Virtual Vehicle Research Articles

Enhancing dealership management software with AI integration for improved customer service and future innovations

This review paper explores the integration of Artificial Intelligence (AI) into dealership management software to enhance customer service and foster future innovations. The primary objective is to synthesize existing research and industry practices to understand how AI technologies can streamline operations, personalize customer interactions, and drive business growth within automotive dealerships. The paper reviews key AI applications, including AI-driven chatbots and virtual assistants that provide automated support, predictive analytics for customer behavior and sales forecasting, and personalized marketing strategies. The analysis reveals that AI significantly improves customer service efficiency by reducing response times and enhancing customer satisfaction. Predictive maintenance and inventory management powered by AI algorithms ensure optimal resource allocation, minimizing downtime and operational costs. Additionally, the paper identifies potential future innovations facilitated by AI advancements, such as augmented reality (AR) for virtual vehicle tours and blockchain for secure transaction management. These innovations promise to elevate the customer experience and streamline dealership processes, creating a competitive edge in the market. The integration of AI into dealership management software presents substantial benefits in improving customer service and operational efficiency. The paper emphasizes the need for continuous investment in AI technologies and employee training to fully harness these advantages. Future research should focus on exploring emerging AI applications and their long-term impact on the automotive industry, ensuring dealerships remain adaptable and competitive in a rapidly evolving technological landscape. Keywords: Artificial Intelligence, Dealership Management Software, Customer Service, Predictive Analytics, Augmented Reality, Blockchain, Automotive Industry.

Fault Detection Methods for Electric Power Steering System Using Hardware in the Loop Simulation

The development of the automotive industry is associated with the rapid advancement of onboard systems. In addition, intensive development in the electronics and control systems industry has resulted in a change in the approach to the issue of assistance systems in vehicles. Classic hydraulic systems have been almost completely replaced by modern electric power steering (EPS) systems, especially in citizen vehicles. This paper focuses on fault detection algorithms for EPS, along with the available tools to aid development and verification. The article discusses in detail the current state of knowledge in this area. The principle of operation of the EPS system and the influence of the structure of the mechanical system on its operation, in particular the characteristics of the ground–tire contact, are presented. Various error identification methods are presented, including those based mainly on a combination of tests of real objects as well as those combined with modern hardware-in-the-loop (HIL) equipment and virtual vehicle environment software, enabling the development of new diagnostic methods, enhancing the security, reliability, and energy control in the vehicle. A review of the literature indicates that although many algorithms which enable fault detection at an early stage are described, their potential for use in a vehicle is highly limited. The reason lies in simplifications, including models and the operating EPS temperature range. The most frequently used simplification of the model is its linearization, which significantly reduces the calculation time; however, this significantly reduces the accuracy of the model, especially in cases with a large range of system operation. The need for methods to detect incipient faults is important for the safety and reliability of the entire car, not only during regular use but also especially during life-saving evasive maneuvers.

Gravity train of variable mass

The gravity train is a virtual vehicle that travels in a tunnel across Earth, and its motion due to only gravity is a simple harmonic oscillator. It can run at a constant velocity by appropriately exhausting fuel. In this case, the train obeys the equation of motion for a variable mass system. We discuss the mass and energy reduction of the gravity train during travel and find the optimal conditions for economical travel. This study is suitable for university students in physics classes.

Development of a Genetic Algorithm-Based Control Strategy for Fuel Consumption Optimization in a Mild Hybrid Electrified Vehicle’s Electrified Propulsion System

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs’ development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development.

Effects of decades of physical driving experience on pre-exposure postural precursors of motion sickness among virtual passengers

Previous research has shown that motion sickness associated with virtual vehicles is more common among passengers than among drivers. Separately, other studies have shown that postural precursors of motion sickness during virtual driving differ as a function of prior experience driving physical vehicles. We investigated the intersection of those prior effects: We asked whether decades of physical driving experience 1) would influence motion sickness among passengers in a virtual vehicle, and 2) would influence postural precursors of motion sickness among passengers in a virtual vehicle. In our study, middle-aged adults were exposed to a virtual vehicle as passengers. Some participants (Physical Drivers) had decades of experience driving physical automobiles, while others (Physical Non-Drivers) had rarely or never driven a physical vehicle. First, we measured head and torso movement as standing participants performed simple visual tasks. Then, each participant watched a recording of the motion of a virtual vehicle, which induced motion sickness in some participants. Afterward, neither the incidence nor the severity of motion sickness differed between Physical Drivers and Physical Non-Drivers. Our analysis of pre-exposure standing body sway revealed postural precursors of motion sickness in measures of the spatial magnitude and temporal dynamics of movement. In statistically significant interactions, these precursors (Well vs. Sick) differed as a function of physical driving experience (Physical Drivers vs. Physical Non-Drivers). Overall, our results indicate that, among virtual passengers, long-term real-world driving experience influenced the postural precursors of motion sickness, but not the incidence or severity of motion sickness. We discuss these results in terms of relationships between perception and motor control in theories of motion sickness etiology.

The Impact of Augmented Reality (AR) on User Engagement in Automotive Mobile Applications

The digital and automotive application ecosystems have been rapidly transformed by augmented reality (AR). Mobile app augmentation in the automotive sector has shown a lot of potential in terms of improving user engagement and the overall experience. Exploring the impact of augmented reality on user engagement, advantages, problems, and future possibilities in automotive mobile apps is the focus of this research. With the use of augmented reality (AR) technology integrated into automobile smartphone applications, users may enjoy real-time experiences, navigational assistance, and virtual vehicle customisation. The most essential benefit of all of these additions is the improvement they bring to user engagement and happiness. By superimposing digital information over the user's actual surroundings, the technology creates an immersive and natural experience. It has also covered the difficulties of implementing automotive applications, such as the high cost of developing robust technology and concerns about user privacy. On the other hand, with the advent of new affordances, the future seems bright for the widespread use of augmented reality technology. Because of this, augmented reality (AR) for automotive mobile apps has a promising future in terms of innovation and integration across different parts of the user experience. That is why augmented reality (AR) in car applications greatly improves the quality of life for the user. Its real-time interaction features cater to users' wants and preferences, greatly improving the overall user experience. As augmented reality develops further, it will play an increasingly important role in the car sector, opening up a wide range of possibilities for consumer engagement and happiness.

Dynamic characteristic identification of a bridge using BPF-WT combined filtering method

ABSTRACT The application of indirect identification of dynamic characteristics of bridge structures is largely hindered due to the complexity of bridge dynamic characteristics identification environment factors and signal processing. We propose a new combined filtering method by Bandpass filter with Wavelet Transform, which combines wavelet transform and bandpass filter to break through the problem of low accuracy of indirect recognition method due to the influence of vehicle frequency and other environmental noise. This new method not only gives full play to the advantages of bandpass filters but can easily reduce the peak caused by the known vehicle frequency. It realises the layer-by-layer decomposition and reconstruction of signals through wavelet transform advantage, to achieve the effect of automatic grouping filtering. The influencing factors of the new method are analysed by a customised trolley as an example to identify the dynamic characteristics of the structure. This paper also combines this method with virtual vehicles on ABAQUS and SIMPACK software to identify the dynamic characteristics of a simply supported box girder bridge. The effectiveness and reliability of this method in identifying bridge dynamic characteristics are further verified.

Development and verification of a virtual prototype of a vehicle

BACKGROUND: Methods of mathematical modelling are widely used in vehicle development. In order to study vehicle dynamics, stability and handling, as well as to accelerate and to reduce the cost of on-board software development, it is necessary to build a digital twin which contains description of special motion of a vehicle with models of units and subsystems as parts of the vehicle. AIMS: Development and verification of a virtual prototype of a vehicle. METHODS: Development of the virtual prototype and vehicle modelling were done in the MATLAB/Simulink software package. Main derivation of the equations necessary to build the models of vehicle’s units and subsystems is given. Verification testing was conducted using special measuring equipment. RESULTS: The vehicle virtual prototype containing description of combined dynamics of bodyframe, transmission elements, suspension and wheels was developed. Comparison of results of field and virtual testing was made in order to confirm operability and adequacy of the virtual prototype. Main graphs showing dynamics of real and virtual vehicles are presented. CONCLUSIONS: Practical value of development and study lies in ability of using a virtual prototype in vehicle dynamic studies and development of on-board control systems.

Modeling the Crash Behavior of Traction Batteries for Virtual Vehicle Integration

Modeling the Crash Behavior of Traction Batteries for Virtual Vehicle Integration

Efficient self-driving control for lead vehicle following in a mixed traffic environment

Autonomous control for vehicles is rapidly developed since it not only improves the mixed traffic flow but also decreases the traffic accidents and the energy consumption. Especially self-driving technology has been attention since it progressively plays an important role in prompting comport driving condition as well as safety. In addition, eco-driving strategy is considered as the energy efficient driving technology of electric vehicles (EVs) since the eco-driving can increase the fuel economy of the limited power capacity of battery. This research introduces an energy efficient EVs control strategy with Model predictive control (MPC) and deep neural networks (DNN). MPC and DNN are combined to solve the optimal problem that is to minimize battery consumption and maximize the energy efficiency during self-driving. The estimated vehicle state information by DNN and the ratio of power consumption at the previous state are fed into the next state information of the MPC controller. Thus, the controller can optimize the energy consumption ratio with respect to the state values such as the vehicle state and the battery state of charge. The hybrid control algorithm was evaluated using PGDrive which is a virtual vehicle driving environment, and simulation results showed that the MPC controller with DNN can efficiently reduce the battery consumption and increase the energy efficient up to 1.7%.

Toward a Generic Vehicular Cloud Network Architecture: A Case of Virtual Vehicle as a Service

In recent years, cloud computing has gained more and more popularity. The motivation towards implementing cloud computing in vehicular networks is due to the availability of communication, storage, and computing resources represented by communication, vehicles, roadside units (RSUs), and central servers. These resources can be utilized and provided to vehicles, drivers on the road, travellers, and customers on the internet. Intelligent Transportation System (ITS) applications can utilize vehicular cloud computing to provide efficient real-time services, as well as to improve transportation safety, mobility, and comfort levels for drivers. In this paper, all possible vehicular cloud models are presented. Each vehicular cloud model offers different services. Integrating all vehicular cloud models into one integrated system will provide all services and serve internet users, passengers, and vehicles. Therefore, a generic vehicular cloud model is proposed. After that, a new service called Virtual Vehicle is proposed in vehicular cloud computing. The virtual vehicle is a virtual machine that migrates from one physical vehicle to another. It provides the same services as the physical vehicle according to the consumer's requirements.

SIL Setup for Motor Controller Testing in Virtual Vehicle Environments

This research paper explores the implementation and benefits of Software-in-the-Loop (SIL) setups for testing motor controllers within virtual vehicle environments. SIL testing offers significant advantages in terms of cost, efficiency, and safety compared to traditional hardware-based testing methods. This paper discusses the key components of SIL setups, their implementation strategies, and case studies demonstrating their effectiveness in real-world applications. Additionally, challenges and future research directions in SIL testing for motor controllers are also addressed.

"The democratization of virtual vehicle development has now been attained"

"The democratization of virtual vehicle development has now been attained"

Extrinsic Calibration of Visual and Inertial Sensors for the Autonomous Vehicle

The extrinsic calibration of visual-inertial sensors has always been a complex problem for autonomous vehicles because the vehicle usually moves on a plane, rendering the calibration problem degenerate. Even if the vehicle drives on a slope, many great works are ineffective due to insufficient excitation. To address these challenges, we propose a method for calibrating the extrinsic parameters of camera and IMU sensors for vehicles, which involves two key contributions. The first is the introduction of a virtual vehicle reference frame, acting as a bridge to calibrate rotation in degenerate case. This involves formulating two least-squares problems to estimate the 3D relative rotation of the camera-IMU. Secondly, we propose an approach for estimating camera-IMU translation when a vehicle is navigating on a slope. This is done by solving a linear system, and these values are then refined using an algorithm that takes into account the gravitational magnitude. Extensive experimental results show that our proposed method effectively estimates rotation in planar motion and translation in sloping motion. Moreover, it achieves competitive accuracy and consistency when compared to state-of-the-art approaches.

Research on AGV path tracking method based on global vision and reinforcement learning.

As the key to the movement of automated guided vehicle (AGV), the design of control algorithm directly affects whether AGV can follow the preset path. Aiming at the difficulty of AGV control, an AGV path tracking control method based on global vision and reinforcement learning is proposed. Firstly, the global view is obtained by the visual sensor, and the position information of obstacles and AGV is obtained by the target detection algorithm. Secondly, the path planning algorithm is used to obtain the driving path information which is used to establish a virtual environment. Thirdly, the position and pose of the physical AGV are introduced into the virtual environment by the visual sensor, and the virtual AGV is reset. Finally, the image obtained by virtual vehicle camera is input into the reinforcement learning model and the output action is sent to the physical AGV for execution. In the experimental part, this method can not only plan the driving path in different environments but also well control AGV to drive along the specified path, which proves that this method has strong robustness and feasibility.

Evaluation of drive-off procedures in dynamic driving simulators: Multisensory cues, interaction and sense of presence

The importance of a realistic driving feeling for the validity of driving simulator studies has been investigated by many researchers. These investigations focus on rather abstract test objectives like driving experience or safety aspects and indicate that a high sense of presence is important to achieve realistic behaviour of the driver. However, it is not clear if this must similarly be true for driving dynamic studies with precise test objectives like acceleration or jerk. In this paper, we investigate the influence of multisensory cues and interaction with the virtual vehicle on the experienced sense of presence and the evaluation of drive-off procedures. In a three-part experimental study with different variations of the virtual reality and the driver’s interaction with the virtual vehicle, the sense of presence is measured and compared in a between-groups design. Furthermore, the evaluations of nine drive-off acceleration profiles are compared. The results show that diverse multisensory cues do not significantly impact the experienced sense of presence regarding the general presence, spatial presence or realism. As expected, interacting with the virtual vehicle leads to higher involvement ratings. The results of the acceleration profiles evaluations indicate that multisensory cueing has a negligible effect on the evaluation of the drive-off behaviour under the tested conditions. The interaction of the driver with the virtual vehicle offers the test subject a better experience in virtual reality.

CHOOSING A NEURAL NETWORK ARCHITECTURE FOR A VEHICLE AUTOPILOT

In the paper the task of choosing a neural network architecture for creating an autopilot is considered. An autopilot was created for a virtual vehicle that can move along a defined route and respond to various traffic lights. The selected architecture, namely a convolutional neural network, has high efficiency in the task of image recognition. Autopilot consists of two convolutional neural networks, one of which recognizes the driving route, the other recognizes traffic light signals. Due to the large amount of noise, the traffic light recognition photos were processed to enhance the red channel and null the green and blue, which helped in the recognition of red and yellow colors. As an environment for training neural networks and testing the performance of the autopilot in general, a two-dimensional game with a top view was created. The autopilot model showed almost 100% accuracy in recognizing the route and traffic lights in the test environment. The positive test result showed that the autopilot can perform control in a simple environment and this gives the opportunity to complicate the operating environment. The proposed autopilot uses only images to navigate in space, which distinguishes it from the other existing autopilots and, in particular, makes it cheaper. The relevance of this work is based on studies of the increase in the number of vehicles and harmful emissions into the atmosphere in the future. In the paper the literary sources are analyzed, the rationale for choosing a neural network architecture is explained, the software implementation is described, the results of testing are shown, and the possible direction of development of this topic is indicated in the conclusions.

Mixed Cloud Control Testbed: Validating Vehicle-Road-Cloud Integration via Mixed Digital Twin

Reliable and efficient validation technologies are critical for the recent development of multi-vehicle cooperation and vehicle-road-cloud integration. In this paper, we introduce our miniature experimental platform, Mixed Cloud Control Testbed (MCCT), developed based on a new notion of Mixed Digital Twin (mixedDT). Combining Mixed Reality with Digital Twin, mixedDT integrates the virtual and physical spaces into a mixed one, where physical entities coexist and interact with virtual entities via their digital counterparts. Under the framework of mixedDT, MCCT contains three major experimental platforms in the physical, virtual and mixed spaces respectively, and provides a unified access for various human-machine interfaces and external devices such as driving simulators. A cloud unit, where the mixed experimental platform is deployed, is responsible for fusing multi-platform information and assigning control instructions, contributing to synchronous operation and real-time cross-platform interaction. Particularly, MCCT allows for multi-vehicle coordination composed of different multi-source vehicles ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , physical vehicles, virtual vehicles and human-driven vehicles). Validations on vehicle platooning demonstrate the flexibility and scalability of MCCT.

Reduction of cybersickness in head mounted displays use: A systematic review and taxonomy of current strategies

This literature review examines the existing research into cybersickness reduction with regards to head mounted display use. Cybersickness refers to a collection of negative symptoms sometimes experienced as the result of being immersed in a virtual environment, such as nausea, dizziness, or eye strain. These symptoms can prevent individuals from utilizing virtual reality (VR) technologies, so discovering new methods of reducing them is critical. Our objective in this literature review is to provide a better picture of what cybersickness reduction techniques exist, the quantity of research demonstrating their effectiveness, and the virtual scenes testing has taken place in. This will help to direct researches towards promising avenues, and illuminate gaps in the literature. Following the preferred reporting items for systematic reviews and meta-analyses statement, we obtained a batch of 1,055 papers through the use of software aids. We selected 88 papers that examine potential cybersickness reduction approaches. Our acceptance criteria required that papers examined malleable conditions that could be conceivably modified for everyday use, examined techniques in conjunction with head mounted displays, and compared cybersickness levels between two or more user conditions. These papers were sorted into categories based on their general approach to combating cybersickness, and labeled based on the presence of statistically significant results, the use of virtual vehicles, the level of visual realism, and the virtual scene contents used in evaluation of their effectiveness. In doing this we have created a snapshot of the literature to date so that researchers may better understand what approaches are being researched, and the types of virtual experiences used in their evaluation. Keywords: Virtual reality cybersickness Simulator Sickness Visually induced motion sickness reduction Systematic review Head mounted display.

Optimal intermittent electrification and its effect on battery sizing and energy saving using a high-fidelity train model

The fastest way to decarbonise traction energy of a railway network is full electrification of the network. However, full electrification can be costly and is often delayed due to complicated planning procedures. Often, it is not financially viable to electrify certain sections of the network due to geographical challenges such as hillsides and requirements to raise or lower the tracks. Intermittent electrification can provide a way to reduce emissions on a route without fully electrifying it. This paper presents an optimal intermittent electrification strategy and compares its effects on required battery size to a ‘common-sense’ approach. The “common-sense” approach is a heuristic electrification method where high-power sections of the route are electrified first. Energy savings are demonstrated using a validated high-fidelity bi-mode train model on the Newbury-Plymouth route of Great Western Railway in the UK. Unlike prior research, the optimal electrification strategy considers energy consumptions for both directions of a journey on the route and is therefore optimal regardless of travel direction. The 278-km Newbury-Plymouth route was divided into 50 equal discrete sections for the optimization process, which then identified sections that should be prioritised for electrification as they consume the maximum energy. A high charge/discharge lithium titanite-oxide battery was modelled and installed on the virtual vehicle to determine the required battery sizing for given optimal percentages of intermittent electrification. The battery sizing strategy includes battery wear effects from charge/discharge cycle life and shows the battery life to be 1.42 years if kept at 55°C and 6 years if kept at 25°C. At 36% electrification, using the ‘common-sense’ approach, the battery weighs 10,500 kg and the battery-OLE train reduces the CO2 emissions and energy usage on the route by 83% when compared to the diesel-only train.