Smart Car Research Articles

Design and Application of Fuzzy PID Controller Based on MATLAB

In an era where the development of intelligent technology is a pivotal force propelling economic advancement, enhancing production efficiency, diminishing costs, and reshaping daily life, the field of intelligent vehicle control technology has garnered significant attention among automotive researchers. The dynamics of smart cars during driving often exhibit nonlinearity and are susceptible to external environmental disturbances, such as wind resistance and road surface inequality. Traditional PID controllers have limited ability to suppress these interferences, so this study uses fuzzy PID algorithms to solve the inherent challenges in intelligent vehicle control. Through comprehensive analysis of the current landscape and technological advancements in intelligent vehicle control systems, coupled with rigorous MATLAB/Simulink simulations and real-world testing, the study showed that the overshoot of the adaptive fuzzy PID was reduced by 86% compared to the traditional PID. Consequently, it markedly enhances the precision and stability of speed control in intelligent vehicles, providing a groundbreaking perspective and practical methods for the enhancement of intelligent vehicle control systems with considerable application potential.

A Novel Light-Weight Machine Learning Classifier for Intrusion Detection in Controller Area Network in Smart Cars

The automotive industry has evolved enormously in recent years, marked by the proliferation of smart vehicles furnished with avant-garde technologies. These intelligent automobiles leverage cutting-edge innovations to deliver enhanced connectivity, automation, and convenience to drivers and passengers. Despite the myriad benefits of smart vehicles, their integration of digital systems has raised concerns regarding cybersecurity vulnerabilities. The primary components of smart cars within smart vehicles encompass in-vehicle communication and intricate computation, in addition to conventional control circuitry. In-vehicle communication is facilitated through a controller area network (CAN), whereby electronic control units communicate via message transmission across the CAN-bus, omitting explicit destination specifications. This broadcasting and non-delineating nature of CAN makes it susceptible to cyber attacks and intrusions, posing high-security risks to the passengers, ultimately prompting the requirement of an intrusion detection system (IDS) accepted for a wide range of cyber-attacks in CAN. To this end, this paper proposed a novel machine learning (ML)-based scheme employing a Pythagorean distance-based algorithm for IDS. This paper employs six real-time collected CAN datasets while studying several cyber attacks to simulate the IDS. The resilience of the proposed scheme is evaluated while comparing the results with the existing ML-based IDS schemes. The simulation results showed that the proposed scheme outperformed the existing studies and achieved 99.92% accuracy and 0.999 F1-score. The precision of the proposed scheme is 99.9%, while the area under the curve (AUC) is 0.9997. Additionally, the computational complexity of the proposed scheme is very low compared to the existing schemes, making it more suitable for the fast decision-making required for smart vehicles.

MEMS Precision Sensors Based on Process Innovation Technology

This article introduces a novel process for achieving micro-scale thermal isolation through solid porous 3D microstructures. It proposes a method to improve existing thermal MEMS devices by utilizing thin-film membrane structures, develops a model for estimating the thermal conductivity of porous microstructures, and constructs porous 3D microstructures made of three different powder materials, studying their applicability for thermal isolation. Breakthroughs have been achieved in process preparation technology, enabling the production of precision MEMS sensors. These sensors are expected to find wide applications in smart cars, intelligent healthcare, electronic resonance sensors, and other fields.

Printed-scalable microstructure BaTiO3/ecoflex nanocomposite for high-performance triboelectric nanogenerators and self-powered human-machine interaction

Triboelectric sensing technology reinforced with deep learning algorithm has shown significant potentials in the fields of sophisticated wearable devices and Internet of Things (IoT) applications. Herein, we present a large-scale and printable nanocomposite film for high-performance microstructured BaTiO3/Ecoflex triboelectric nanogenerator (MBE-TENG) and demonstrate its applications in deep-learning assisted self-powered sensing and human-machine interaction. The MBE-TENG device achieves a short-circuit current of 1.46 μA, an open-circuit voltage of 144 V, and a transfer charge density of 6.62 nC/cm². Additionally, the MBE-TENG device can charge a 10 μF capacitor from 0 to 2 V within 200 seconds, illuminate over 70 commercial LEDs, and power a digital calculator. Based on the MBE-TENG, a sensory array keyboard is demonstrated to wirelessly control the movement of a smart car via Bluetooth communication. Furthermore, a smart tactile sensing glove is developed by combining the MEB-TENG, residual neural network and convolutional block attention module, realizing the facile recognition on six different objects with a high accuracy of 96.33 %. This work presents a scalable fabrication for microstructured triboelectric layer and highlights the versatility and efficiency of TENG sensing technology when combined with advanced neural networks and embedded systems, paving the way for innovative development in human-machine interaction, personalized healthcare, and intelligent control interfaces.

Smart Car Parking System based on UWB Tracking

Smart Car Parking System based on UWB Tracking

Securing blockchain-enabled smart health care image encryption framework using Tinkerbell Map

IoT enables the emergence and implementation of smart devices to address real-world problems and challenges. Today we are surrounded by various smart devices, such as smart phones, smart homes, smart cars, smart televisions, and smartwatches that assist us in making our lives easier and smoother. IoT is a conglomeration of multiple technologies at various layers to impart the best of ubiquitous and pervasive computing to deliver several benefits in a variety of application sectors such as medicine, agriculture, and industry. In an IoT setting, blockchain technology is used for addressing security challenges and eradicating third-party participation. Utilizing public networks for storing or transmitting health care images poses risks of eavesdropping, data breaches, and unauthorized access. Before uploading medical data to the decentralized network, encryption is required to prevent unauthorized access. The aim of this study is to integrate technologies to ensure transactions are conducted safely and securely. We proposed an IoT-Blockchain system based on chaos encryption scheme using Tinkerbell mapping to ensure medical data integrity and authenticity. The suggested approach is examined to assess performance parameters such as, key space analysis, key sensitivity analysis, Information Entropy (IE), histogram, correlation of adjacent pixels, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM). These findings demonstrated that the suggested method is extremely efficient in avoiding security breaches and guaranteeing information integrity.

Smart Cars Parking Systems of Big Cities based on the Internet of Things

Smart Cars Parking Systems of Big Cities based on the Internet of Things

Smart Car Parking System

In this fast-growing economy, the number of vehicle users is growing tremendously, wasting a significant amount of time and fuel while searching for an available parking space. This work highlights the optimum utilization of parking slots without the physical intervention of humans by making use of an IoT-integrated embedded system. This study aimed to create a mobile application for car parking that uses NodeMCU as the Microcontroller, which is further integrated with Radio-Frequency Identification (RFID) and the Internet of Things (IoT) to detect available parking slots, saving people’s time. When combined with an interactive mobile application, this technology provides an efficient solution by highlighting the availability and occupancy of parking spaces as green and red, respectively. It contributes to enhancing the quality of life of people living in congested metropolitan cities by digitizing the conventional parking system.

Influence of dysprosium addition on corrosion behavior of NdFeB magnets

PurposeThis paper aims to investigate the corrosion kinetics and corrosion behavior of NdFeB magnets with the addition of heavy rare earth dysprosium (Dy) for its inhibitory activity on poor corrosion resistance of NdFeB magnets.Design/methodology/approachTo study the effect of dysprosium addition on corrosion behavior of NdFeB magnets and investigate its mechanism, potentiodynamic polarization, scanning electron microscopy (SEM), electrochemical impedance, energy dispersion spectrum (EDS) and scanning Kelvin probe force microscopy (SKPFM) were applied in the research. Besides, microstructures were observed by SEM equipped with EDS. Atomic force microscopy was introduced to analyze the morphology, potential image as well as the contact potential difference. The SKPFM mapping scan was applied to obtain the contact potential around Nd-rich phase at 0.1 Hz. The magnets were detected via X-ray diffraction.FindingsSubstitution of Nd with Dy led to improvement of corrosion resistance and reduced the potential difference between matrix and Nd-rich phase. Corrosion resistance is Nd-rich phase < the void < metal matrix; maximum potential difference between matrix and Nd-rich phase of Dy = 0, Dy = 3 and Dy = 6 Wt.% is 411.3, 279.4 and 255.8 mV, respectively. The corrosion rate of NdFeB magnet with 6 Wt.% Dy is about 67% of that without Dy at steady corrosion stage. The addition of Dy markedly enhanced the corrosion resistance of NdFeB magnets.Originality/valueThis research innovatively investigates the effect of adding heavy rare earth Dy to NdFeB permanent magnets on magnetic properties, as well as their effects on microstructure, phase structure and most importantly on corrosion resistance. Most scholars are studying the effect of element addition on magnetic properties but not on corrosion resistance. This paper creatively fills this research gap. NdFeB magnets are applied in smart cars, robotics, AI intelligence, etc. The in-depth research on corrosion resistance by adding heavy rare earths has made significant and outstanding contributions to promoting the rapid development of the rare earth industry.

Comparative analysis of different spacing policies for longitudinal control in vehicle platooning

Most existing automated driving vehicles in the platoon equipped with an Adaptive Cruise Control (ACC) systems and a Cooperative Adaptive Cruise Control (CACC) systems have mainly focused on enhancing safety, improving traffic efficiency problems, and reducing workload. The spacing strategy is the core of all platoon designs, and the performance of an ACC/CACC systems hinges on the select of the spacing strategy. Although, in the literature, there are many papers dealing with platoon control, detailed explanations of the operating mechanisms of two types of spacing policies including the Headway Spacing Strategy (CTHS), and Constant Spacing Strategy (CSS), and comparative studies on them are still lacking. This work presents the studies of the longitudinal control strategy of a platoon of vehicles equipped with the existing ACC systems and CACC systems under two different spacing policies to evaluate the performances. The contributions in this work are carefully reviewed and the operating mechanisms and characteristics of two different spacing policies: the CTHS and CSS, the general evaluation criteria for spacing strategies are provided and their advantages and disadvantages are based on the numerical results. Both numerical simulations and experiments with a platoon of smart cars in real-time have demonstrated the effectiveness and practicability of the presented methodology.

Online LiDAR-camera extrinsic parameters self-checking and recalibration

Abstract During production, smart cars are equipped with calibrated LiDARs and cameras. However, due to the vibrations that inevitably occur during driving, the sensors’ extrinsic parameters may change slightly over time. It is a significant challenge to ensure the ongoing security of these systems throughout the car’s lifetime. To address this issue, we propose a self-checking and recalibration algorithm that can continuously detect the sensor data of intelligent vehicles. If the sensor’s miscalibration is detected, the data can be repaired promptly to ensure the vehicle’s reliability. Our self-checking algorithm extracts features from the point cloud and image and performs pixel-wise comparisons. To improve feature quality, we utilize the patch-wise transformer to enhance local information exchange, which also benefits the subsequent extrinsic recalibration. To facilitate the study, we generate two customized datasets from the KITTI dataset and the Waymo Open Dataset. The experiments conducted on these datasets demonstrate the effectiveness of our proposed method in accurately calibrating the LiDAR and camera systems throughout the car’s lifetime. This study is the first to highlight the importance of continually checking the calibrated extrinsic parameters for autonomous driving. Our findings contribute to the broader goal of improving safety and reliability in autonomous driving systems. The dataset and code are available at https://github.com/OpenCalib/LiDAR2camera_self-check.

Extremely Stable, Multidirectional, All‐in‐One Piezoelectric Bending Sensor with Cycle up to Million Level

AbstractFlexible bending sensors are crucial components of flexible/wearable electronics. However, their broad application is restricted by their difficulty in sensing different bending directions and, more importantly, their significant output attenuation after long cycles. Herein, a new design of an All‐in‐One piezoelectric bending sensor is proposed, in which double‐deck, cross‐over 3D interdigital microelectrodes (silver nanowires (Ag NWs)) are embedded in a piezoelectric polymer film (poly(vinylidene fluoride‐trifluoroethylene (P(VDF‐TrFE))). The piezoelectric film with the embedded 3D microelectrodes exhibits a high anisotropy coefficient ( &gt; 1) based on dual piezoelectric modes (d31 and d33), which renders it more sensitive to different bending directions. More importantly, the homogeneous interconnected interface and heterogeneous microinterlocked interface formed inside the sensor significantly enhance its interface mechanical properties, with at least a tensile strength of 51 MPa, a shear strength of 28 MPa, and an interfacial toughness of 300 J m−2, which are nearly two orders of magnitude greater than those of conventional sandwich architectures. The prepared All‐in‐One sensor shows an extremely stable piezoelectric output over as many as 16 00 000 bending cycles, which represents a remarkable breakthrough. Furthermore, a single sensor can be used to remotely control the multidirectional motion of a smart car, demonstrating enormous potential in practical applications.

Advancing automotive high-performance computing: integrating direction API with LORA technology in unified machine vision for future smart cars

Advancing automotive high-performance computing: integrating direction API with LORA technology in unified machine vision for future smart cars

Prototype Design Remote Smart Car 4WD Robot Car with Additional Power Photovoltaic Source Integrated Arduino

The problem addressed in this research is designing an Electric Car Prototype, a technology design using Arduino Uno, L298N Driver Module, and HC-06 Bluetooth sensor which is controlled automatically using an application. The method used is a hybrid PLTS (Solar Power Plant) system based on Arduino Uno. This prototype combines electrical energy from batteries with solar energy produced by solar panels, and the main controller is Arduino Uno. Tests were carried out to evaluate the performance and efficiency of this electric car. The results of solar panel experiments in designing electric car prototypes, testing will be carried out on the efficiency of solar panels on the power produced. The following is the data from the experimental results measured through the following table 2 experiments. solar panels were installed on the roof or other parts exposed to sunlight on a prototype electric car. Solar panels consist of photovoltaic solar cells which are capable of converting solar energy into electrical energy. When sunlight falls on a solar panel, the solar cell produces an electric current. The electric current generated from the solar cell is then channeled to the charging module to charge the battery. The battery charging module regulates the voltage and electric current according to battery charging needs. This is important so that the battery does not receive too much or too little power during the charging process. After passing through the battery charging module, the electrical energy from the solar panels is used to charge the electric car battery. Electric car batteries function as energy storage which is used to control the engine and other electronic systems in the car. Obtained were the development of an environmentally friendly and sustainable electric vehicle.

Efficient Management of Containers for Software Defined Vehicles

Containerization technology, such as Docker, is gaining in popularity in newly established software-defined vehicle architectures (SDVA). However, executing those containers can quickly become computationally expensive in constrained environments, given the limited CPU, memory, and energy resources in the Electric Control Units (ECU) of SDVA. Consequently, the efficient management of these containers is crucial for enabling the on-demand usage of the applications in the vehicle based on the available resources while considering several constraints and priorities, including failure tolerance, security, safety, and comfort. In this paper, we propose a dynamic software container management approach for constrained environments such as embedded devices/ECUs in SDVA within smart cars. To address the conflicting objectives and constraints within the vehicle, we design a novel search-based approach based on multi-objective optimization. This approach facilitates the allocation, movement, or suspension of containers between ECUs in the cluster. Collaborating with our industry partner, Ford Motor Company, we evaluate our approach using different real-world software-defined scenarios. These scenarios involve using heterogeneous clusters of ECU devices in vehicles based on real-world software containers and use-case studies from the automotive industry. The experimental results demonstrate that our scheduler outperforms existing scheduling algorithms, including the default Docker scheduler -Spread- commonly used in automotive applications. Our proposed scheduler exhibits superior performance in terms of energy and resource cost efficiency. Specifically, it achieves a 35% reduction in energy consumption in power-saving mode compared to the scheduler employed by Ford Motor Company. Additionally, our scheduler effectively distributes workload among the ECUs in the cluster, minimizing resource usage, and dynamically adjusts to the real-time requirements and constraints of the car environment. This work will serve as a fundamental building block in the automotive industry to efficiently manage software containers in smart vehicles considering constraints and priorities in the real world.

Thoughts on Smart Cars and Autonomous Consciousness [Editor’s Column]

Thoughts on Smart Cars and Autonomous Consciousness [Editor’s Column]

상황인식 기반 AR-HUD(헤드업 디스플레이) 인터페이스 디자인에 관한 연구

With the development of intelligentization of the vehicle industry, cars are no longer just means of transportation, and various sensors and virtual entertainment systems have begun to be applied for autonomous vehicles and smart cars. Rise of in-vehicle information required by the new intelligent interaction method for information processing theory, this study has see car - human interaction interface based on AR technology introducing, driving experience and can improve the driving safety is studied interface design. To this end, literature research, case comparative analysis, and surveys were conducted, and comparative analysis was conducted on Tesla HUD and Huawei AR-HUD. In addition, through in-depth investigation of contextual user demands, it analyzed existing problems and proposed optimization of AR-HUD interface design based on situational awareness from three aspects of function, interaction and vision. Study driver's driving experience, improve driving stability, AR - propulsion, smart and personalization of HUD interface design intelligent vehicles and vehicles of the self-driving car and future improvement and theoretical foundation for design of an interface by providing drivers contributing to meet demand.

Smart Cities towards Artificial Intelligence

The creation of smart cities has come a long way in recent years, especially in domains that use AI, such IoT-based components, security systems, smart living options, and environmental monitoring. These advancements seek to build surroundings that meet human requirements by utilizing cutting edge technologies such as automated parking, smart healthcare, and seismic monitoring systems. Even with these encouraging advancements, the current generation of smart cities is still unable to attain genuine autonomy, particularly when it comes to component-level autonomous situation management and decision-making. The transformational perspective proposed in this work views the constituent components of smart environments as self-governing agents with the ability to make decisions. The purpose of these autonomous agents is to improve security and smart living by being able to self-manage and react automatically to different situations. Such autonomous agents can be integrated to accomplish the goal of artificially intelligent smart cities. In smart cities, autonomous agents may behave pro-actively without human involvement by continually monitoring and analyzing data, anticipating possible problems, and taking proactive actions. In the context of smart healthcare, these agents may, for example, track a patient's vitals in real time, anticipate possible health emergencies, notify medical personnel, or even start emergency procedures. Autonomous agents may dynamically assign parking spots in the context of smart car parking based on real-time data, maximizing space use and minimizing traffic congestion. Agents that can autonomously manage emergency actions, such as directing people to safety and managing infrastructure to minimize damage, might be beneficial for earthquake detecting systems. As autonomous entities endowed with decision-making skills are included, we are moving closer to truly intelligent cities. Because of these agents' capacity for independent operation and quick response to changing circumstances, smart cities are able to offer a better quality of security and service while accommodating the changing demands of its residents. As a result, smart cities that have these intelligent agents installed will be more responsive, efficient, and resilient—ultimately capturing the spirit of artificial intelligence in urban planning.

Temperature Call Alert to Prevent Child Death from Heatstroke in Smart Car using IoT

According to a study conducted by the National Health Service (NHS), 937 children died of heatstroke in the United States between 1998 and January 2023. Shockingly, out of these 937 deaths, 493 occurred due to parents forgetting to take their children out of cars. To prevent such tragic incidents from occurring, this paper proposes a simple system, "Temperature Call Alert”. The system operates by utilizing multiple sensors to collect and analyze data regarding the temperature inside a car. This data is then sent to a cloud platform for storage and processing. If the system detects a higher- than-normal temperature inside the car, it automatically sends an alert call to the parent's mobile number with a message. This alert call can help the parent to take necessary actions to prevent their child's life from being lost due to heatstroke. To implement the temperature call alert system, we used an IFTTT webhook with an IoT cloud. This allows us to give a call alert to the authorized mobile number whenever the system detects a potentially dangerous situation. With the help of this system, we can reduce the risk of heatstroke deaths caused by parents forgetting their children in cars.

IoT-Enabled Smart Car Parking Management: Revolutionizing Urban Traffic Flow

IoT-Enabled Smart Car Parking Management: Revolutionizing Urban Traffic Flow