Automobile Technology Research Articles

Review of PID Control in Industrial Areas for Tobacco Production, Self-driving Car Technology and Robotic Arm Robots

Abstract. PID control is widely employed across diverse industries such as tobacco production, autonomous car technology, and mechanical arm robotics. This shows that PID control has a substantial developmental potential, so I choose the PID control as the topic. Since few people summarize its different applications in different industries, this study has the motivation to comprehensively summarize the applications of PID. This research provides an in-depth overview and analysis of PID control's varied applications in tobacco production, autonomous car technology, and mechanical arm robotics. It focuses on addressing practical challenges and exploring optimization strategies encountered in these fields. This article is summarized using the methods of the review. PID plays a crucial role in enhancing stability, precision, and response speed in these applications, thereby improving overall control system dynamic response performance. This study consolidated existing literature to offer insights into PID's effectiveness across these industrial sectors, shedding light on its impact and potential for further advancements in control technology.

Error and Traffic State Analysis of Floating Car Data

Abstract. Floating car technology is widely applied in traffic information collection and has become a common method for real-time and dynamic monitoring of urban traffic conditions. Using floating car data from Shanghai as the research object, this paper analyzes the positioning error of floating cars and finds that most errors are within 15 meters. The error is significantly affected by vehicle speed but less by time, and the error decreases as the road grade increases. Speed and travel time ratio are used as parameters to assess the spatiotemporal traffic state, exploring the spatial distribution and temporal evolution of speed, as well as the spatiotemporal distribution of traffic congestion. In the spatial distribution of speed, clustering analysis is used to deduce the temporal distribution pattern of speed on the same road segment. Based on this, corresponding traffic management measures can be applied to different road segments and at different times.

Selection of Solar-Based Water-Splitting Hydrogen Production and Hydrogen Storage Technologies Using Neutrosophic Analytic Hierarchy Process and Neutrosophic Complex Proportional Assessment

Abstract The production of hydrogen through water-splitting using solar energy has the potential to meet the world’s future energy demand while also addressing the issue of greenhouse gas emissions. To compare and select the best technology among solar thermochemical cycles, photoelectrochemical, and photocatalytic water-splitting in terms of solar-to-hydrogen efficiency, production cost, safety, life expectancy, maintenance cost, and detrimental impact on the environment, this study used a multi-criteria decision analysis hybrid called Neutrosophic Analytic Hierarchy Process (NAHP) and Neutrosophic Complex Proportional Assessment (NCOPRAS). The same method was used to determine the best technology for automobiles among compressed hydrogen storage, metal hydrides, metal-organic frameworks, and chemical storage in terms of gravimetric system capacity, volumetric system capacity, safety, system cost, cycle life, energy efficiency, detrimental impact on the environment, and refueling time. There were 4 experts each in hydrogen storage and hydrogen, specifically from the Philippines, Australia, India, Romania, and Italy. All experts’ judgments have a consistency ratio (CR) < 0.1. The priority criterion for hydrogen production was life expectancy (weighted value (WV) = 0.189), and gravimetric system capacity for hydrogen storage (WV = 0.137). The result showed that among all alternatives for hydrogen production, the solar thermochemical cycle was the best (relative significance value (RSV) = 0.3432) as for hydrogen storage, the best technology among the alternatives was metal hydrides (RSV = 0.2575). A sensitivity analysis was used to check the variability and robustness of the solution.

SCVAN- BKM: Swarm clustering for vehicular ad hoc network with a secure blockchain-based key management scheme

The advancements in telematics and communication technology for automobiles have contributed to the development of Vehicular ad Hoc Networks (VANETs). These networks serve as the foundation for Intelligent Transportation Systems (ITS). Communication across various VANET entities is challenging due to the open communication environment and the highly dynamic nature of VANETs. One of the major challenges in VANETs is ensuring stable and secure transmission. To address these factors, several studies have been conducted. The article discusses a clustering algorithm for VANETs that leverages SCVAN-DPSO to create stable clusters and provide secure communication for mitigating malicious nodes. Additionally, Hyperelliptic Curve Cryptography (HECC) with signcryption in blockchain is proposed. This algorithm is designed to identify and mitigate malicious nodes, ensuring robust and secure communication within the network. To verify vehicles in VANETs, this study also suggests a safe and reliable key management method that generates and stores cryptographic keys using blockchain’s distributed ledger technology. By doing so, the risk of rogue nodes is reduced, and a safe, stable connection between vehicles is guaranteed. This work has been implemented using NS 3.34 simulation with the SUMO real-time traffic mobility simulator, and its performance has been evaluated. The proposed algorithm SCVAN-BKM provides a 99.6 % packet delivery ratio for 20 nodes, 0.4 % packet loss, end-to-end delay of 32.65 ms, throughput of 9.522 Mbps, and a cluster lifetime of 105 s. As the number of nodes increases in the communication zone, stable communication is maintained due to the clustering algorithm used in the proposed approach, as cluster formation supports stability in the high-mobility nature of vehicles, while secure communication is ensured using blockchain technology. The findings demonstrate that our approach is more effective and yields better outcomes than existing systems.

Overview of Fuel Cell-Hybrid Power Sources Vehicle Technology: A Review

Today, the world suffers from excessive and unfair consumption of non-renewable energy sources, such as high rate of global pollution and global warming. Accordingly, fields of life in general and industry in particular, led by the automobile industry, tended to use clean and renewable energy in industry and consumption to reduce the negative impacts on the global environment. The automotive industry tended to produce electric cars that do not depend at all on traditional energy sources from fossil fuel derivatives. Accordingly, it was necessary to find alternative energy sources that achieve both goals: avoiding the negative impact on the envi-ronment, and producing sufficient energy to achieve the requirements of performance and efficiency from the use of electric cars to be a permanent alternative to traditional cars that run on fossil fuels. This scientific paper provides an overview of one of the versions of modern technology in the field of electric vehicles energy performance to provide the vehicle with energy continuously and the mechanism of control and management in this system. This paper studies the hybrid power sources technology in electric and hybrid cars that depend on a main power source, Fuel Cells (FC), and a secondary power sources, Battery and Ultracapacitor, in the vehicle. This paper presents a brief overview of this system, its components, their characteristics, the advantages of hybridization in this type of energy source with the working mechanism of the system, an overview of the control systems in this technology and a set of challenges facing this type technology and its future perspectives.

Vehicle Autonomous Driving System

Autonomous vehicles or self-driving cars industry and technology have played an important role in research industry and automotive industry. Autonomous cars are those vehicles have the ability to sense there surrounding and navigate and drive themselves on roads through traffic without human interventions. In other words, they can move from one location to another one without human interaction. In this paper, an autonomous vehicle system prototype is proposed. The vehicle is able to sense its surrounding and keep going on the road on its own through traffic and other barriers such as people and traffic lights. That is, the vehicles are able to drive and detect the road signals and take decision accordingly, whether to continue or to make a turn. The proposed system uses raspberry pi microcontroller and ultrasonic sensors to detect any object, obstacle, or pedestrians in front of the vehicle and to measure the distance. In addition, a raspberry pi camera is connected to the raspberry pi to continuously take pictures of the road. These pictures will be analyzed by the raspberry pi microcontroller. The vehicle is able to reach its destination safely. A self-driving car prototype has been designed and implemented. The porotype autonomous vehicle system was tested and performed as expected.

Employability Skill Development of Automobile Technology Students in a Work-Integrated Learning Environment: Barriers and Best Practices

Employability Skill Development of Automobile Technology Students in a Work-Integrated Learning Environment: Barriers and Best Practices

Coordination dynamics between fuel cell and battery technologies in the transition to clean cars

Significant progress reconciling economic activities with a stable climate requires radical and rapid technological change in multiple sectors. Here, we study the case of the automotive industry's transition to electric vehicles, which involved choosing between two different technologies: fuel cell electric vehicles (FCEVs) or battery electric vehicles (BEVs). We know very little about the role that such technological uncertainty plays in shaping the strategies of firms, the efficacy of technological and climate policies, and the speed of technological transitions. Here, we explain that the choice between these two technologies posed a global and multisectoral coordination game, due to technological complementarities and the global organization of the industry's markets and supply chains. We use data on patents, supply-chain relationships, and national policies to document historical trends and industry dynamics for these two technologies. While the industry initially focused on FCEVs, around 2008, the technological paradigm shifted to BEVs. National-level policies had a limited ability to coordinate global players around a type of clean car technology. Instead, exogenous innovation spillovers from outside the automotive sector played a critical role in solving this coordination game in favor of BEVs. Our results suggest that global and cross-sectoral technology policies may be needed to accelerate low-carbon technological change in other sectors, such as shipping or aviation. This enriches the existing theoretical paradigm, which ignores the scale of interdependencies between technologies and firms.

Development and Validation of Autotronic Training Module for Automobile Technology Students in Polytechnics in Southern Nigeria

Development and Validation of Autotronic Training Module for Automobile Technology Students in Polytechnics in Southern Nigeria

The safety effect of increased pedestrian protection, autonomous emergency braking for pedestrians and bicyclists on passenger cars, and speed management

This was the first retrospective study to estimate the effect of increased pedestrian protection, autonomous emergency braking, and speed management to reduce serious injuries among pedestrians and bicyclists. More specifically, the aim was to estimate the injury mitigating effects of the following interventions: AEB with pedestrian and bicyclist detection, Euro NCAP pedestrian test score, active bonnet, traffic calming at pedestrian and bicycle crossings, and additionally, the combined effect of the above-mentioned treatments. The main source of data was the Swedish traffic data acquisition system (Strada), where information of road traffic crashes between passenger cars and pedestrians or bicyclists for the period 1 January 2003–31 December 2022 was obtained. Cars with optional fitment of AEB systems were identified, and the license registration number was used to access individual car equipment lists to identify whether the vehicle was equipped with AEB with pedestrian and/or cyclist detection. Information about traffic calming at pedestrian and bicycle crossings was obtained from the Swedish Transport Administration. The injury metric used was risk of permanent medical impairment (RPMI) of at least one percent and ten percent. RPMI captures the risk of long-term medical impairment based on a diagnosed injury location and Abbreviated Injury Severity (AIS) score. The relative difference between the mean values of RPMI (mRPMI1%+ and mRPMI10%+) was calculated and tested using an independent two sample t-test which was conducted for unequal sample sizes and variance. Although many results were found to be statistically non-significant, the following results were found to be significant at least at 90% level. Pedestrian mRPMI10%+ was reduced by 44% in speed zones ≤ 50 km/h comparing the group struck by cars equipped with AEB with pedestrian detection compared to the group struck by cars without the system. For cyclists, the mRPMI10%+ was reduced by 35% in speed zones ≤ 50 km/h. For crashes within ± 20 meters from a pedestrian or bicycle crossing, the AEB system reduced 60% of pedestrians mRPMI10%+ at crossings with good safety standard compared to crossings of poor safety standard. The comparison of cars with poor performance (1–9 points) in the NCAP pedestrian test and cars with a high score (28–36 points) showed that pedestrian mRPMI10%+ was reduced by 48% across all speed limits, and by 64% including only those aged ≤ 64 years. For bicyclists, a significant reduction of cyclist mRPMI10%+ was found comparing low scoring cars to high scoring cars in ≤ 30 km/h speed limit (-73%) and across all speed limits (-36%). Including only those aged ≤ 64 years, the reduction was 49%. For the active bonnet, a significant reduction of mRPMI1%+ by 24% was observed but given that the rate of helmet wearing was higher in the group struck by cars with active bonnet, this difference cannot be attributed to an effect of an active bonnet. The STA safety rating of pedestrian and bicycle crossings showed that overall pedestrian mRPMI1%+ was reduced by 15%, while cyclists mRPMI10%+ was reduced by 32% comparing crossings of high safety level to crossings of poor safety level. The analysis of combined interventions showed that the total reduction of pedestrians and cyclists mRPMI10%+ together was 69%, from 6.4% to 2%. This paper demonstrates that a road environment with adapted infrastructure and speed, combined with passenger car technologies that improve the safety for vulnerable road users, can create significant reductions of serious (long-term) injuries among pedestrians and bicyclists.

Implications of Generative Al and Machine Learning on Automotive Industry Development & Reduction of Carbon Footprint: An Analysis of the U.S. Economy Perspective

The U.S. automotive industry is instrumental to the economy and contributes significantly to the American GDP, employment, and global trade. The principal objective of this research paper is to examine the implications of Generative AI and ML in advancing the automotive industry from the U.S. economic perspective. Generative AI is the latest frontier in artificial intelligence software development, where algorithmic generation can be achieved across various types of content: text, images, audio, and video. The generative AI in the Automotive market at the global level had witnessed boisterous growth and commanded a value of approximately $389.47 million by 2023. The analysis exposed that North American regions are dominating the market, which was attributed to high technological infrastructure, along with partnerships among automotive companies with research institutes and universities to foster AI innovations. Application analysis exposed that Advanced Driver Assistance Systems (ADAS) had the biggest market share, indicating a strong focus on developing and implementing Generative AI technologies to enhance driver safety and vehicle autonomy. Followed by, Connected Car Technologies, representing growing efforts towards implementing generative AI solutions that will improve vehicle connectivity, infotainment, and user experience. The impact of Generative AI and Machine Learning can be witnessed in terms of virtual prototyping, generative automotive designs, consolidation with the CAD system, supply chain optimization, Sensor Fusion and Perception Enhancement as well as automotive manufacturing process optimization.

IoT-Based Wireless Battery Surveillance and Management System

This research offers an Internet-of-Things (IoT) based battery surveillance machine for electric powered motors (EVs). The machine, is built using ESP32 microcontroller and Blynk IoT Cloud, to monitor the battery health in real-time. . The is equipped with sensors, to collects important facts along with voltage and charging styles. In case of faults, alerts are sent for prompt action. The ESP32 wirelessly transmits facts to the Blynk IoT Cloud platform, enabling the system to track and manage. Through a consumer-friendly interface, key parameters are visualized, facilitating proactive battery control. Additionally, the device offers Wi-Fi charging abilities through inductive coils, enhancing comfort. This included the capability to optimize EV battery overall performance and strengthen electric automobile technology.

Implementation of Smart Vehicle Assistant using Computer vision and Deep Learning

bstract: Autonomous cars are a popular research topic in the field of intelligent transportation systems because they have the potential to greatly reduce traffic and improve travel efficiency. Scene classification is a key technology for autonomous cars because it forms the basis of these vehicles' decision-making. In recent years, deep learning-based methods have demonstrated promise in resolving the scene classification issue. However, further research is needed on a few difficulties related to scene classification methodologies, such as how to treat similarities and contrasts within the same category. To tackle these problems, this research suggests an improved deep network-based scene classification method. Autonomous cars are a popular research topic in the field of intelligent transportation systems because they have the potential to greatly reduce traffic and improve travel efficiency.

Integrating Electric Vehicle Technology into Automotive Education Curricula in Colleges of Education in Nigeria: Challenges and Opportunities

The main purpose of the study was to integrate electric vehicle technology into Automotive Education Curricula in Colleges of Education in Nigeria with focus on the challenges and opportunities. The study which adopted a descriptive survey research design was conducted in northeast Nigeria and had three research questions. The population of the study was 395 comprising 248 lecturers in Automobile Technology Education from 8 Colleges of Education in the Northeast and 147 industry professionals whose industries are registered with the Corporate Affairs Commission as well as the Ministry of Commerce and Industries in the various states of the Northeast. The study adopted simple random sampling technique which Krejcie and Morgan (1970) sample size table and 191 respondents were selected. A structured questionnaire developed by the researcher titled: "Automobile Electric Vehicle Technology Integration Questionnaire, (AEVTIQ)" was used as instrument for data collection. The instrument was validated by three experts and a reliability index of 0.89 was obtained using Cronbach Alpha. Mean statistic was used to answer the research questions. Findings of the study revealed that the content needed for inclusion include Electric Powertrain Systems, Battery Technology and Management, Charging Infrastructure and Technology, Emerging Technologies and Trends, and Thermal Management Systems. Based on the findings, the study recommended that the NCCE should as a matter of urgency update and enrich the existing curriculum to incorporate comprehensive training on EV technology, covering key areas such as electric powertrain systems, battery management, and charging infrastructure.

Development and trend analysis of the cybertruck

Considering that the popularity of electric vehicle trucks (EVs) is growing, this paper takes Teslas Cybertruck as an example and discusses its power system, autonomous driving system, design concepts and materials. The purpose of this research is to explore the trends of future automobile development that are more environmentally friendly, more capable of living, more intelligent, and more multi-functional. Through the analysis, it can be found that the future automobile technology will develop in the direction of electrification, intelligence, multi-function and multi-modal. First of all, vehicles are more inclined to use battery engines with stronger endurance and faster charging speed to further reduce energy consumption and improve efficiency. Secondly, to improve fully autonomous driving technology, future vehicles tend to combine artificial intelligence (AI) with driving control systems. Moreover, according to the different needs of different models, the most suitable design scheme is combined with aerodynamics, and the materials with stronger strength, lighter weight and easier recycling are selected for the manufacture of the body.

A Holistic Approach to Cybersecurity Integration in Autonomous Vehicles

The breakthrough of the self-driving car technology is a double-edged sword that gives us incredible opportunities and at the same time, it is the source of huge cybersecurity challenges. This paper champions the creation of a complete legal system and worldwide standards, like the ISO 26262, to regulate different parts of the autonomous vehicle (AV) operations, such as safety testing, liability, data privacy, and cybersecurity. By highlighting the significance of cybersecurity from the very start, the stakeholders and the automakers have to incorporate the practices like secure programming, routine updates, intrusion detection systems and encryption methods which will be the effective shield against the unauthorized access and data breaches. People should be informed and educated about the cybersecurity risks of AVs so that they can take the steps to protect themselves. Nevertheless, the integration of AVs with the Internet of Things (IoT) and other devices makes the cybersecurity landscape even more complicated, which means that there is a constant need for research and development to deal with problems like adversarial attacks. Presently, the deep learning-driven AV systems do not have the explainability which complicates the safety-critical applications, hence the need to increase the resilience training and deepen the deep learning models against attacks. The paper goes through all the literature on AV cybersecurity which stresses the need for functional safety standards and cybersecurity engineering guidelines. Collaboration between the governments, the industry, and the researchers is a must to set the legal foundations and the technical details for the AV cybersecurity across all levels of automation. Finally, solid cybersecurity is the key to the success of the autonomous vehicle revolution, hence the road to transformative safety, efficiency, and sustainability is paved. INDEX TERMS: Autonomous vehicles, Cybersecurity, ISO 26262, Encryption methods, ISO 21434, EU regulations, Security and Privacy.

From Perception to Prediction: Leveraging Explainable AI in Self-Driving Cars for Enhanced Passenger Trust

Self-driving cars hold immense potential for revolutionizing transportation. However, public acceptance hinges on trust in the car's ability to navigate safely and make critical decisions. This trust deficit stems from the "black box" nature of traditional machine learning models used in self-driving cars. Passengers are left in the dark about the car's perception of the environment and the reasoning behind its actions. This research proposes leveraging Explainable Artificial Intelligence (XAI) techniques to enhance passenger trust in self-driving cars. By incorporating explainability into the perception and prediction modules of the car's decision- making system, we aim to provide passengers with real-time insights into how the car perceives its surroundings and translates those perceptions into driving decisions. This paper explores various XAI methods suitable for self-driving car applications. We discuss the integration of these techniques into the perception and prediction pipelines, enabling the car to explain its reasoning behind lane changes, obstacle avoidance maneuvers, and other critical actions. We evaluate the effectiveness of the proposed approach through user studies, assessing how explainability can improve passenger trust and comfort in self-driving vehicles. The ultimate goal of this research is to foster greater transparency and trust in self-driving car technology, paving the way for wider public adoption and a future of safe and reliable autonomous transportation.

Content Framework for Modern Vehicle Technology Integration into Motor Vehicle Mechanic Work Curriculum in Technical Colleges in Gombe State, Nigeria

The main purpose of this study was to develop a content framework for inclusion of Modern Vehicle Technology into Motor Vehicle Mechanics Work MVMW curriculum in Government Science and Technical Colleges (GSTC) in Gombe State in order to enhance knowledge, attitude and skills for self-employment in modern vehicle maintenance and repairs and employable skills among MVMW trade students in technical colleges. The study was guided by 4 research questions. The population consisted of all the 30 MVMW trade teachers in Gombe State. The study adopted developmental research which was aimed to improve the current MVMW trade curriculum. A structured questionnaire was developed by the researcher with four point rating scale. The questionnaire was validated by four (4) experts from different field of Automobile Technology and trial tested on MVMW trade teachers. Mean and standard deviation was used to answer the responses while factor analysis and Cronbach Alpha was used to determine the validity and reliability of the instrument. The findings revealed that MVT content has four (4) units: these include: Introduction to MVT, Safety Rules and Regulation on MVT, Instrument for MVT Engine Testing and Measurement, It was recommended that: National Board for Technical Education (NBTE) should integrate these elements into the MVMW trade curriculum. Curriculum planners should adopt these clusters in designing the MVMW trade curriculum among other.

Analyzing traffic conflict scenarios and safety assessment in the context of intelligent vehicles: A research review

The several internal disputes and various types of traffic accidents at an urban crossroads make for a complex traffic scenario. Because accident data and crash-based methodologies are limited, researchers are now examining other safety measures, including traffic conflicts that do not depend on crashes. To better assess road safety in the decades to come new real-time data is essential. The automobile industry is heading toward intelligent cars with the goal of enhancing road safety. Although there is a common failure mechanism between accidents and traffic conflicts during driving, which makes it possible to apply crash frequency and severity estimates to traffic conflict frequency and severity models, predicting traffic conflicts presents distinct difficulties. This study discusses conceptual and methodological concerns while reviewing research papers on traffic disputes as safety measures. It offers a thorough synopsis of previous research from the viewpoint of intelligent vehicles. It explains that there are three primary categories into which the perception technology of intelligent cars may be separated: perception technology, communication technology, and the combination of perception and communication technologies. The paper emphasizes the need of harmonizing international safety standards as well as the role that governments and regulatory agencies play in guaranteeing the security of intelligent vehicles. It highlights the need for additional research on transport conflict modeling techniques and offers practical recommendations for future study.

Safe Autonomy: Sensor Fusion in Complex Traffic Environments

Advancements in autopilot driving and car technology have propelled the development of safe autonomy, particularly in navigating complex traffic environments. This paper delves into applying sensor fusion techniques in automobile systems, focusing on their relevance in ensuring safe navigation amidst the intricacies of Indian road conditions. Of course, the conditions of Indian roads have changed hugely and drastically over time but still, it would not be wrong to say that India is still grappling with a high incidence of road accidents exacerbated by non-compliance with traffic rules, integrating sensor fusion technologies becomes pivotal in enhancing the safety and reliability of autonomous systems. By including and adding data from various sensors such as cameras, LiDAR, and radar, these systems gain a comprehensive understanding of their surroundings, enabling real-time decision-making in dynamic traffic scenarios. Furthermore, the paper explores the integration of machine learning algorithms to augment sensor fusion capabilities, facilitating adaptive responses to erratic traffic behaviour and irregular driving practices. Through a technology-centric lens, this research aims to play the role of sensor fusion in bolstering the efficacy of autonomous systems on Indian roads, thereby contributing to the mitigation of road accidents and promoting safer transportation infrastructures.