Remote Vehicle Research Articles

Research on the possibility of carrying out convoy driving missions using vehicles with varying degrees of autonomy

The objective of this work is to assess the possibility of driving unmanned vehicles in a convoy, depending on the vehicle type (wheeled or tracked, level 0, according to SAE J3016), and the mutual coincidence with a human-controlled vehicle in accordance with the driving scenario adopted. The assessment is based on tests carried out while driving the vehicles along a designated route and measuring the physical quantities that describe the vehicles’ motion, such as the components of the velocity vectors and distances between the vehicles. The tests were carried out on a safe training ground, using inertial-satellite devices mounted on the vehicles; they provide a good basis for planning the minimum passage corridor for a column of vehicles. During the tests, the expected distances between the vehicles were recorded and analyzed depending on the above-mentioned types of the vehicles; based on that, the possibility of using the technology in the carrying out of various missions for the needs of the tactical level units of the Polish Armed Forces was preliminarily assessed. The required lane width for the safe passage of Target 1, Hunter and Target 2 vehicles along the designated routes was calculated, taking into account the external dimensions of the vehicles, the additional widths associated with the vehicles' yaw angles and the maximum lateral distances between the vehicles. During tests of a convoy of remote-controlled vehicles, maintaining a speed of 1.5 m/s and a distance of 10 m, the requirements for the lane width for safe passage were analyzed. The largest lateral gaps were observed between Target 2 and Hunter vehicles, which may affect the planning of the convoy route. The differences in lane width between the two tests were due to the yaw angles of the vehicles and their different dimensions and drive types. In the first test, the lane width for Target 1 and Hunter was 5.50 m and for Target 2 3.70 m; in the second test it was reduced to 3.73 m and increased to 3.75 m, respectively.

Using remote-controlled vehicles (ROV) as tools for sea cucumber conservation: A review

The use of Remotely Operated Vehicles (ROVs) in marine research and conservation has become increasingly popular due to their versatility and ability to operate in challenging environments. This technology shows great potential in the conservation of marine species such as sea cucumbers, which are vital for ecosystem balance but are threatened by overfishing and habitat degradation. This review explores the application of ROVs in sea cucumber population monitoring, highlighting their role in providing real-time data on population dynamics, habitat conditions, and human impacts. The integration of ROV data with aerial orthomosaics to improve the accuracy and reliability of habitat assessments is discussed. The versatility of ROVs extends beyond sea cucumber conservation, with applications in mapping coral reefs and monitoring fish nurseries, showcasing their broad utility in marine ecosystem protection. As these technologies continue to advance, ROVs offer immense potential for developing informed and effective conservation strategies. This review underscores the critical role of ROVs in marine conservation, emphasizing their capacity to enhance our understanding of sea cucumber ecology and to support sustainable management practices. Highlights UAV and ROV integration enables precise sea cucumber habitat mapping and monitoring. OBIA and machine learning improved species identification in complex marine habitats. ROVs’ high-resolution imagery supports accurate population density and habitat surveys. UAV limitations include battery life, environmental challenges, and species detection. Future research focuses on enhanced algorithms and improved validation for data accuracy.

A data privacy survey on personal identifiable information (PII) left on rental vehicle infotainment systems

In the current automotive ecosystem, the trend of pairing mobile devices to connected vehicles is gaining momentum, providing a vast number of benefits such as hands-free driving and remote vehicle control. However, along with these conveniences arises the issue of data accumulation, ranging from vehicle diagnostics to personal identifiable information (PII). The problem emerges when a consumer rents a vehicle, pairs their mobile device to the infotainment system, and neglects to remove their device prior to returning the vehicle. This oversight can potentially expose vulnerabilities with the current renter’s PII for subsequent renters to exploit. Research indicates that renters often overlook the deletion process prior to returning the rental vehicle and are unaware of whose responsibility it is to perform this task. In this survey, we investigated the experiences and perceptions of a group of consumers who have previously rented vehicles. We wanted to know if the participants were aware they were responsible for deleting their mobile device from the rental vehicle before returning it, the renters’ importance and tolerance for risk if they overlooked the deletion process leaving their shared data on the infotainment system for subsequent users, and if they were aware who was responsible for deleting their shared data. Lastly, we explored if the participants supported an automated solution to perform this manual deletion process. The goal of this paper is to demonstrate the significance of safeguarding PII in connected vehicles and advocate for the adoption of an automated solution to mitigate this privacy risk.

A Mobile Surveillance System with Face Detection and Recognition Abilities

This paper proposes a mobile surveillance system similar to an electronic dog which has the ability to move around a certain location all while detecting and recognizing the individuals and notifying the respective authorities about the detected person. The system uses an Arduino UNO microcontroller along with IR receiver and remote to function as a remote-controlled vehicle while also providing an option for automatic movement of the vehicle. The system also has an integrated camera that constantly feeds live video to a computer system. Haar-cascades take in the frames of videos and detect appearance of human face. Then, pretrained FaceNet Convolutional Neural Network (CNN) compares the detected face to the authorized faces in an attempt to recognize the face. The system also boasts the feature of email notification for which it employs the SMTP protocol to send an email with the detected face to the authorized personnel. The methods and techniques used to implement these features are presented in depth in this paper.

Cybersecurity for tactical 6G networks: Threats, architecture, and intelligence

Edge intelligence, network autonomy, broadband satellite connectivity, and other concepts for private 6G networks are enabling new applications for public safety authorities, e.g., for police and rescue personnel. Enriched situational awareness, group communications with high-quality video, large scale IoT, and remote control of vehicles and robots will become available in any location and situation. We analyze cybersecurity in intelligent tactical bubbles, i.e., in autonomous rapidly deployable mobile networks for public safety operations. Machine learning plays major roles in enabling these networks to be rapidly orchestrated for different operations and in securing these networks from emerging threats, but also in enlarging the threat landscape. We explore applicability of different threat and risk analysis methods for mission-critical networked applications. We present the results of a joint risk prioritization study. We survey security solutions and propose a security architecture, which is founded on the current standardization activities for terrestrial and non-terrestrial 6G and leverages the concepts of machine learning-based security to protect mission-critical assets at the edge of the network.

Efficient Motion Estimation for Remotely Controlled Vehicles: A Novel Algorithm Leveraging User Interaction

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a variety of applications, including entertainment, surveillance, and delivery. However, the real-time Motion Estimation (ME) of UAVs is challenging due to the high speed and unpredictable movements of these vehicles. This paper presents a novel algorithm for optimizing ME for Remotely Controlled Vehicles (RCVs), with a particular focus on UAVs. The proposed algorithm, called Motion Dynamics Input Search (MDIS), incorporates information from vehicle motion dynamics estimation to enhance the accuracy and efficiency of ME. The MDIS algorithm addresses the challenges associated with real-time ME in RCVs by leveraging user input to guide the search for the most similar blocks in the previous video frame. Through extensive experimentation and evaluation, this study demonstrates the effectiveness of the proposed algorithm in improving ME performance for RCVs. The findings highlight the potential impact of user interaction and motion dynamics estimation in shaping the future of ME algorithms for RCVs and similar applications.

Teleoperated Driving with Virtual Twin Technology: A Simulator-Based Approach

This study introduces an innovative Teleoperated Driving (ToD) system integrated with virtual twin technology using the MORAI simulator. The system minimizes the need for extensive video data transmission by utilizing text-based vehicle information, significantly reducing the communication load. Key technical advancements include the use of high-precision GNSS devices for accurate vehicle location tracking, robust data communication via the MQTT protocol, and the implementation of the Ego Ghost mode in the MORAI simulator for precise vehicle simulation. The integration of these technologies enables efficient data transmission and enhanced system reliability, effectively mitigating issues such as communication blackouts and delays. Our findings demonstrate that this approach ensures stable and efficient operation, optimizing communication resource management and enhancing operational stability, which is crucial for scenarios requiring high video quality and real-time response. This research represents a significant advancement in ToD technology, establishing a precedent for integrating virtual twin systems to create more resource-efficient and reliable autonomous driving backup solutions. The virtual twin-based ToD system provides a robust platform for remote vehicle operation, ensuring safety and reliability in various driving conditions.

A Review of Anomalous Behavior Detection in Internet of Vehicles

The intelligent transportation system with the Internet of Vehicles as its core is gradually penetrating into the lives of urban residents, but it has also exposed security threats such as remote control of vehicles and leakage of personal information of car owners. Compared to the security issues at the level of vehicle end devices and vehicle networking service platforms, the article focuses on the security issues of abnormal behavior in vehicle networking. Based on this, the article reviews the relevant research on abnormal behavior detection mechanisms in the Internet of Vehicles environment in recent years. Firstly, the definition of abnormal behavior was analyzed, and the basic framework for detecting abnormal behavior was provided; Then, the classification of abnormal behavior detection mechanisms was discussed from three aspects: deep learning based abnormal behavior detection, spatiotemporal fusion based abnormal behavior detection, and visual based abnormal behavior detection; Finally, the unresolved technical issues and future research trends in the current abnormal behavior detection mechanism for the Internet of Vehicles were summarized.

Vehicle Control by using Human Voice for Physically Disabled and Aged People

The most crucial form of human communication is speech signals. Almost all encounters use speech signals in conversations. With a microphone, sounds and different speech signals can be transformed into electrical form. Physical disability can result from a variety of causes, such as age-related issues, health issues, and accident-related injuries. For these challenged individuals who suffer from impairments in their hands and legs, vehicles are used as a means of transportation. It might be challenging for anyone with certain conditions, such as paralysis, to manually push a wheelchair or use a remote assembly. For these individuals, the project is made to work with voice-based commands, enabling the paralyzed or impaired person to offer directions simply by speaking into the provided microphone. Additionally, the device has directional buttons for remote vehicle control. An Atmega328-based circuit interfaced with a voice recognition module builds up the system. The voice recognition module receives user commands and translates them into digital data, which the micro-controller analyzes to obtain directional orders. The transmitter circuit and the receiver circuit make up the whole system. The voice recognition module is part of the transmitter circuit, and the motor and driver assembly are part of the one that receives circuit. For communication, an NRF trans-receiver module is used. The command issued to the vehicle is shown on a 16*2 LCD. Keywords: Bluetooth module, Arduino, wireless night vision camera, robotic arm.

Remote path-following control for a holonomic Mecanum-wheeled robot in a resource-efficient networked control system

This paper introduces a novel resource-efficient control structure for remote path-following control of autonomous vehicles based on a comprehensive combination of Kalman filtering, non-uniform dual-rate sampling, periodic event-triggered communication, and prediction-based and packet-based control techniques. An essential component of the control solution is a non-uniform dual-rate extended Kalman filter (NUDREKF), which includes an h-step ahead prediction stage. The prediction error of the NUDREKF is ensured to be exponentially mean-square bounded. The algorithmic implementation of the filter is straightforward and triggered by periodic event conditions. The main goal of the approach is to achieve efficient usage of resources in a wireless networked control system (WNCS), while maintaining satisfactory path-following behavior for the vehicle (a holonomic Mecanum-wheeled robot). The proposal is additionally capable of coping with typical drawbacks of WNCS such as time-varying delays, and packet dropouts and disorder. A Simscape Multibody simulation application reveals reductions of up to 93% in resource usage compared to a nominal time-triggered control solution. The simulation results are experimentally validated in the holonomic Mecanum-wheeled robotic platform.

Системный анализ достижимости параметров динамики телеуправляемых аппаратов по показателям статической устойчивости и технической производительности

В работе приведены исследования показателей эффективности телеуправляемых необитаемых подводных аппаратов (ТНПА) при синтезе оптимальной системы управления по параметрам статической устойчивости и рабочей производительности. Анализ показателей проводился по методу минимума среднеквадратического значения ошибки управления и альтернативного ему метода динамического программирования (принцип максимума быстродействия параметра). Статическая устойчивость достигается выбором компоновки лёгкого и прочного корпуса, выбором конфигурации системы обеспечения нейтральной плавучести, но в то же время повышает общую инертность динамики аппарата и ухудшает показатель технической производительности. Управление ТНПА обеспечивается постоянной динамикой движительно-рулевого комплекса в прямом и обратном направлении и комбинацией переключения направления тяги. Лучшие условия повышения технической производительности обеспечивается применением пропорционально-интегральным дифференциальным регулятором (ПИД) в котором учёт показателей статической устойчивости реализуется идентификацией динамики ТНПА. Оптимальным с точки зрения величины отношения скорости развиваемой ДРК к возможной является величина 20дБ/дек. Реализация оптимального управления требует значительного объёма статистик коэффициентов сопротивления движению, в зависимости от направления движения, пространственного положения ТНПА и калибровочных параметров ДРК. В этом аспекте лучшие результаты получаются при исследовании моделей в аэротрубном эксперименте. The paper presents studies of the performance indicators of remote-operations vehicles (ROVs) in the synthesis of an optimal control system according to the parameters of static stability and operational productivity. The analysis of the indicators was carried out using the method of the minimum RMS value of the control error and an alternative method of dynamic programming (the principle of maximum performance of the parameter). Static stability is achieved by choosing the layout of a lightweight and durable hull, choosing the configuration of a neutral buoyancy system, but at the same time increases the overall inertia of the dynamics of the device and worsens the indicator of technical performance. The control of the POVs is provided by the constant dynamics of the propulsion and steering complex in the forward and reverse directions and a combination of switching the thrust direction. The best conditions for increasing technical productivity are provided by the use of a proportional integral differential regulator (PID) in which the consideration of static stability indicators is realized by identifying the dynamics of ROVs. The optimal value from the point of view of the ratio of the speed of the developed PSC to the possible one is 20dB/dec. The implementation of optimal control requires a significant number of statistics on the coefficients of resistance to movement, depending on the direction of movement, the spatial position of the ROVs and the calibration parameters of the PSC. In this aspect, the best results are obtained when studying models in an aero tube experiment.

Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments

In the development of intelligent mines, unmanned driving transportation has emerged as a key technology to reduce human involvement and enable unmanned operations. The operation of unmanned vehicles in mining environments relies on remote operation, which necessitates the low-latency transmission of high-definition video data across multiple channels for comprehensive monitoring and precise remote control. To address the challenges associated with unmanned driving in mines, we propose a comprehensive scheme that leverages the capabilities of 5G super uplink, edge collaborative computing, and advanced video transmission strategies. This approach utilizes dual-frequency bands, specifically 3.5 GHz and 2.1 GHz, within the 5G super uplink framework to establish an infrastructure designed for high-bandwidth and low-latency information transmission, crucial for real-time autonomous operations. To overcome limitations due to computational resources at terminal devices, our scheme incorporates task offloading and edge computing methodologies to effectively reduce latency and enhance decision-making speed for real-time autonomous activities. Additionally, to consolidate the benefits of low latency, we implement several video transmission strategies, such as optimized network usage, service-specific wireless channel identification, and dynamic frame allocation. An experimental evaluation demonstrates that our approach achieves an uplink peak rate of 418.5 Mbps with an average latency of 18.3 ms during the parallel transmission of seven channels of 4K video, meeting the stringent requirements for remote control of unmanned mining vehicles.

LANDMINE DETECTOR AND SURVEILLANCE ROBOT

Landmines and different explosives are set in war fields and other populated places which causes wounds and once in a while death to people as well as property misfortune. There are different kinds of landmines, for example, Anti-tank landmines and people killing landmines. Antitank landmines and some of the people killing landmines are in metal casing so these landmines can be identified by utilizing metal finders. These landmines must be moved for dispersion utilizing mechanical arms. For recognizing the landmine, without human obstruction, the automated remote controlled ground vehicle with appended mechanical arm design model is proposed in this paper. This model consists of a spy-cam and an ESP sensor as well as a sound buzzer along with a microprocessor that detects the landmine and also spy on enemy territory. Key Words: ESP, SPY-CAM, Automated remote control, Sound buzzer, Metal finder, Microprocessor

Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed designed to simulate an urban environment. Participants were alerted to an incoming braking event via an audio countdown to elicit anticipatory potentials that were measured using an EEG. The CSNN’s performance was compared to a standard CNN, EEGNet and three graph neural networks via 10-fold cross-validation. The CSNN outperformed all the other neural networks, and had a predictive accuracy of 99.06% with a true positive rate of 98.50%, a true negative rate of 99.20% and an F1-score of 0.98. Performance of the CSNN was comparable to the CNN in an ablation study using a subset of EEG channels that localized SCPs. Classification performance of the CSNN degraded only slightly when the floating-point EEG data were converted into spike trains via delta modulation to mimic synaptic connections.

Development of Online Based Smart Vehicle Renting System

Abstract—The purpose of this system is designed to streamlinethe rental process and enhance user experience. Leveraging web technologies, the system offers a user-friendly interface for browsing, booking and managing vehicle rentals. Key features include real-time vehicle availability, secure online payments and integration with GPS for remote vehicle tracking. Through automation and optimization, this platform aims to change the vehicle rental industry by providing convenience, efficiency and enhanced customer satisfaction. Index Terms—GPS

A survey on Transportation System Using the WPM method

Transportation System, Introduction: Every movement has a goal, a starting point, possibly a number of intermediate places, and a final location. Transport systems, which are made up of infrastructure, modes, and terminals, support and propel mobility. They allow for interaction and the pursuit of economic, social, social, or political endeavors by individuals, organizations, corporations, regions, and nations. The main goal of this textbook is to help readers understand how geography of transportation and mobility are related. With a wide-ranging overview of the field's concepts, procedures, and areas of application, The Geographic location of Transport Systems provides a thorough and approachable introduction. Research significance: There are twelve chapters in the textbook. A particular conceptual aspect of transport topography, such as routes, modes, destinations, and urban transportation, is covered in each of the first ten chapters. Emerging issues like globalization, management of supply chains, computing infrastructure, energy, and also the environment are thoroughly covered in advisement to these traditional subjects. Methodology: Popular Multiple Dimension Analysis (MCDA) and Inter Decision Making (MCDM) techniques include the Weighted Product Model (WPM). The primary distinction is that multiplication has replaced addition as the fundamental mathematical operation. The objective of this research is to suggest a CS evaluation method from the perspective of the customer for the development of personalized products. Alternative: Roadside Assistance, Routing/location Assistance, Convenience services, and Remote Vehicle Diagnosis. Evaluation Option: Extremely satisfied, satisfied, somewhat satisfied, Not Very satisfied, not at all satisfied Result: “from the result it is seen that not at all satisfied and is got the first rank whereas is the Not Very satisfied got is having the lowest rank”. Conclusion: “The value of the dataset for Nutritional Facts in Weighted product method shows that it results in not at all satisfied and top ranking”.

A mathematical analysis of teleoperator optimization using multiple sensors

As the role of remote vehicle operation gains prominence, ensuring robust wireless communication systems is crucial. Prior studies have delved into various aspects of vehicle teleoperation, including its legal framework and workforce implications. Some have even formulated models to estimate the number of remote operators for large automated vehicle fleets. These models suggest that automated vehicles overseen by remote operators could supplant a significant portion of current driving jobs in the United States. Such findings underscore the need for governmental review and policy adjustment to better accommodate the burgeoning field of teleoperation, which could substantially accelerate the advancement of vehicle automation. The present study focuses on evaluating the reliability of a bi-directional communication channel based on key variable parameters. This assessment aids in determining the optimal parameter settings, thereby enhancing the effectiveness of teleoperation systems.

Accurate Classification and Prediction of Remote Vehicle Position Classes Using V2V Communication and Deep Learning

Accurate Classification and Prediction of Remote Vehicle Position Classes Using V2V Communication and Deep Learning

Performance improvement of DC servo motor using sliding mode controller

<p>Sliding mode control has emerged as a valuable technique for enhancing dynamic response in various fields, including load frequency regulation and remote vehicle applications. While the widely adopted PID controller has proven effective for optimizing control tasks in industries, sliding mode control offers distinct advantages. By controlling the slope of the dynamical trends of state variable behavior, it enables rapid dynamic response with minimal or no overshoot, as well as negligible steady-state error. The robustness of sliding mode control, which makes it highly resilient to changes in plant parameters and outside disturbances, is one of its main advantages. A digital computer simulation was run using Simulink in the MATLAB software, concentrating on a position control system using an armature voltage-controlled D.C. servo motor to assess how well it performed. To learn more about the operation of sliding mode control, several control laws were used and state trajectories were examined. When compared to the conventional tuned PID control, the findings and discussion conclusively show sliding mode control to be more successful. The sliding mode technique has exceptional effectiveness, including enhanced dynamic response, less overshoot, and almost no steady-state error. Furthermore, its robust nature ensures consistent operation even in the face of parameter fluctuations and external disturbances. This study underscores the immense potential of sliding mode control as a powerful alternative to conventional control methods. Its ability to enhance system performance, coupled with its inherent robustness, makes it a compelling choice for various industrial applications where precise control and resilient operation are crucial.</p>

ICVTest: A Practical Black-Box Penetration Testing Framework for Evaluating Cybersecurity of Intelligent Connected Vehicles

Intelligent connected vehicles (ICVs) are equipped with extensive electronic control units which offer convenience but also pose significant cybersecurity risks. Penetration testing, recommended in ISO/SAE 21434 “Road vehicles—Cybersecurity engineering”, is an effective approach to identify cybersecurity vulnerabilities in ICVs. However, there is limited research on vehicle penetration testing from a black-box perspective due to the complex architecture of ICVs. Additionally, no penetration testing framework has been proposed to guide security testers on conducting penetration testing for the whole vehicle. The lack of framework guidance results in the inexperienced security testers being uncertain about the processes to follow for conducting penetration testing. Moreover, the inexperienced security testers are unsure about which tests to perform in order to systematically evaluate the vehicle’s cybersecurity. To enhance the penetration testing efficiency of ICVs, this paper presents a black-box penetration testing framework, ICVTest. ICVTest proposes a standardized penetration testing process to facilitate step-by-step completion of the penetration testing, thereby addressing the issue of inexperienced testers lacking guidance on how to initiate work when confronted with ICV. Also, ICVTest includes 10 sets of test cases covering hardware and software security tests. Testers can select appropriate test cases based on the specific cybersecurity threats faced by the target object, thereby reducing the complexity of penetration testing tasks. Furthermore, we have developed a vehicle cybersecurity testing platform for ICVTest that seamlessly integrates various testing tools. The platform enables even novice testers to conduct vehicle black-box penetration testing in accordance with the given guidance which addresses the current industry’s challenge of an overwhelming number of testing tasks coupled with a shortage of skilled professionals. For the first time, we propose a comprehensive black-box penetration testing framework and implement the framework in the form of a cybersecurity testing platform. We apply ICVTest to evaluate an electric vehicle manufactured in 2021 for assessing the framework’s availability. With the aid of ICVTest, even testers with limited experience in automotive penetration can effectively evaluate the security risks of ICVs. In our experiments, numerous cybersecurity vulnerabilities were identified involving in-vehicle sensors, remote vehicle control systems, and in-vehicle controller area network (CAN) bus.