Automotive Technology Research Articles

Smart Driver Monitoring System using Deep Learning

This research introduces a monitoring system that leverages deep learning methodologies, to analyze and interpret facial features to assess the driver’s alertness. With the rapid advancement of technology especially in automobiles this type of monitoring can be employed in all types of vehicles. The proposed system employs deep learning models like YOLOv8 to detect the state of alertness of the driver. This project aims to achieve this by training the deep learning model on a custom dataset with varying background noise to train the model as accurately as possible. Various performance metrics and evaluations will be done to evaluate the accuracy of the model to integrate it into automobiles. This holds great promise for revolutionizing intelligent transportation systems, automotive technologies, regulatory frameworks, public policies, and societal initiatives aimed at improving road safety, driver well-being, and operational excellence on a global scale, ushering in a safer, more efficient, and sustainable transportation ecosystem.

Automotive Remanufacturing Capability for End of Life Vehicle (ELV) in Malaysia

The Malaysia Automotive Remanufacturing Roadmaps (MAAR) outline policies and technologies to transform the automotive used part sector into remanufacturing. Remanufacturing processes offer significant benefits, hence the need for transformation, despite the substantial availability of used automotive products. The automotive industry in Malaysia has experienced considerable growth and development, leading manufacturers to incorporate remanufacturing into their business operations. Recently, research has concentrated on end-of-life vehicle (ELV) management. Malaysia has no specific period for the end-of-life (ELV) regulation policy. It is essential to consider the proper disposal of these vehicles once they have reached the end of their useful life. The number of abandoned cars is increasing, leading to significant costs for society regarding removal and disposal expenses and other indirect costs due to environmental degradation. Addressing this issue will positively impact both the environment and the economy. This study explores how industries’ capabilities can face sustainability goals under the National Automotive Policy (NAP) 2020 and gain insights into how the remanufacturing process works. Therefore, it is imperative to establish remanufacturing as a norm within automotive practices.   

Driving Towards Safety: The Role of ECUs and IMUs in Advanced Driver-Assistance Systems (ADAS)

This research paper explores the pivotal role of Electronic Control Units (ECUs) and Inertial Measurement Units (IMUs) in Advanced Driver-Assistance Systems (ADAS), focusing on their functionalities, applications, regulatory frameworks, emerging technologies, and real-world case studies. Through a comprehensive review of literature, this paper investigates the integration of ECUs and IMUs in ADAS and its implications for vehicle safety, mobility, and sustainability. Key topics addressed include the adoption challenges, societal impact, international regulatory landscape, standardization efforts, emerging technologies such as artificial intelligence and sensor fusion, and real case studies exemplified by Tesla's Autopilot system, Volvo's City Safety system, and Waymo's autonomous taxi service. The paper concludes by highlighting the transformative potential of ECUs and IMUs in shaping the future of automotive technology and advancing towards safer, more efficient, and more sustainable transportation systems.

Enhancing Reliability of Advanced Driver-Assistance Systems through Predictive Maintenance and Data-Driven Insights

The advancement of Advanced Driver Assistance Systems (ADAS) marks a pivotal evolution in automotive technology, aiming to enhance road safety and driving efficiency through a wide array of functionalities like blind spot detection, emergency braking, and adaptive cruise control. This research paper delves into the operational integrity, performance metrics, and maintenance strategies of ADAS components, underpinned by a comprehensive methodology involving data collection, pre-processing, feature engineering, machine learning model development, and rigorous validation processes. Systematic inspection of ADAS components indicates their importance in vehicle safety and reliability. The visibility, distance, speed, and steering angle of front cameras, LiDAR, radar, and ultrasonic sensors are carefully evaluated. Maintenance logs show proactive error code management, boosting efficiency. SVM, Gradient Boosting, and Random Forest machine learning models predicted ADAS component failures during validation and testing. Random Forest scored 90% accuracy, 92% precision, 88% recall, and 90% F1. Gradient Boosting was the most accurate, with 93% accuracy, 94% precision, 91% recall, and 92% F1. SVM predicted ADAS component failures with 88% accuracy, 90% precision, 85% recall, and 87% F1 score. Machine learning helps shift from reactive to proactive maintenance. Modelling sensor signal quality, actuator reaction times, error code frequencies, and maintenance intervals enables predictive maintenance and failure detection. Feature engineering builds predictive models using maintenance logs and operational KPIs. The models predict ADAS component failures, boosting vehicle safety and dependability. Using external data improves predictive maintenance models. The maintenance model's adaptability and forecast accuracy are proved by ADAS operation after traffic, accident, and manufacturer upgrades. Predictive maintenance and machine learning improve ADAS dependability and safety, the study found. Advanced analytics and data-driven insights can reduce automotive system failures, improving safety and reliability. 

Flexible, Transparent, and Bifacial Perovskite Solar Cells and Modules Using the Wide-Band Gap FAPbBr3 Perovskite Absorber.

Perovskite solar cells (PSCs) offer impressive performance and flexibility, thanks to their simple, low-temperature deposition methods. Their band gap tunability allows for a wide range of applications, transitioning from opaque to transparent devices. This study introduces the first flexible, bifacial PSCs using the FAPbBr3 perovskite. We investigated the impact of optimizing electron and hole transport layers on the cells' bifaciality, transparency, and stability. PSCs achieved a maximum power conversion efficiency (PCE) of 6.8 and 18.7% under 1 sun and indoor light conditions (1200 lx), respectively, showing up to 98% bifaciality factor and an average visible transmittance (AVT) of 55%. Additionally, a P1-P2-P3 laser ablation scheme has been developed on the flexible poly(ethylene terephthalate) (PET) substrate for perovskite solar modules showing a PCE of 4.8% and high geometrical fill factor (97.8%). These findings highlight the potential of flexible, bifacial PSCs for diverse applications such as building-integrated photovoltaics (BIPV), agrivoltaics, automotive technology, wearable sensors, and Internet of things (IoT).

Evaluating the Impact of Price Fluctuations on Consumer Buying Decisions and Adaptation Strategies in the Automotive Market

This research paper delves into the intricate dynamics of the U.S. automotive market from 2012 to 2022, with a specific focus on understanding how price fluctuations influence consumer buying decisions and the corresponding adaptation strategies deployed by manufacturers. Through a mixed-methods approach, combining quantitative analysis of sales data, price trends, and economic indicators with qualitative insights from interviews with industry experts and consumers, the study reveals a pronounced shift towards fuel-efficient and electric vehicles (EVs). This shift is primarily driven by consumer sensitivity to price changes and a burgeoning emphasis on environmental sustainability. The findings highlight significant demographic variations in price sensitivity, particularly among younger and middle-income consumers, and delineate the impact of broader economic indicators on purchasing behaviors, especially in the luxury vehicle segment. Manufacturers’ adaptation strategies, including product diversification, investment in sustainable technologies, and flexible pricing models, are also examined. The study’s insights underscore the complex interplay between economic theories of consumer behavior, market adaptation strategies, and the evolving landscape of automotive technology. It offers valuable implications for manufacturers, policymakers, and consumers, aiming to navigate the challenges of market volatility and the transition towards sustainability. The research concludes with recommendations for future studies to explore emerging trends and assess long-term impacts, contributing to a deeper understanding of the automotive market’s adaptive mechanisms in response to economic fluctuations.

Investigation of Different Communication Topologies for Cooperative Adaptive Cruise Control Systems

After Adaptive Cruise Control (ACC) system applications, Cooperative Adaptive Cruise Control (CACC) systems are becoming an important part of automotive technology and industry in autonomous vehicles convoy applications. Together with this developing technology, CACC systems use vehicle to vehicle (V2V) communication to automatically transmit the movement information of vehicles. In this context, ACC systems use Radar or LIDAR measurements while CACC systems also consider the acceleration of the preceding vehicle. In this paper, the forms of information transmission between vehicles in autonomous vehicle convoys using CACC systems have been examined. From these forms of information transmission, the leader following, the predecessor follow-ing and the leader – predecessor following topologies have been considered. For each topology, an autonomous vehicle convoy consisting of eight vehicles was modeled in the MATLAB/Simulink environment. The feedforward and the feedback control system structure were given for CACC and ACC systems. For different communication topologies, the position-time, the speed-time, the acceleration-time and the headway time-time results were obtained. The maximum intervehicle distance error plots for each vehicle in different topology convoys were given to analyze the dynamic behavior of the convoys. The results have been analyzed in terms of the maximum intervehicle distance, the maximum speed, the minimum and the maximum acceleration, and the maximum headway time deviation from the desired headway time.

Mathematical Analysis of the Electromotive Induced Force in a Magnetically Damped Suspension

This study explores the advanced mathematical modeling of electromagnetic energy harvesting in vehicle suspension systems, addressing the pressing need for sustainable transportation and improved energy efficiency. We focus on the complex challenge posed by the non-linear behavior of magnetic flux in relation to displacement, a critical aspect often overlooked in conventional approaches. Utilizing Taylor expansion and Fourier analysis, we dissect the intricate relationship between oscillation and electromagnetic damping, crucial for optimizing energy recovery. Our rigorous mathematical methodology enables the precise calculation of the average power per cycle and unit mass, providing a robust metric for evaluating the effectiveness of energy harvesting. Further, the study extends to the practical application in a combined system of passive and electromagnetic suspension, demonstrating the real-world viability of our theoretical findings. This research not only offers a comprehensive solution for enhancing vehicle efficiency through advanced suspension systems but also sets a precedent for the integration of complex mathematical techniques in solving real-world engineering challenges, contributing significantly to the future of energy-efficient automotive technologies. The cases reviewed in this article and listed as references are those commonly found in the literature.

Comparative Analysis of Following Distances in Different Adaptive Cruise Control Systems at Steady Speeds

Adaptive Cruise Control (ACC) systems have emerged as a significant advancement in automotive technology, promising safer and more efficient driving experiences. However, the performance of ACC systems can vary significantly depending on their type and underlying algorithms. This research presents a comprehensive comparative analysis of car-following distances in different types of Adaptive Cruise Control systems. We evaluate and compare three distinct categories of ACC systems using three different commercial vehicles brands. The study involves extensive real-world testing at Zalazone Proving Ground, to assess the performance of these systems under various driving conditions, including driving at multiple speeds and applying different car following scenarios. The study investigates how each ACC system manages the minimum following distances according to the type of ACC sensors in each tested vehicle. Our findings revealed that at low to medium ranges of constant driving speeds, there was an approximate linear increase in the average clearances between the two following vehicles for all applied scenarios, with comparatively shorter clearances obtained by the vision-based ACC system, while unstable measurements with a high level of dispersion for all ACC systems were observed at high range of driving speeds.

Assessing Training Methods for Advanced Driver Assistance Systems and Autonomous Vehicle Functions: Impact on User Mental Models and Performance

Understanding the complexities of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technologies is critical for road safety, especially concerning their adoption by drivers. Effective training is a crucial element in ensuring the safe and competent operation of these technologies. This study emphasises the critical role of training methodologies in shaping drivers’ mental models, defined as an individual’s cognitive frameworks for understanding and interacting with ADAS and AV systems. Their mental models substantially influence their interactions with those technologies. A comparative analysis of text-based and video-based training methods has been conducted to assess their influence on participants’ performance and the development of their mental models of ADAS and AV functionalities. Performance is evaluated in terms of the accuracy and reaction time of the participants as they interacted with ADAS and AV functions in a driving simulation. The findings reveal that video-based training yielded better performance outcomes, more accurate mental models, and a deeper understanding of ADAS functionalities among participants. These findings are crucial for policy makers, automotive manufacturers, and educational institutions involved in driver training. They underscore the necessity of developing tailored training programs to facilitate the proficient and safe operation of increasingly complex automotive technologies.

Advancing Automotive Technologies [Automotive Electronics]

Advancing Automotive Technologies [Automotive Electronics]

Editorial

Dear Readers,                                                      In the fast-paced landscape, modern transportation is undergoing a profound transformation, driven by technological innovation, changing consumer preferences, and the need for sustainability. In the country, from urban areas to remote rural areas, the evolution of mobility technology is restructuring the way we navigate, unlocking new opportunities for efficiency, sustainability, and accessibility. As we stand at the threshold of a new era in mobility, it's essential to explore the innovative technologies driving this revolution and their potential to redefine the future of transportation. ARAI Journal of Mobility Technology takes pride in presenting its Jan–Mar 2024 issue that covers IC engine technology, Brake hose failure analysis, modeling on different technologies, battery and EV related subjects, and vehicle dynamics. The ARAI Journal journey is not just about getting the technologies which are about shaping the future of transportation and society as a whole. Electric vehicle technologies are poised to revolutionize the future of transportation, offering a cleaner, more sustainable alternative to traditional gasoline-powered cars. With growing concerns about climate change and environmental sustainability, the shift towards electric propulsion represents a significant step forward in reducing carbon emissions and mitigating the effects of climate change. With ongoing advancements in battery technology, charging infrastructure, and vehicle design, EVs are becoming increasingly practical and accessible for consumers around the world. By embracing innovation, fostering collaboration, and addressing the challenges ahead, we can accelerate the transition to a cleaner, greener transportation system that benefits both people and the planet. With ongoing advancements in battery technology, charging infrastructure, and vehicle design, EVs are becoming increasingly practical and accessible for consumers around the world. By embracing innovation, fostering collaboration, and addressing the challenges ahead, we can accelerate the transition to a cleaner, greener transportation system that benefits both people and beyond electrification, automotive technologies are also driving progress in safety and convenience. Advanced driver-assistance systems (ADAS) such as adaptive cruise control, lane-keeping assist, and automatic emergency braking are becoming standard features in modern vehicles, helping to prevent accidents and save lives. Moreover, the emergence of autonomous driving technology promises to revolutionize transportation by eliminating human error and making roads safer for everyone. ARAI Journal is a gateway for all researchers, industry professionals, students, faculty to discover the latest technologies and advancements driving the automotive industry forward on the planet. In the midst of life's challenges and uncertainties, may this journal be a source of solace and strength, reminding you of your resilience and inner wisdom. May it be a space where you can find clarity, purpose, and peace amidst the chaos of daily life. I want to extend my warmest wishes to each and every reader of this journal.

Navigating Innovation Highways: Unraveling the Entrepreneurial Culture's Role with Knowledge Management as the Mediator in Automotive Technology Innovations

The electric vehicle (e-vehicle) automotive sector in India has been undergoing substantial expansion and metamorphosis, propelled by reasons like governmental regulations, ecological considerations, and technological progress. The objective of this study is to determine the impact of entrepreneurial Culture on technical innovation in the e-vehicle automobile business, while also examining the role of knowledge management as a mediator. Employing a cross-sectional design, data for analysis were gathered using a questionnaire technique from managers and employees of six e-vehicle automobile industries in India. Despite distributing 500 questionnaires, the study received responses from 53 managers and 332 employees. Utilizing the PLS-SEM approach, the research reveals the positive influence of an entrepreneurial culture on technological innovation. Moreover, it highlights the partial mediation effect of knowledge management in the relationship between entrepreneurial culture and technology innovation. These findings carry substantial implications for businesses in the e-vehicle sector aiming to enhance their innovation capabilities by fostering an entrepreneurial culture and implementing effective knowledge management techniques.

Driving Distraction Evaluation Model of In-Vehicle Infotainment Systems Based on Driving Performance and Visual Characteristics

With the rapid development of automotive electronic technology, increasingly car enterprises choose to carry touchscreen in-vehicle infotainment systems (IVISs) in smart cars. The integration of navigation, music, telephone, and other functions into touchscreen IVISs has greatly enriched the driving experience. However, the design of touchscreen IVISs, which lacks consideration of driver human–computer interaction leads to increased driving distraction. The current discussion is limited to evaluating the overall distractions of the secondary task, and there is a lack of research on classifying and summarizing the types of driving distractions. Therefore, this paper proposes a new method to evaluate the degree of driving distraction during the interaction with touchscreen IVISs based on driving performance and visual characteristics. A driving simulation environment was built to conduct psychological experiments on three types of driving distraction, namely visual, manual, and cognitive distraction, respectively, to obtain the impact of different types of driving distraction by using touchscreen IVISs. Combining the criteria importance through intercriteria correlation method with fuzzy comprehensive evaluation to establish a driving distraction evaluation method, we quantify the driving distraction caused by the touch screen IVIS. The results of this study indicate that the proposed driving distraction evaluation model by using driving performance and visual characteristics can effectively evaluate the degree of driving distraction caused by the touchscreen IVIS and provide reference and specification for IVIS safety design from a human–computer interaction perspective.

Hydrogen-Powered Vehicles: Comparing the Powertrain Efficiency and Sustainability of Fuel Cell versus Internal Combustion Engine Cars

Due to the large quantities of carbon emissions generated by the transportation sector, cleaner automotive technologies are needed aiming at a green energy transition. In this scenario, hydrogen is pointed out as a promising fuel that can be employed as the fuel of either a fuel cell or an internal combustion engine vehicle. Therefore, in this work, we propose the design and modeling of a fuel cell versus an internal combustion engine passenger car for a driving cycle. The simulation was carried out using the quasistatic simulation toolbox tool in Simulink considering the main powertrain components for each vehicle. Furthermore, a brief analysis of the carbon emissions associated with the hydrogen production method is addressed to assess the clean potential of hydrogen-powered vehicles compared to conventional fossil fuel-fueled cars. The resulting analysis has shown that the hydrogen fuel cell vehicle is almost twice as efficient compared to internal combustion engines, resulting in a lower fuel consumption of 1.05 kg-H2/100 km in the WLTP driving cycle for the fuel cell vehicle, while the combustion vehicle consumed about 1.79 kg-H2/100 km. Regarding using different hydrogen colors to fuel the vehicle, hydrogen-powered vehicles fueled with blue and grey hydrogen presented higher carbon emissions compared to petrol-powered vehicles reaching up to 2–3 times higher in the case of grey hydrogen. Thus, green hydrogen is needed as fuel to keep carbon emissions lower than conventional petrol-powered vehicles.

Multiplicity dependence of the freezeout parameters in high energy hadron-hadron collisions* *Supported by Princess Nourah bint Abdulrahman University, Researchers Supporting Project Number PNURSP2024R106, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. We would like to express our gratitude for the support received from Abdul Wali Khan University Mardan, Pakistan; Hubei University of Automotive Technology, Doctoral Research Fund number BK202313; University of Guyana; University of Tabuk, Saudi Arabia; and Qassim University, Saudi Arabia, which have contributed to creating a conducive research environment.

We examined the transverse momentum ( ) spectra of various identified particles, encompassing both light-flavored and strange hadrons ( , , , ϕ, , , , and ), across different multiplicity classes in proton-proton collisions (p-p) at a center-of-mass energy of TeV. Utilizing the Tsallis and Hagedorn models, parameters relevant to the bulk properties of nuclear matter were extracted. Both models exhibit good agreement with experimental data. In our analyses, we observed a consistent decrease in the effective temperature (T) for the Tsallis model and the kinetic or thermal freeze-out temperature ( ) for the Hagedorn model, as we transitioned from higher multiplicity (class-I) to lower multiplicity (class-X). This trend is attributed to the diminished energy transfer in higher multiplicity classes. Additionally, we observed that the transverse flow velocity ( ) experiences a decline from class-I to class-X. The normalization constant, which represents the multiplicity of produced particles, was observed to decrease as we moved toward higher multiplicity classes. While the effective and kinetic freeze-out temperatures, as well as the transverse flow velocity, show a mild dependency on multiplicity for lighter particles, this dependency becomes more pronounced for heavier particles. The multiplicity parameter for heavier particles was observed to be smaller than that of lighter particles, indicating a greater abundance of lighter hadrons compared to heavier ones. Various particle species were observed to undergo decoupling from the fireball at distinct temperatures: lighter particles exhibit lower temperatures, while heavier ones show higher temperatures, thereby supporting the concept of multiple freeze-out scenarios. Moreover, we identified a positive correlation between the kinetic freeze-out temperature and transverse flow velocity, a scenario where particles experience stronger collective motion at a higher freeze-out temperature. The reason for this positive correlation is that, as the multiplicity increases, more energy is transferred into the system. This increased energy causes greater excitation and pressure within the system, leading to a quick expansion.

Digital Learning Content in Automotive Technology Program Towards Student Cognition in TVET: A Partial Experiment

This study examines, via a partial experiment, the impact of the automotive technology program digital learning content on students' cognition. Consequently, the researcher conducted a partial experiment in this study to evaluate the functionality and usability of digital content. It fits with the social stigma of TVET, shaped by the perception that vocational education and training exist to serve school leavers rather than as an essential strategy to provide competent workers to the labour market. In total, 47 students in the Bachelor of Engineering Technology (Honours) in the Mechatronics (Automotive) programme participated in the investigation. The study's findings indicated that digital learning content has a significant positive influence on the cognitive and psychomotor achievement of students. The functionality of digital learning content is evaluated based on its capacity to produce the desired learning outcomes and its compatibility with the current curriculum. Assessing the usability of digital learning content involves measuring usability, navigation, and overall user experience. This evaluation demonstrates that the digital learning content is functional and usable, providing students with a positive learning experience. In addition, the results of this test demonstrate a significant improvement in students' cognitive achievement, indicating that digital learning content is beneficial in facilitating the learning process, and students are evaluated on their ability to perform practical tasks related to the course material. The results of this evaluation demonstrate a significant improvement in the psychomotor performance of students, indicating that digital learning content can enhance their practical skills. This study concluded that incorporating digital learning content into the curriculum for automotive technology has a significant positive influence on students' cognitive and psychomotor achievement. Available digital learning content can facilitate learning and provide students with a positive educational experience. These findings suggest that incorporating digital learning content into automotive technology programs may be a valuable addition to conventional learning methods. This study implies that digital learning effectively facilitates the learning process and that students are evaluated on their ability to complete practical tasks related to the course material. Consequently, this study was conducted to aid educators in using digital learning for students in automotive programs.

Mechanical engineering in automotive innovation: A review of electric vehicles and future trends

This paper provides a succinct overview of the extensive landscape of automotive innovation within the realm of mechanical engineering, focusing on the transformative emergence of electric vehicles (EVs) and the anticipated future trends in this dynamic field. As the automotive industry undergoes a paradigm shift towards sustainable and eco-friendly transportation solutions, the role of mechanical engineering in shaping the future of mobility becomes increasingly pivotal. The review begins by delving into the evolution of electric vehicles, tracing their historical development and highlighting key milestones that have propelled them from niche alternatives to mainstream contenders in the automotive market. A comprehensive examination of the underlying mechanical engineering principles governing EV design and performance is presented, emphasizing advancements in battery technology, electric drivetrains, and energy management systems. The document scrutinizes current challenges and opportunities associated with electric vehicles, addressing issues such as range anxiety, charging infrastructure, and the environmental impact of battery production and disposal. It explores ongoing research efforts within the field of mechanical engineering aimed at overcoming these challenges and enhancing the overall efficiency, safety, and sustainability of electric vehicles. Furthermore, the paper outlines the anticipated future trends in automotive innovation, providing insights into cutting-edge technologies and methodologies poised to shape the next generation of electric vehicles. Topics include advancements in materials science for lightweight construction, artificial intelligence integration for autonomous driving, and the integration of renewable energy sources to further enhance the environmental footprint of EVs. This review underscores the pivotal role of mechanical engineering in the ongoing revolution of automotive technology, emphasizing the transformative impact of electric vehicles and offering a glimpse into the exciting future trends that will define the landscape of sustainable mobility.

A Review of One-Box Electro-Hydraulic Braking System: Architecture, Control, and Application

With the development of automobile electrification and intelligence, new requirements have been put forward for automotive braking technologies. Under this background, the One-box EHB (Electro-Hydraulic Braking system) brake-by-wire technology has emerged, which combines the electric booster and wheel-cylinder control module into one box and can realize vehicle stability and comfort functions such as service brake, pedal feel simulation, brake decoupling, failure backup, active braking, and wheel-cylinder pressure control. This article reviews the current research of key technologies of One-box EHB, including system architecture design and applications under high-level autonomous driving, master cylinder pressure control algorithm design, wheel-cylinder pressure control algorithm design, and electro-hydraulic composite braking control algorithm design. Finally, this article summarizes the current research status of One-box EHB key technologies and puts forward suggestions for future research directions.

Strategi Pemasaran Berbasis Kualitas Produk, Citra Merek, dan Gaya Hidup: Studi Kasus Keputusan Pembelian Konsumen Mobil Mitsubishi di Kabupaten Kudus

Rapid advancements in automotive technology have shifted consumer paradigms regarding four-wheeled motor vehicles, transforming them from mere transportation tools to lifestyle choices. This study investigates the influence of product quality, brand image, and lifestyle on consumer purchasing decisions regarding Mitsubishi automobiles in Kudus Regency. The research methodology employs an explanatory approach, amalgamating qualitative and quantitative data gathered from 125 Mitsubishi consumers.The analysis results reveal significant influences: product quality positively impacts purchase interest and decisions, brand image similarly exerts a significant positive influence on purchase interest and decisions, and lifestyle demonstrates a positive influence on purchase interest and decisions. Purchase interest also functions as a mediating variable between brand image and purchase decisions, as well as between lifestyle and purchase decisions. However, purchase interest does not mediate the relationship between product quality and purchase decisions. In conclusion, product quality, brand image, and lifestyle significantly contribute to the purchasing decisions of Mitsubishi consumers in Kudus Regency, with purchase interest acting as a mediating factor in several relationships. These findings offer crucial insights for manufacturers, marketers, and automotive industry practitioners to develop more effective marketing strategies that cater to consumer needs and attract the market.