Direct adaptive control for lane keeping in intelligent vehicle systems

In this paper, with parametric uncertainties such as the mass of vehicle, the inertia of vehicle about vertical axis, and the tire cornering stiffness, we deal with the vehicle lateral control problem in intelligent vehicle systems. Based on the dynamical model of vehicle, by applying Lyapunov function method, the control problem for lane keeping in the presence of parametric uncertainty is studied, the direct adaptive algorithm to compensate for parametric variations is proposed and the terminal sliding mode variable structure control laws are designed with look-ahead references systems. The stability of the system is investigated from the zero dynamics analysis. Simulation results show that convergence rates of the lateral displacement error, yaw angle error and slid angle are fast.

ROBUST STABILITY ANALYSIS FOR RAILWAY VEHICLE SYSTEMS

The lateral stability for railway vehicle dynamic system with uncertainparameters and nonlinear uncertain force vector is studied by using the Lyapunov stability theory. Arobust stability condition for the considered system is derived, and the obtained stability boundsare not necessarily symmetric with respect to the origin in the parameter space. The lateralstability analysis for a railway bogie model is analyzed by using the proposed approach. Thesymmetric and asymmetric results are both given and the influence of the adjustable parameter betaon the stability bounds is also discussed. With the help of the proposed method, the robuststability analysis can provide a reference for the design of the railway vehicle systems.

Automobile Crash Investigation Based on Vehicle System Related Causes: Systematic Literature Review

Automobile accidents are one of the leading causes of death worldwide. Every year, over 1.24 million people are killed in traffic accidents. Even though automobiles are designed to help people, they have been used to kill them in large numbers. Automobile accident research has primarily focused on past tragedies to develop and implement policies to combat this pandemic. The aim of this systematic review is to assess the different methods used to investigate the vehicle system-related cause factors of road traffic accidents. Police report reports have served as a foundation for providing historical facts about the causes of automobile accidents. It has been observed that police reports have limitations when it comes to reporting the involvement of vehicle systems in causing a traffic accident. The majority of the research was conducted on articles that investigated vehicle system risk factors using statistical data. Following articles that used statistical data to investigate vehicle system risk factors, the inclusion criteria were chosen. Articles on traffic accidents published in Cameroon were included on the condition that they studied at least one traffic accident risk factor. Two hundred twenty-five distinct records were identified, and 155 full texts were screened for inclusion, resulting in the inclusion of 25 studies in the review. According to the findings, failure to break the braking system, tyre puncture, poor driving, speeding, and overtaking are the leading causes of automobile crash reports reported by police. The majority of the study’s conclusions lamented that accusing vehicle systems was based on assumptions and the reporter’s judgment. It was determined that the use of stringent vetting procedures to investigate vehicle systems is the cause of a traffic accident. As a result, stakeholders will require accurate facts from a traffic crash investigation.

OPENING NEW HORIZONS FOR VEHICLE SYSTEM DYNAMICS

The non-linear wheel-rail motional model is the first research breakthrough I have made in the field of vehicle system dynamics. The main external interference to a vehicle system in rail-borne transportation comes from the dynamic interaction between the wheel and the rail. To determine the forces exerted on the rail-contacting patches of a railcar is known to be one of the most complicated problems in rail haulage, expecially in its unsaturated state, i.e. before overall sliding occurs. Since the 1960s, many scholars, including K.L. Johnson and J.J.Kalker, have considered it a problem in rolling contact mechanics. However, none of the presented

Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue.A unique multi-mode coupling(MMC)AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles.Based on the parameters of the benchmarking model of a hybrid vehicle,the best model-predictive control-based energy management strategy is proposed.First,the drive system model was built after the analysis of the MMC-AWD’s drive modes.Next,three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed,which was followed by the design of a road driving force observer.Then,the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient.Finally,the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD.The findings indicate that,in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6,the MMC-AWD’s capacity to accelerate increases by 5.26%and 7.92%,respectively.When the road adhesion coefficient is 0.8,0.6,and 0.4,the maximum climbing ability increases by 14.22%,12.88%,and 4.55%,respectively.As a result,the dynamic performance is greatly enhanced,and the fuel savings rate per 100 km of mileage reaches 12.06%,which is also very economical.The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

Composite armor philosophy(CAP):Holistic design methodology of multi-layered composite protection systems for armored vehicles

A philosophy for the design of novel,lightweight,multi-layered armor,referred to as Composite Armor Philosophy(CAP),which can adapt to the passive protection of light-,medium-,and heavy-armored vehicles,is presented in this study.CAP can serve as a guiding principle to assist designers in comprehending the distinct roles fulfilled by each component.The CAP proposal comprises four functional layers,organized in a suggested hierarchy of materials.Particularly notable is the inclusion of a ceramic-composite principle,representing an advanced and innovative solution in the field of armor design.This paper showcases real-world defense industry applications,offering case studies that demonstrate the effectiveness of this advanced approach.CAP represents a significant milestone in the history of passive protection,marking an evolutionary leap in the field.This philosophical approach provides designers with a powerful toolset with which to enhance the protection capabilities of military vehicles,making them more resilient and better equipped to meet the challenges of modern warfare.

Dynamic thermo-mechanical responses of road-soft ground system under vehicle load and daily temperature variation

A complete road-soft ground model is established in this paper to study the dynamic responses caused by vehicle loads and/or daily temperature variation.A dynamic thermo-elastic model is applied to capturing the behavior of the rigid pavement,the base course,and the subgrade,while the soft ground is characterized using a dynamic thermo-poroelastic model.Solutions to the road-soft ground system are derived in the Laplace-Hankel transform domain.The time domain solutions are obtained using an integration approach.The temperature,thermal stress,pore water pressure,and displacement responses caused by the vehicle load and the daily temperature variation are presented.Results show that obvious temperature change mainly exists within 0.3 m of the road when subjected to the daily temperature variation,whereas the stress responses can still be found in deeper places because of the thermal swelling/shrinkage deformation within the upper road structures.Moreover,it is important to consider the coupling effects of the vehicle load and the daily temperature variation when calculating the dynamic responses inside the road-soft ground system.

Advances in Active Suspension Systems for Road Vehicles

Active suspension systems(ASSs)have been proposed and developed for a few decades,and have now once again become a thriving topic in both academia and industry,due to the high demand for driving comfort and safety and the compatibility of ASSs with vehicle electrification and autonomy.Existing review papers on ASSs mainly cover dynamics modeling and robust control;however,the gap between academic research outcomes and industrial application requirements has not yet been bridged,hindering most ASS research knowledge from being transferred to vehicle companies.This paper comprehensively reviews advances in ASSs for road vehicles,with a focus on hardware structures and control strategies.In particular,state-of-the-art ASSs that have been recently adopted in production cars are discussed in detail,including the representative solutions of Mercedes active body control(ABC)and Audi predictive active suspension;novel concepts that could become alternative candidates are also introduced,including series active variable geometry suspension,and the active wheel-alignment system.ASSs with compact structure,small mass increment,low power consumption,high-frequency response,acceptable economic costs,and high reliability are more likely to be adopted by car manufacturers.In terms of control strategies,the development of future ASSs aims not only to stabilize the chassis attitude and attenuate the chassis vibration,but also to enable ASSs to cooperate with other modules(e.g.,steering and braking)and sensors(e.g.,cameras)within a car,and even with high-level decision-making(e.g.,reference driving speed)in the overall transportation system-strategies that will be compatible with the rapidly developing electric and autonomous vehicles.

A digital twins enabled underwater intelligent internet vehicle path planning system via reinforcement learning and edge computing

The Autonomous Underwater Glider(AUG)is a kind of prevailing underwater intelligent internet vehicle and occupies a dominant position in industrial applications,in which path planning is an essential problem.Due to the complexity and variability of the ocean,accurate environment modeling and flexible path planning algorithms are pivotal challenges.The traditional models mainly utilize mathematical functions,which are not complete and reliable.Most existing path planning algorithms depend on the environment and lack flexibility.To overcome these challenges,we propose a path planning system for underwater intelligent internet vehicles.It applies digital twins and sensor data to map the real ocean environment to a virtual digital space,which provides a comprehensive and reliable environment for path simulation.We design a value-based reinforcement learning path planning algorithm and explore the optimal network structure parameters.The path simulation is controlled by a closed-loop model integrated into the terminal vehicle through edge computing.The integration of state input enriches the learning of neural networks and helps to improve generalization and flexibility.The task-related reward function promotes the rapid convergence of the training.The experimental results prove that our reinforcement learning based path planning algorithm has great flexibility and can effectively adapt to a variety of different ocean conditions.

Efficient and Cost-Effective Vehicle Detection in Foggy Weather for Edge/Fog-Enabled Traffic Surveillance and Collision Avoidance Systems

Vision-based vehicle detection in adverse weather conditions such as fog,haze,and mist is a challenging research area in the fields of autonomous vehicles,collision avoidance,and Internet of Things(IoT)-enabled edge/fog computing traffic surveillance and monitoring systems.Efficient and cost-effective vehicle detection at high accuracy and speed in foggy weather is essential to avoiding road traffic collisions in real-time.To evaluate vision-based vehicle detection performance in foggy weather conditions,state-of-the-art Vehicle Detection in Adverse Weather Nature(DAWN)and Foggy Driving(FD)datasets are self-annotated using the YOLO LABEL tool and customized to four vehicle detection classes:cars,buses,motorcycles,and trucks.The state-of-the-art single-stage deep learning algorithms YOLO-V5,and YOLO-V8 are considered for the task of vehicle detection.Furthermore,YOLO-V5s is enhanced by introducing attention modules Convolutional Block Attention Module(CBAM),Normalized-based Attention Module(NAM),and Simple Attention Module(SimAM)after the SPPF module as well as YOLO-V5l with BiFPN.Their vehicle detection accuracy parameters and running speed is validated on cloud(Google Colab)and edge(local)systems.The mAP50 score of YOLO-V5n is 72.60%,YOLOV5s is 75.20%,YOLO-V5m is 73.40%,and YOLO-V5l is 77.30%;and YOLO-V8n is 60.20%,YOLO-V8s is 73.50%,YOLO-V8m is 73.80%,and YOLO-V8l is 72.60%on DAWN dataset.The mAP50 score of YOLO-V5n is 43.90%,YOLO-V5s is 40.10%,YOLO-V5m is 49.70%,and YOLO-V5l is 57.30%;and YOLO-V8n is 41.60%,YOLO-V8s is 46.90%,YOLO-V8m is 42.90%,and YOLO-V8l is 44.80%on FD dataset.The vehicle detection speed of YOLOV5n is 59 Frame Per Seconds(FPS),YOLO-V5s is 47 FPS,YOLO-V5m is 38 FPS,and YOLO-V5l is 30 FPS;and YOLO-V8n is 185 FPS,YOLO-V8s is 109 FPS,YOLO-V8m is 72 FPS,and YOLO-V8l is 63 FPS on DAWN dataset.The vehicle detection speed of YOLO-V5n is 26 FPS,YOLO-V5s is 24 FPS,YOLO-V5m is 22 FPS,and YOLO-V5l is 17 FPS;and YOLO-V8n is 313 FPS,YOLO-V8s is 182 FPS,YOLO-V8m is 99 FPS,and YOLO-V8l is 60 FPS on FD dataset.YOLO-V5s,YOLO-V5s variants and YOLO-V5l_BiFPN,and YOLO-V8 algorithms are efficient and cost-effective solution for real-time vision-based vehicle detection in foggy weather.

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

A Fault Detection Method for Electric Vehicle Battery System Based on Bayesian Optimization SVDD Considering a Few Faulty Samples

Accurate and reliable fault detection is essential for the safe operation of electric vehicles.Support vector data description(SVDD)has been widely used in the field of fault detection.However,constructing the hypersphere boundary only describes the distribution of unlabeled samples,while the distribution of faulty samples cannot be effectively described and easilymisses detecting faulty data due to the imbalance of sample distribution.Meanwhile,selecting parameters is critical to the detection performance,and empirical parameterization is generally timeconsuming and laborious and may not result in finding the optimal parameters.Therefore,this paper proposes a semi-supervised data-driven method based on which the SVDD algorithm is improved and achieves excellent fault detection performance.By incorporating faulty samples into the underlying SVDD model,training deals better with the problem of missing detection of faulty samples caused by the imbalance in the distribution of abnormal samples,and the hypersphere boundary ismodified to classify the samplesmore accurately.The Bayesian Optimization NSVDD(BO-NSVDD)model was constructed to quickly and accurately optimize hyperparameter combinations.In the experiments,electric vehicle operation data with four common fault types are used to evaluate the performance with other five models,and the results show that the BO-NSVDD model presents superior detection performance for each type of fault data,especially in the imperceptible early and minor faults,which has seen very obvious advantages.Finally,the strong robustness of the proposed method is verified by adding different intensities of noise in the dataset.

Research and design of an expert diagnosis system for rail vehicle driven by data mechanism models

Purpose-The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.Design/methodology/approach-The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle.Based on data mechanism models,it predicts the lifespan of key components,evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.Findings-The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system,which helps operators to monitor the operation of vehicle online,predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.Originality/value-This system improves the efficiency of rail vehicle operation,scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.

Stochastic sampled-data multi-objective control of active suspension systems for in-wheel motor driven electric vehicles

This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets.For this purpose,a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities.Then,an asynchronous fuzzy sampled-data controller,featuring distinct premise variables from the active suspension system,is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership.Furthermore,novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous𝐻2 and𝐻∞performance requirements.Finally,the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.

Design and Analysis of Power and Transmission System of Downhole Pure Electric Command Vehicle

Electric vehicles use electric motors, which turn electrical energy into mechanical energy. As electric motors are conventionally used in all the industry, it is an established development site. It’s a mature technology with ideal power and torque curves for vehicular operation. Conventional vehicles use oil and gas as fuel or energy storage. Although they also have an excellent economic impact, the continuous use of oil and gas threatened the world’s reservation of total oil and gas. Also, they emit carbon dioxide and some toxic ingredients through the vehicle’s tailpipe, which causes the greenhouse effect and seriously impacts the environment. So, as an alternative, electric car refers to a green technology of decarbonization with zero emission of greenhouse gases through the tailpipe. So, they can remove the problem of greenhouse gas emissions and solve the world’s remaining non-renewable energy storage problem. Pure electric vehicles (PEV) can be applied in all spheres, but their special implementation can only be seen in downhole operations. They are used for low noise and less pollution in the downhole process. In this study, the basic structure of the pure electric command vehicle is studied, the main components of the command vehicle power system, namely the selection of the drive motor and the power battery, are analyzed, and the main parameters of the drive motor and the power battery are designed and calculated. The checking calculation results show that the power and transmission system developed in this paper meets the design requirements, and the design scheme is feasible and reasonable.

Intelligent Vehicle Auxiliary Handling System Based on the Internet of Things Technology

This paper proposes an intelligent vehicle auxiliary handling system based on Internet of Things(IoT)technology,featuring an innovative holding mechanism design that adjusts to the length and width of various vehicles.The system combines precise positioning using satellite tracking technology,intelligent recognition via OpenCV,and the interconnectivity of IoT.This intelligent vehicle auxiliary handling system can independently identify vehicle positions and plan optimal handling paths,eliminating the traditional reliance on manual operation.It offers efficient and accurate handling,setting a new trend in the handling industry.Additionally,the system integrates seamlessly with parking lot management systems,providing real-time updates on vehicle locations and statuses.This allows managers to monitor the parking lot operations clearly and efficiently.This intelligent coordination greatly enhances overall work efficiency and streamlines parking management.Overall,the innovative design of the intelligent vehicle auxiliary handling system represents a significant breakthrough in both function and performance,gaining user favor with its smooth operation.Looking ahead,continued technological advancements and the expansion of application fields will bring even more vitality and intelligence to societal development.

Deep Learning-Based Control System Design for Emergency Vehicles through Intersections

This paper addresses the challenge of integrating priority passage for emergency vehicles with optimal intersection control in modern urban traffic. It proposes an innovative strategy based on deep learning to enable emergency vehicles to pass through intersections efficiently and safely. The research aims to develop a deep learning model that utilizes intersection violation monitoring cameras to identify emergency vehicles in real time. This system adjusts traffic signals to ensure the rapid passage of emergency vehicles while simultaneously optimizing the overall efficiency of the traffic system. In this study, OpenCV is used in combination with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to jointly complete complex image processing and analysis tasks, to realize the purpose of fast travel of emergency vehicles. At the end of this study, the principle of the You Only Look Once (YOLO) algorithm can be used to design a website and a mobile phone application (app) to enable private vehicles with emergency needs to realize emergency passage through the application, which is also of great significance to improve the overall level of urban traffic management, reduce traffic congestion and promote the development of related technologies.

Data synchronization in IMU/GPS integrated measurement system of vehicle motion parameters

To realize the data synchronization between the inertial measurement unit （IMU） and the global positioning system （GPS）, the synchronization technology in the IMU/GPS integrated measurement system of vehicle motion parameters is studied. According to the characteristics of the output signals of the IMU and the GPS, without the IMU synchronization signal, the synchronization circuit based on CPLD is designed and developed, which need not alter the configurations of the IMU and GPS. Experiments of measuring vehicle motion parameters, which rely on the synchronization circuit to realize IMU/GPS data synchronization, are made. The driving routes in experiments comprise a curve and a straight line. Experimental results show that the designed circuit can accurately measure the synchronization time difference and the IMU period, and can effectively solve the data synchronization in IMU/GPS integration. Furthermore, the IMU/GPS integrated measurement system based on the synchronization circuit can measure and calculate many vehicle motion parameters in high frequency mode.

Battery Management System with State ofCharge Indicator for Electric Vehicles

Aim To research and develop a battery management system(BMS)with the state of charge(SOC)indicator for electric vehicles (EVs).Methods On the basis of analyzing the electro-chemical characteristics of lead-acid. battery, the state of charge indicator for lead-acid battery was developed by means of an algorithm based on combination of ampere-hour, Peukert's equation and open-voltage method with the compensation of temperature,aging,self- discharging,etc..Results The BMS based on this method can attain an accurate surplus capa- city whose error is less than 5% in static experiments.It is proved by experiments that the BMS is reliable and can give the driver an accurate surplus capacity,precisely monitor the individual battery modules as the same time,even detect and warn the problems early,and so on. Conclusion A BMS can make the energy of the storage batteries used efficiently, develop the batteries cycle life,and increase the driving distance of EVs.

Design of a road vehicle detection system based on monocular vision

In order to decrease vehicle crashes, a new rear view vehicle detection system based on monocular vision is designed. First, a small and flexible hardware platform based on a DM642 digtal signal processor （DSP） micro-controller is built. Then, a two-step vehicle detection algorithm is proposed. In the first step, a fast vehicle edge and symmetry fusion algorithm is used and a low threshold is set so that all the possible vehicles have a nearly 100% detection rate （TP） and the non-vehicles have a high false detection rate （FP）, i. e., all the possible vehicles can be obtained. In the second step, a classifier using a probabilistic neural network （PNN） which is based on multiple scales and an orientation Gabor feature is trained to classify the possible vehicles and eliminate the false detected vehicles from the candidate vehicles generated in the first step. Experimental results demonstrate that the proposed system maintains a high detection rate and a low false detection rate under different road, weather and lighting conditions.