Automobile Technology Research Articles

Evaluation of the barrier and mechanical characteristics of bromo‐butyl rubber‐containing blends for use in inner liner of automotive tires

AbstractOne of the best elastomers for the inner liner of a tire is brominated butyl rubber (BIIR). On the other hand, cutting‐edge automobile technologies call for BIIR with enhanced curing, mechanical, and barrier qualities. Consequently, in this investigation, BIIR blends were created with different elastomers that have similar permeability qualities of BIIR but have improved physical properties. Elastomeric blends of BIIR with epoxidized natural rubber (ENR), hydrogenated nitrile rubber (HNBR), and polyepichlorohydrin rubber (CO) were prepared using a melt mixing procedure that could be scaled up for commercial use. The crosslinking behavior, physical, morphological, and barrier characteristics of the developed blends were assessed. The crosslinking properties of new blends revealed a 100% improvement in torque difference, indicating superior cocurability of them with other general‐purpose rubbers used in tire in comparison to the reference BIIR sample. It was also found that the newly developed blends have enhanced mechanical qualities without sacrificing the air impermeability of BIIR, which in turn gives the capacity to withstand high air pressures and thereby a decrease in rolling resistance.

A Systematic Review of Emergency Braking Assistant System to Avoid Accidents Using Pulse Width Modulation and Fuzzy Logic Control Integrated with Antilock Braking

The increasing number of fatalities in nations across the world because of a deficiency of protective technology in automobiles has created a chaotic scene in recent years. However, the car driver’s Perception-Reaction Time (PRT) plays an important variable during such accidents and emergencies. The Anti-Lock Braking System (ABS) seems a viable technology, which today is used to prevent tires from sliding during quick brakes, whereas EBS is designed for braking assistance during tuning or emergency braking. Physical weight, rotational rigidity, diameter, and tire material strength of the vehicle are all used to simulate them. Advance Driver Assistance Systems (ADAS), which include Anti-lock brakes (ABS) and Emergency Braking Systems (EBS), are the foremost viable technique for minimizing the environmental impact and uncertainties of driving road transportation. The presented systematic review aims to deliver a ground-level analysis that can be used to enhance the safety of motor vehicle driving, reduce wheel slip to achieve the best possible stopping distance in commercial and specialized vehicles, and influence future transportation. In this study, the most widely utilized technologies for ADAS have been reviewed and discussed. Various sensors used to improve braking and vehicle performance have been systematically studied in the context of low power-consuming techniques like pulse width modulation. An analysis of emergency braking procedures performed by riders with varying degrees of braking expertise was conducted using previously collected experimental data, and the results were used to conclude potential loss of control situations.

Probabilistic Semantic Mapping for Autonomous Driving in Urban Environments.

Statistical learning techniques and increased computational power have facilitated the development of self-driving car technology. However, a limiting factor has been the high expense of scaling and maintaining high-definition (HD) maps. These maps are a crucial backbone for many approaches to self-driving technology. In response to this challenge, we present an approach that fuses pre-built point cloud map data with images to automatically and accurately identify static landmarks such as roads, sidewalks, and crosswalks. Our pipeline utilizes semantic segmentation of 2D images, associates semantic labels with points in point cloud maps to pinpoint locations in the physical world, and employs a confusion matrix formulation to generate a probabilistic bird's-eye view semantic map from semantic point clouds. The approach has been tested in an urban area with different segmentation networks to generate a semantic map with road features. The resulting map provides a rich context of the environment that is valuable for downstream tasks such as trajectory generation and intent prediction. Moreover, it has the potential to be extended to the automatic generation of HD maps for semantic features. The entire software pipeline is implemented in the robot operating system (ROS), a widely used robotics framework, and made available.

Efficiency Optimization of Wireless Power Transfer System for Electric Vehicle Based on Improved Marine Predators Algorithm

Electric vehicle (EV) is the core part of future automobile technology, and safe and reliable wireless power transfer (WPT) technology is the key link to improve the intelligent driving technology of EV. In this paper, the uncertainty quantification method is proposed to guide the optimization design of WPT structure, so as to improve the efficiency of WPT. First this paper establishes a surrogate model of WPT efficiency based on the adaptive sparse polynomial chaos expansion, and the uncertainty of EVs WPT transmission efficiency is quantified, the computational efficiency is improved by about 8.4 times. Then the surrogate model is combined with the global sensitivity analysis method to quantify the impact of different variables in WPT on efficiency and screen out the variables with greater impact. Finally, this paper uses the improved marine predators algorithm to optimize the selected WPT system structure parameters. Considering the uncertainty, the average efficiency of the optimized WPT system is increased from 73.43% to 94.64%. Compared with other optimization methods, it proves that the method in this paper can optimize WPT more efficiently, and significantly improve the transmission efficiency.

Quantifying fuel cell vehicles and hydrogen refueling station networks in China based on roadmap

Fuel-cell vehicles (FCVs) represent a cutting-edge and zero-emission car technology, which would be promoted to enter market by construction of an efficient network of hydrogen refueling stations (HRSs). Herein, the quantity of FCVs and HRSs in China is recorded and analyzed to figure out the past and future scenarios, with the emphasis on HRS location and FCV refueling distance. A reducing FCV/HRS ratio is witnessed after 2018 due to an intensive growth of HRSs, implying improved refueling experience and reduced refueling distance. 5000 HRSs will be built before 2035 according to the Roadmap of China. In order to estimate the refueling distance in 2035, the number of HRSs is assumed to construct in cities with diverse population sizes. If projected HRSs are entirely placed in cities with more than one million people, the refueling distance would be ~5 km, which falls into the consumer expectation for fossil fuel powered vehicles. The refueling distance can be further shortened to 3 km, provided centralizing the limited HRSs in cities with >5 million people. This work enlightens a more feasible and applicable strategy to formulate a necessary HRS network in densely populated city regions in priority to promote FCV and HRS development in the current stage.

Artificial intelligence in the field of driving

The advent of the electrification of cars has made it possible to use a large number of electronic components in cars, and the intelligent car industry is developing rapidly because of the growing maturity of automotive electronics. Smart driving is safer, more comfortable, and more efficient than traditional driving, and it has great prospects for development in terms of brain-controlled car technology, intelligent internet interaction technology, and deep learning technology. It is undeniable that the development of smart driving technology is accompanied by challenges in terms of technology networks, policies and regulations, and economic conditions.

Research on the application of artificial intelligence to auto-driving cars

Since the concept of artificial intelligence was introduced in the 1950s, artificial intelligence technologies have emerged more and more frequently. Besides, the application of artificial intelligence technologies in driverless cars has increased, especially in the field of image recognition. Many deep learning methods of image recognition have emerged in the field of autonomous driving. The primary purpose of this paper is to highlight the importance and functionality of using neural networks and image recognition in artificial intelligence for driverless functions. This paper also provides a comprehensive review of the core aspects of driverless technology, the role of image recognition and person recognition in environment perception, the impact of deep learning on driverless behavior decisions, and driverless vehicle kinetic energy control systems. Although the driverless car technology is now being updated, all three modes have great accuracy and results. However, each subsystem still faces many problems and challenges, which may be solved in future research. The driverless car technology would be promoted on a large scale after its safety and suitability are ensured.

Older drivers in Australia and advanced vehicle technologies: What are their opinions? A qualitative study

BackgroundThere is limited research on older drivers’ perspectives on advanced vehicle technologies (AVTs). This study investigates what older drivers know and understand about AVTs in the current vehicle market, and identifies what motivates older drivers to use or not use AVTs. MethodOlder drivers (≥65 years) living in New South Wales, Australia completed semi-structured telephone interviews guided by the Car Technology Acceptance Model. All interviews were audio-recorded and transcribed verbatim. Content analysis and deductive-inductive thematic analysis were completed on each transcript independently by two researchers, with all themes presented to the investigative team, discussed until consensus was reached. Participant recruitment were ceased after thematic saturation. Results24 participants (mean age: 74.5 years; 13 males, 11 females) were interviewed. Eleven different AVTs were mentioned by participants. Attitudes towards these AVTs impacted how much participants understood how these technologies worked. These attitudes and motivation of technology usage could be explained by four major themes; (1) AVTs help with safety but overall responsibility remains with the driver, (2) Lack of clear information and instructions make AVTs look confusing, (3) AVTs need to be more user-friendly for older adults, and (4) Expensive out-of-pocket costs stop AVT usage. ConclusionsDespite understanding the safety benefits of simple, standard AVTs, older drivers perceive barriers that hinder their use of more complicated technologies. Increased consultations to make AVTs more acceptable to older adults are needed. In conjunction, more resources and options aimed at helping older adults better understand and access AVTs need to be developed.

Analysis on the Influence of Government Subsidy on Foton Automobile Technology Innovation Efficiency under the Background of Dual Carbon

The new energy industry is an emerging industry; the rise of the emerging industry is inseparable from the national macro-control. In recent years, new energy automotive industry as the mainstay of carbon reduction, more and more to receive government subsidies. Based on the DEA-Malmquist model, this paper takes Foton Automobile as the research object to explore whether enterprises can rationally use government subsidies to promote technology innovation under the dual-carbon background. It is found that government subsidies have a certain incentive effect on Foton automobile technology innovation. The static efficiency score shows that after the two-carbon goal is proposed, the government subsidies continue to increase and the innovation efficiency of the enterprise reaches the best state. The dynamic efficiency score shows that the input-output of Foton Automobile has not reached the optimal scale, and Foton Automobile still needs to adjust the production scale of the enterprise and improve the innovation efficiency. Finally, the government should strengthen the supervision of the use of government subsidies, enterprises should speed up the transformation of technological achievements, pay attention to the rational distribution of input and output suggestions and countermeasures.

Obstacle avoidance control algorithm of self-driving electric vehicle based on DRL

Facing the increasingly severe pressure of environmental pollution, road safety and traffic congestion, intelligent vehicle has become the development focus of modern automobile industry, and it is also the main direction and key competitive field of automobile technology development in the future. This trend puts forward higher requirements for vehicle control system. Under the background of the vigorous development of electric vehicle and automatic driving technology, through the overall planning of obstacle avoidance strategy of automatic driving electric vehicle, studying the correct control method and designing obstacle avoidance system under different working conditions, it has theoretical guiding significance and practical application value for the development of obstacle avoidance control system of automatic driving electric vehicle in the future. This chapter first introduces the basic theoretical knowledge of reinforcement learning, then introduces the DL theory and classical neural network, and finally introduces DQN and DDPG algorithms in DRL, so as to interact RL data. Combining DL with reinforcement learning, the feature representation of complex driving scene is established, which is used as input to train intelligent vehicle obstacle avoidance control strategies that can complete various driving tasks, and transplanted to practical applications, so that they have better performance in automatic driving obstacle avoidance control. To sum up, DRL has great research and application value for obstacle avoidance control of self-driving electric vehicles, which can greatly promote the level of intelligent transportation system.

A secret image sharing solution for autonomous vehicles

With the improvement of personal privacy awareness and government agencies' awareness of national security information protection, data access and data security of smart cars is a thorny problem. The fundamental reason is that the key to access and store data is kept in the hands of smart car companies. Although many scholars have made great progress in the research of secret image sharing solutions before, such as progressive visual secret sharing, polynomial based secret image sharing, etc., these encryption schemes are single key encryption and cannot solve the technical problems encountered by the current autonomous vehicle. Therefore, based on the research of previous scholars and taking into account the social problems in solving the existing image sharing, this paper proposes two secret image sharing solutions. One is key sharing solution; The other is Images Sharing solution. Through the theoretical analysis of the application scenario, we can conclude that these two solutions can well solve the problems of whether users, smart car companies or third-party institutions infringe on users' privacy, whether it is due to the security hidden dangers brought by smart car technology, and whether they violate the national information security law.

Design and simulation of automotive radar for autonomous vehicles

Modern automobile technology is pushing towards maximizing road safety, connected vehicles, autonomous vehicles, etc. Automotive RADAR is core sensor technology used for ADAS (Advanced Driver Assistance Technology), ACC (Adaptive Cruise Control), AEB (Automatic Emergency Braking System), traffic assistance, parking aid, and obstacle/pedestrian detection. Despite being inexpensive, RADAR technology provides robust results in harsh conditions such as harsh weather, extreme temperature, darkness, etc. However, the performance of these systems depends on the position of the RADAR and its characteristics like frequency, beamwidth, and bandwidths. Moreover, the characterization of varied materials like layers of paint, polish, primer, or layer of rainwater needs to be analyzed. This performance can be predicted through real-time simulation using advanced FEM software like Altair FEKO&WinProp. These simulations can provide valuable insight into the performance of the system, allowing engineers to optimize the system for specific use cases. For example, simulation can be used to determine the optimal parameters of the RADAR system for a given application. This information can then be used to design and build a physical model or prototype that is optimized for the desired performance. These simulations play a prominent role in determining appropriate data collection and sensor fusion, which reduces the cost and time required for the development of a physical model or prototype. The continued growth and demand for advanced safety features in vehicles further highlight the importance of RADAR technology in modern automobile technology. By accurately characterizing the environment and simulating the system's behavior in real time, engineers can optimize RADAR systems for specific use cases, contributing to safer and more efficient driving experiences

PERFORMANCE OF LOCALIZATION BASED ON VISUAL SLAM AND LIDAR POINT CLOUD REGISTRATION

Abstract. Self-driving car technology has become increasingly popular in recent years. Traditionally, these cars rely on GNSS and INS, but limitations in urban environments can affect their effectiveness. GNSS technology has developed to address this issue, but it still has limitations in certain environments. LiDAR technology has emerged as a solution to obtain high-precision point cloud maps and SLAM technology is now used to integrate these maps with sensor data, such as cameras, to achieve precise positioning. This enables visual SLAM to be performed in environments where GNSS signals are blocked. In this research, we aim to use SLAM technology to obtain posterior environmental information and match it with prior high-precision point cloud maps. The research will start with hardware configuration, actual measurement and analysis of SLAM algorithms, and 3D point cloud matching methods. Results and benefits will be analysed to compare the advantages and disadvantages of point cloud matching algorithms and applicable hardware.

AI Technology in Self-Driving Cars

AI Technology in Self-Driving Cars

SID Display Week: Celebrating 60 Years of Innovation

THIS YEAR'S DISPLAY WEEK, THE ANNUAL International Symposium, Seminar, and Exhibition organized by the Society for Information Display (SID), is being held May 21–26 at the Los Angeles Convention Center. It's occurring 60 years after the first annual event was held in the same city. On this anniversary, I want to reflect on where we are today. All around us, we are witnessing transformations in the ways we interact with technologies and devices. Today's human-technology interfaces are more natural, intuitive, and interactive than ever before, with visual displays at the center. We've seen the evolution of human inputs from joysticks, to mice and keyboards, to touchscreens. Now voice and vision are emerging as the next wave. With continued developments in artificial intelligence (AI), natural interfaces based on touch, voice, and vision are poised to become the primary ways that we interact with devices. New products and markets have been driven by innovators who capitalized on new human-technology interaction paradigms. A driving force in the widespread proliferation of personal computers was the advent of the mouse and keyboard, and modern smartphones were largely made possible because of touch interfaces, among other key enabling technologies, including flat-panel displays. Now virtual and augmented reality (VR/AR) devices are emerging, benefiting from computer vision innovations in inside-out tracking and natural gesture control interfaces. The significance of this continuing transformation for the industry that SID serves cannot be overstated. It's no longer about just designing and manufacturing a display screen that shows information. The entire system must be considered together—from vision, voice, and touch sensors that see, hear, and feel human inputs, to AI algorithms that understand human intention and enable the system to respond through the display. Engineers must understand the entire information flow and architect integrated systems from sensors to displays. The value that SID provides to our members is in supporting them in delivering greater value to their customers, so we owe it to them to expand our focus to encompass end-to-end imaging and vision technologies. I explained this drive to “redefine the ‘I’ in SID” in the last issue of Information Display (ID). With that as a backdrop, I would like to share some highlights of the Display Week 2023 program. Display Week is well known for its expansive exhibition and international symposium, and this year is no exception. The International Technical Symposium will feature top scientists, researchers, and academics with presentations and hundreds of papers from around the world. Special topics include AR/MR/VR, machine learning, and outdoor displays, as well as sensors integration and multifunctional displays—a new topic for 2023 in line with our expanding focus. We also are debuting the SID Imaging and Vision Special Event with speakers from organizations such as Meta, Microsoft, Sony, UCLA, HKU, and OpenCV. The event will focus on advances in image sensing, computer graphics, AI-aided computer vision, and machine learning techniques that enable new intelligent and interactive systems and applications. On the exhibition floor, the largest display and imaging companies will demonstrate advances in AR/VR, OLED, microLED, miniLED, LCD, quantum dots, automobile technology, wearables, digital signage, ePaper, vision sensors, and more. In the Innovation Zone (I-Zone), attendees can see prototypes and pre-market products from small companies, startups, and research organizations. An amazing part of Display Week is how it honors people who have made significant contributions to the industry. The 2023 Honors and Awards will be presented to a select group of innovators who have furthered research, pushed the boundaries of discovery, and improved technology. To read more about the prizes and honorees, see the March/April 2023 issue of ID. Other special events and programs include the SID/DSCC Business Conference, CEO Forum, Women in Tech event, Young Leadership Conference, SID Job Fair, and much more. There is something for everyone in the ecosystem. Looking to the next 60 years, SID will continue to evolve as the world evolves, and Display Week will continue to be the place to experience the latest innovations in electronic displays and imaging technologies. We're excited to bring these important developments to our community. Please reach out with ideas and comments to [email protected]. I hope to see you at Display Week! Achin Bhowmik is the president of the Society for Information Display. He can be reached at [email protected].

Electrical Vehicle Wireless Charging System

As electric vehicles become increasingly popular, the need for convenient and efficient charging solutions is becoming more urgent. One promising technology that addresses this need is wireless car charging. This technology allows vehicles to charge without the need for physical connections or cables, making it a more seamless and user-friendly solution for drivers. However, there are still challenges to be overcome, such as optimizing charging efficiency and ensuring compatibility with different vehicle models. In this paper, we present a comprehensive review of the current state of wireless car charging technology, including its benefits and limitations, and propose new approaches to address these challenges. Our research will help pave the way for a more sustainable and convenient future for electric vehicles. Key Words: Efficient Charging, Charging Efficiency, Charging Technology, Electrical Vehicles.

Abstract 4097: Functional and translatable efficacy of CAR-T cells targeting B cell lymphomas

Abstract Chimeric Antigen Receptor (CAR)-T cell therapy has achieved great success in treating a variety of liquid tumors, in particular CD19+ lymphomas. CAR technology is advancing rapidly, with notable improvements in efficacy, safety and complexity as human ingenuity seeks solid tumor applicability. Here we describe how to efficiently generate, expand and validate in-vitro and in-vivo efficacy of a clinically tested CD19-specific CAR. HLA-A*02:01+ CD3+ T-cells were transduced to express a CD19-CAR to >80% efficiency (in 5 donors) and reproducibly expanded to >68 fold over a 19 day period. In-vitro studies were used to assess tumor killing ability, utilizing a HLA-A*02:01+ patient derived xenograft (PDX) CD19+ cell line, alongside human cancer cell lines RAJI (CD19+) and K562 (CD19-). Analysis revealed the potent ability of CD19-CAR-T cells to specifically target CD19+ cells in a dose dependent manner. Supernatant analysis showed highly elevated IFNγ concentrations, identifying enhanced activation of CAR-T cells in the presence of CD19+ target cells only. In-vivo survival studies were completed to identify translatability & efficacy in both systems. Mice were inoculated with CD19+ cells to produce multiple tumor burdened models (RAJI, JEKO-1 & PDX4009). This demonstrated high efficacy of CAR-T at targeting both tumor cell line RAJI or PDX in vivo, with almost complete tumor regression seen in both models. JEKO-1 tumors saw progression slowed in comparison to non-transduced or no treatment controls however due to the nature of this cell line (highly tumorigenic) the level of tumor regression was not matched to the other models. Cell bio-distribution was assessed with CAR-T cells tracking successfully to the tumors as well as the highly vascularized tissues, which was expected and consistent with previous data. These assays can be translated to any CAR construct aimed at targeting either solid or hematological tumors and demonstrates an integrated platform for translating CAR research that can be utilized end to end. Citation Format: Lauren Kelsey, Karin Latta, Joey Kolb, Bincy John, Ilona Aylott, Daniel Rocca, Alexandru Bacita, Christopher Kirkham, Patrick Walters, Charlotte Humphery, Lorena Sueiro Ballesteros, Jezrom Self-Fordham, Louise Brackenbury, Chassidy Hall, Harris David, Julia Schueler, Robert Nunan. Functional and translatable efficacy of CAR-T cells targeting B cell lymphomas. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4097.

PELATIHAN DAN PERAWATAN KENDARAAN RODA EMPAT NEGLA UNTUK MENINGKATKAN KUALITAS PELAYANAN NEGLA SARI TANGERANG

The car repair business does not only rely on car maintenance service activities, but is supported by comfortable facilities and excellent service. The "Koh Aloy" car repair shop is a micro-small business with a number of limitations, including limitations on the types of service services, infrastructure facilities, sources of funds, and the ability to manage a repair shop. The objectives of this training include car service with injection technology, application of a workshop administration system and management of the Occupational Health and Safety Environment (LK3). The target subjects of the activity are workshop managers and mechanics. Evaluation of training participant satisfaction is carried outthrough customer satisfaction measurement instruments. The results of the training and mentoring activities for small and micro car repair shops in Negla Sari Tangerang to increase the competitiveness of this service are; 1) the service activities carried out at the "Koh Aloy" Car Repair Shop, received a pretty good response. The indicators are that both the manager and the mechanics of the partner car repair shop participate in the activity from start to finish; 2) training and mentoring activities for car repair shop managers and mechanics, have increased knowledge about injection technology and service for injection technology cars, entrepreneurship and marketing management, workshop administration systems, recording financial reports, and environmental management, occupational safety and health (LK3 ); and 3) training and mentoring activities have provided provisions for car repair shop managers and mechanics to be better prepared in providing injection car service services.

Study on practical model for Indian 2 W automobile manufacturing

The country's economic prosperity is mostly fueled by the automobile sector. Two-wheeled (2 W) vehicles are essential for urban transportation in India and further low-income Asian nations because they offer millions of people access to economical mobility. Aside from having high emissions and road fatalities per passenger kilometre, these cars also have a substantial influence on urban transportation impacts and energy usage. In light of the popularity of these cars and their effects on transportation, technology plays a significant role. This essay examines the viability of 2 W car technology in India, as well as contributing elements including market dynamics and environmental regulations. Today, the nation is home to practically all of the major auto manufacturers in the world. The rise of the automotive industry is seen in the trend of vehicle manufacturing. While the Indian 2 W car sector has transformed in terms of product development, innovation, and quality in response to consumer expectations and conservation concerns, the study demonstrates several technologies. The automotive industry’s manufacturing trend is rising at the moment.

Research on Dynamic Recognition and Tracking Technology for Complex Scenes of Automatic Driving

<p>With the development of automobile technology, intelligent vehicle and automatic driving technology will make due contributions to reducing traffic accidents. This paper aims to improve the dynamic identification and tracking technology in the current intelligent vehicle and automatic driving. First, it is improved based on the MobileNet V2 backbone network, and then a new tracking model framework is designed combining with the SiamRPN single target tracker. Secondly, it integrates space-time tracking clues to improve the stability and robustness of the algorithm. Finally, it constructs a pedestrian dynamic identification algorithm based on the dynamic pedestrian factors in the driving process. Through the training of data sets and video tracking experiments, the performance of the algorithm in this paper is proved quantitatively and qualitatively.</p> <p> </p>