neural network (CNN) and extended short-term memory (LSTM) network to extract features and data for detection of car network intrusion from large-scale car network data traffic and discovery of abnormal behavior. The experimental results show that compared with other existing models, the algorithm of this model can reach 20 in the fastest time, and the accuracy rate is up to 99.7%, with a good detection effect.

CAN Bus Messages Abnormal Detection Using Improved SVDD in Internet of Vehicles

Controller Area Network (CAN) bus anomaly detection on the Internet of Vehicle (IoV) is a topic of ongoing interest and increasing importance, particularly as IoV becomes commonplace. However, existing abnormal detection schemes are not ideal due to the lack of abnormal data in IoV and the difficulty of parsing the diverse message rules in existing vehicles. While single-classification of the SVDD (Support Vector Domain Description) algorithm can detect abnormalities only with normal message information, the approach has a high rate of false negatives when deployed directly in the in-vehicle network environment. In addition, the real-time data generation (e.g., IoV messages) compounds the challenge of designing effective detection approaches. Therefore, this paper proposes a mechanism for car networking message classification for a broad range of data, and establishes a weak model for many simple redundant data in the vehicle intranet. The detection can reduce the time and computation costs of detection while ensuring accuracy. Then, this paper proposes two improved SVDD schemes: M-SVDD scheme which adds the Markov chain to detect time-related messages; G-SVDD scheme which maps the kernel function to the Gaussian kernel function to reduce the redundant area of the model and the false-negative rate. The experimental results show that our proposed schemes have higher accuracy, recall rate and fewer computation overhead.

Сравнительный анализ пространственных структур Московской и Санкт-Петербургской агломераций

The article provides a comparative analysis of the existing spatial structure of urban settlement of the two largest agglomerations of our country – Moscow and St. Petersburg. Quantitative (dynamics of the number of settlements with the status of a city, dynamics of the population of cities, the number of houses under construction and the total area of residential units under construction) and qualitative (analysis of the automobile and railway networks) indicators are used. The following scientific methods are used in the work: statistical, geostructural, cartographic modeling, grouping method. 2002–2021 the two agglomerations are characterized by positive trends in population growth, which are associated with migration growth. The supporting framework of the Moscow agglomeration is represented by a uniform radial-circular type with a mixed monopolycentric model with a dominant core and the presence of large sub-centers, while the St. Petersburg agglomeration is a coastal type with a pronounced monocentric model and exclusive dominance of the core. On the basis of the existing supporting frames, to determine the stages of development of two agglomerations, a spatial typology of cities was carried out depending on the degree of their remoteness from the corresponding central points (nuclei). As a result, five orders (belts) with averaged transport availability isochrones were identified. Transformation processes of staged agglomeration development proceed most intensively within the entire Moscow agglomeration, which is at the stage of suburbanization. In the St. Petersburg agglomeration, the most active urbanized zone are cities located at distances of up to 50 km from the central point of the core, and it is characterized by a transition from the urbanization stage to the suburbanization stage

Design and Modelling of Piezoelectric Road Energy Harvesting

In recent years, road piezoelectric energy harvesting (RPEH) has attracted great attention from industry and academia, as it can provide power to traffic ancillary facilities and low-power wireless sensor devices to support car networking and intelligent transportation. The output power of RPEH in a recent research project demonstrated a watt level RPEH. In this proposal, we propose to harvest energy from piezoelectric modules (also called stacks) to power selected highways, tolls, and bridges in Pennsylvania. The project incorporates electrical, mechanical, and civil engineering works. The proposed smart highway RPEH will be conducted using optimization parameters to evaluate the system performance and trade-offs. MATLAB will be used with other optimization solvers in problem modeling and optimization. During this project, an RPEH hardware system will be constructed. The system will include a piezoelectric module, rectifier (AC-DC), Storage battery, data acquisition system (DAQ), and computer. The captured data will be analyzed using MATLAB/Simulink. The results show that optimum harvested parameters were addressed when the thickness is selected as 2 mm.

Tensor Network-Encrypted Physical Anti-counterfeiting Passport for Digital Twin Authentication.

The trend of digitalization has produced rapidly increasing data interaction and authentication demand in today's internet of things ecosystem. To face the challenge, we demonstrated a micro-scale label by direct laser writing to perform as a passport between the physical and digital worlds. On this label, the user information is encrypted into three-dimensional geometric structures by a tensor network and then authenticated through the decryption system based on computer vision. A two-step printing methodology is applied to code the randomly distributed fluorescence from doped quantum dots, which achieved physical unclonable functions (PUFs) of the passport. The 105 bits/mm2 data storage density enables abundant encrypted information from physical worlds, for example, the biometric data of human users. This passport guarantees the strong correlation between the user's privacy data and the PUF-assisted codes, successfully overcoming the illegal transfer of authentication information. Due to its ultra-high security level and convenience, the printed passport has enormous potential in future digital twin authentication anytime anywhere, including personal identity, valuable certificates, and car networking.

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

Predicting the trajectories of neighboring vehicles is essential to evade or mitigate collision with traffic participants. However, due to inadequate previous information and the uncertainty in future driving maneuvers, trajectory prediction is a difficult task. Recently, trajectory prediction models using deep learning have been addressed to solve this problem. In this study, a method of early warning is presented using fuzzy comprehensive evaluation technique, which evaluates the danger degree of the target by comprehensively analyzing the target’s position, horizontal and vertical distance, speed of the vehicle, and the time of the collision. Because of the high false alarm rate in the early warning systems, an early warning activation area is established in the system, and the target state judgment module is triggered only when the target enters the activation area. This strategy improves the accuracy of early warning, reduces the false alarm rate, and also speeds up the operation of the early warning system. The proposed system can issue early warning prompt information to the driver in time and avoid collision accidents with accuracy up to 96%. The experimental results show that the proposed trajectory prediction method can significantly improve the vehicle network collision detection and early warning system.

Malicious vehicle detection based on beta reputation and trust management for secure communication in smart automotive cars network

High reliance on wireless network connectivity makes the vehicular ad hoc network (VANET) vulnerable to several kinds of cyber security threats. Malicious vehicles accessing the network can lead to hazardous situation by disseminating misleading information or data in the network or by performing cyber-attacks. It is a requirement that the information must be originated from the authentic and authorized vehicle and confidentiality must be maintained. In these circumstances, to protect the network from malicious vehicles, reputation system based on beta probability distribution with trust management model has been proposed to differentiate trustworthy vehicles from malicious vehicles. The trust model is based on adaptive neuro fuzzy inference system (ANFIS) which takes trust metrics as input to evaluate the trustworthiness of the vehicles. The simulation platform for the model is in MATLAB. Simulation results show that the vehicles need at least 80% trustworthiness to be considered as a trusted vehicle in the network.

Ad Hoc Vehicle Networks: Recapitulation and Challenges

Being ad-hoc in design, VA NET is a form of networks generated by the idea of building up a network of cars for a specific needs or circumstance. In addition to the benefits, VANET poses a large number of challenges such as providing QoS, high bandwidth and connectivity, and vehicle and individual privacy security. Each report discusses VANET 's state-of-the-art, explaining the relevant problems. We address in depth network design, signal modelling and propagation mechanisms m, usability modeling, routing protocols and network security. The paper's key results are that an effective and stable VANET satisfies all architecture criteria such as QoS, minimal latency, low BER and high PDR. At the end of the paper are addressed several primary work areas and challenges at VANET.

Realization of Future Car Sharing Speed Safety System Based on Internet of Vehicles

In the future, cars will integrate the advantages of intelligent network technology on the basis of car networking applications formed by car bus technology, communication technology, and Internet technology, and be equipped with safer, smarter and more comfortable car control systems. Based on the Internet of Vehicles technology, this paper proposes a car sharing speed safety system, analyzes its structure, composition and working principle, and expounds the advantages of the application of the system in the Internet of Vehicles technology. The application of the shared speed safety system will play a good supporting role in the unmanned driving, peer optimization and safe driving of future cars.

Watt-level road-compatible piezoelectric energy harvester for LED-induced lamp system

Road-compatible piezoelectric energy harvesters (RPEHs) have recently been widely demonstrated to power traffic ancillary facilities and intelligent sensing devices. The output power of RPEHs was previously always at the μW or mW level. A watt-level RPEH (π × 17.52 × 8 cm3), consisting of 40 metal–piezoelectric ceramic units, based on an improved drum-transducer structure is proposed herein. Its electrical productivity under different excitation frequencies and displacements was tested in a laboratory. At a 10 Hz excitation frequency and 2 mm excitation displacement, a maximum output voltage of 25.64 V, maximum output current of 25.64 mA, and maximum output power of 657.41 mW (power density: 6.83 W/m2) were observed at a 0.9 kΩ load resistance. Then, its electrical productivity at different vehicle speeds was tested on an actual roadway. When a small-sized vehicle passed the RPEH at a speed of 80 km/h, a maximum output voltage of 40.39 V, maximum output current of 403.9 mA, and maximum output power of 16.31 W (power density: 169.52 W/m2) were observed at a 0.1 kΩ load resistance. This is the maximum output power observed thus far on an actual roadway. The RPEH successfully illuminated a 5-m-long LED-induced 300-lamp strip, demonstrating potential for car networking and intelligent transportation.

Mobile Platform for Car Service

The analysis of the use of drives of mobile and transport robots is given. Various options for design solutions are considered, including the main selected drive of a mobile platform with four Omni-wheels, ensuring maximum mobility and maneuverability. Masses and ground clearance of vehicles of various classes are estimated to determine the overall dimensions of the mobile platform in terms of height and carrying capacity. It has been established that the necessary elements of a mobile platform are universal mounting plates and trailed equipment.The control systems of two variants have been developed. The first is automatic, for an autonomous mobile platform, and the second is for remote control. Control systems include 3 microcontrollers, 5 drivers, 6 motors, 4 sensors. The position of the mobile platform can be controlled by the operator remotely using a mobile application. Power supply of the MP is carried out from its own battery, from the car network or an external network. An algorithm for the operation of the control system for different versions is proposed. 3D models of the mobile platform and suspension unit models have been developed. Calculations of the lifting mechanism of the MP are given. The layout of the nodes of the mobile platform is presented.

Research on human-vehicle interaction based on natural language processing

With the rapid development of intelligent networked vehicles and driverless technology, the importance of the dialogue between human and vehicle artificial intelligence has also become prominent. In the case of the driver outputting speech and converting it into text, how to correctly distinguish the driver’s intention and make the correct response is the purpose of the research. Google released the first-generation BERT (Bidirectional Encoder Representations from Transformers) training model in 2018, bringing natural language processing methods to a new way of understanding. This article analyzes the current mainstream machine learning algorithms and applies them to natural language processing (NLP) to classify text data sets. The purpose is to study various machine learning models to achieve higher accuracy of text classification. Research results not only lay the foundation for improving vehicle driver interaction, but also serve as a reference for the natural language processing part of the future car networking and driverless technology.

Development of a conflict-free unsignalized intersection organization method for multiple connected and autonomous vehicles.

An effective traffic control strategy will improve travel reliability in urban transportation networks. Lack of coordination between vehicles, however, exacerbates congestion due mainly to frequent stops at unsignalized intersections. It is beneficial to develop a conflict-free cooperation method that collects basic safety message from multiple approaching Connected and Autonomous Vehicles (for short, CAVs) and guarantees efficient unsignalized intersection operations with safe and incident free vehicle maneuvers. This paper proposes an interspersed traffic organization method under controlled constraints. Firstly, relied on shared location technology and considered the operating characteristics of CAVs at unsignalized intersections to detect and analyze traffic conflicts to establish a right-of-way judgment model for CAVs. In order to further ensure the safety and operating efficiency of the vehicle, based on the judgment results of right-of-way judgment model, a vehicle speed guidance model is established for different traffic conditions. Taking the real city standard intersection as the experimental analysis object, through data collection and simulation experiment, the signal control method and the organization method proposed in this paper are compared and analyzed. The results showed that the traffic organization method proposed in this paper improves the operational efficiency of 46%, the average travel time is reduced by 6.54s, which is not only better than the signal control method, but also supports the development of car networking technology.