Car Class Research Articles

Gaussian Process Regression as a precrash velocity determination method–subcompact vehicle class

The following paper presents an innovative approach to determining vehicle precrash velocity when hitting an immovable obstacle facing forward. Precrash velocity is necessary in order to perform a crash reconstruction. It is needed for the time-space analysis of the events, as well as to assesscrash mitigation and to evaluate drivers’ technique and tactics. For this task, the authors are using Gaussian Process Regression (GPR). Such an approach offers a number of advantages over the currently used methods that prove to be outdated when considering modern vehicles. The mathematical model was trained on a database shared by the National Highway Traffic Safety Administration.. This database covers a large number of crash tests of different kind, however authors focus on frontal collisions of the subcompact car class. Due to low accuracy of linear methods used up till now, Authors developed an innovative approach to determine the EES parameter utilizing Gaussian process regression. The newly developed method is an effective and accurate way to determine the vehicle’s velocity and shows promising results, as is demonstrated in this paper.

AEPF: Attention-Enabled Point Fusion for 3D Object Detection.

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class's easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

Analysis of the mechanism of side impact of cars

Problem. Globally, car crashes are the major cause of death, killing 1.2 million people, and despite improvements in car safety, forecasts indicate that car crash deaths will increase significantly by 2030 due to the increase in the number of cars. Such a trend requires an increase in passive safety in the design of cars. It is also necessary to consider with these factors the most popular segments of SUV cars, which during an accident create more dangerous consequences in the event of a side impact, taking into account the mass and dimensions parameters. Goal. The aim is to conduct an analysis of the side collision mechanism of cars of different mass and dimensional parameters. Taking into account the trends in the sale of cars, identifying the most popular classes of cars in Ukraine both on the new car market and on the second-hand market, to further identify problems in testing cars according to various certification protocols. Methodology. The approaches to solving the tasks used in the work are based on the use of statistical data and comparative analysis of various methodologies and certification protocols. Results. Considering that scientific studies of frontal impact are presented by the scientific community in the broadest form, the study of the side impact of two cars is currently a relevant direction, taking into account the global trend towards the production of cars in the SUV segment, which exceed passenger cars in terms of mass and dimensions. In a road accident with a side impact of a passenger car and an off-road vehicle of the SUV segment, we will get a large difference in the height of the primary impact, which is 250 millimeters. That is, all the energy of the impact to the side of the passenger car falls not on the safety bar, but 250 millimeters higher, which will inevitably lead to fatal injuries to the driver and passenger. Unfortunately, the European NCAP and the US National Highway Traffic Safety Administration use moving barriers that are similar in terms of mass and dimensions to an average passenger car. Originality. The obtained results of the analysis of the mechanism of the car side collision make it possible to evaluate the current trend of the automobile market in terms of passive safety and, in particular, to conduct certification tests for side impact in a new way. Practical value. The obtained results can be recommended when studying the structural features of preparing and conducting crash tests.

Effects of expectation on auditory impression of car door-closing sounds

If consumers have preconceptions or expectations about a product, these may influence their evaluation of the product sound. In this study, a psychoacoustic experiment was conducted to investigate the effect of preconception about the product on the perceived quality of the product sound. First, participants were presented with information about three different classes of cars (kei car, economy car and luxury car), including pictures, prices and displacements, and were asked to imagine the door-closing sound of each class of car, and evaluate the auditory imagery of door-closing sounds recalled from the three types of car information using the semantic differentials method. Next, five door-closing sounds were presented, assuming that they were the sounds of each car type. Participants rated perceived quality of stimuli using the same method. As a result, the stimulus of a kei car presented as the door-closing sound of a luxury car was rated as more luxurious than the same stimulus presented as that of a kei car. Similar results were obtained for the stimulus of a luxury car. These results indicate the effect of preconceptions (or expectations) on the auditory impression of the door-closing sounds.

Analysis of the used passenger car market in Ukraine

The paper analyzes the used car market in Ukraine for the period 2017-2023. The author provides information on the volumes and structure of the used car market, analyzes the number of used cars sold from primary imports and re-registrations. It was found that the secondary car market has shown a rapid growth in the period from 2017 to 2023. In 2022, 69.37% more used cars were sold than in 2017. The share of imported cars in the used car market increased from 9.82% in 2017 to 37.75% in 2022. At the same time, the share of used passenger cars with re-registrations decreased from 90.18% in 2017 to 62.25% in 2022. The author collected statistics for the TOP-20 brands of used passenger cars according to the data of the websites "PLANETAVTO", "AUTO.RIA" and «RST» and analyzed 620 models of passenger cars by their sales volumes from May 2021 to May 2023. The paper calculated the growth rates of sales of the most popular used passenger cars in 2022-2021 and 2023-2022. According to the results of the study, the leaders of the Ukrainian used passenger car market were Volkswagen, Renault, VAZ and Skoda. The author studied the most popular models of used passenger cars, including Volkswagen Passat, Skoda Octavia, Volkswagen Golf, Ford Focus and Renault Megane, among others. It was found that the most popular types of bodywork for used passenger cars are sedans, hatchbacks and crossovers. Among the different types of passenger car classes, the most popular are mid-range and comfort class cars. The author pays special attention to environmentally friendly used passenger cars. The number of electric cars and hybrid models sold in 2021-2023 is 98,794 units, which is 7.81% of the total volume of sales of used passenger cars. The most popular models of used electric cars are Nissan Leaf, Volkswagen E-Golf, Chevrolet Bolt EV, and BMW i3. The most popular used hybrids are Toyota Camry, Hyundai Tucson, Toyota Corolla, and Nissan Qashqai. The author analyzed the factors that influence the development of the used passenger car market, including: changes in the economy, rising fuel prices, the introduction of new technologies in the automotive industry, the availability of loans and interest rates, the growing awareness of ecology and environmental standards, changes in legal norms and political decisions related to the automotive industry.

Detection of Foreign Objects Around the Railway Line with YOLOv8

This study proposes a deep learning-based method to detect foreign objects around the railway line. It is important to make this determination with high accuracy for rail transport safety, but traditional methods are disadvantageous in terms of time and cost. In the proposed method, the RailSem19 dataset was used, and a YOLOv8-based model was designed. YOLOv8 is a prominent algorithm in the literature with its fast and accurate object detection capability. In the study, the dataset was diversified using image enhancement techniques. The training, validation, and testing stages used manually labeled data for human and car classes. The training process was carried out through Google Colab and different YOLOv8 sub-architectures were evaluated. The results showed that the YOLOv8m sub-architecture had higher mAP50 values than the other sub-architectures and showed a successful performance in the validation phase. The YOLOv8m model was able to clearly distinguish people and cars around the railway line. The YOLOv8m sub-architecture achieved a mAP50 value of 88.8%. This study presents an automated and efficient method to improve rail transport safety. The high success of the YOLOv8-based model with the RailSem19 dataset can be considered an effective solution to detect potential risks around the railway line.

SHUNIT: Style Harmonization for Unpaired Image-to-Image Translation

We propose a novel solution for unpaired image-to-image (I2I) translation. To translate complex images with a wide range of objects to a different domain, recent approaches often use the object annotations to perform per-class source-to-target style mapping. However, there remains a point for us to exploit in the I2I. An object in each class consists of multiple components, and all the sub-object components have different characteristics. For example, a car in CAR class consists of a car body, tires, windows and head and tail lamps, etc., and they should be handled separately for realistic I2I translation. The simplest solution to the problem will be to use more detailed annotations with sub-object component annotations than the simple object annotations, but it is not possible. The key idea of this paper is to bypass the sub-object component annotations by leveraging the original style of the input image because the original style will include the information about the characteristics of the sub-object components. Specifically, for each pixel, we use not only the per-class style gap between the source and target domains but also the pixel’s original style to determine the target style of a pixel. To this end, we present Style Harmonization for unpaired I2I translation (SHUNIT). Our SHUNIT generates a new style by harmonizing the target domain style retrieved from a class memory and an original source image style. Instead of direct source-to-target style mapping, we aim for source and target styles harmonization. We validate our method with extensive experiments and achieve state-of-the-art performance on the latest benchmark sets. The source code is available online: https://github.com/bluejangbaljang/SHUNIT.

PEMANFAATAN YOUTUBE SEBAGAI MEDIA PEMBELAJARAN BAHASA INDONESIA KOMPETENSI MENYIMAK CERITA ANAK PADA SISWA KELAS V SEKOLAH DASAR

The purpose of this study was to improve the learning outcomes of Indonesian language students in class V at SD Negeri Caturtunggal I Depok, Yogyakarta Special Region in the competency of listening to children's stories by utilizing YouTube media. This study used the CAR class action research method. The subjects in this study were fifth grade students at SD Negeri Caturtunggal I Depok, Special Region of Yogyakarta. Data were collected by observation, tests, and interviews. Data were analyzed with an interactive analysis model including reduction, presentation, and drawing conclusions. The research results prove that; 1) there is a change in lesson planning, from conventional learning to modified learning designed by researchers by applying YouTube media in learning. This can be seen from the learning in pre-action, cycle I and cycle II. 2) There is a change in the implementation aspect of the learning action. All forms of learning problems faced by teachers have been corrected so that the learning process can run well. In other words, the application of YouTube media in learning Indonesian for the material "Listen to Children's Stories" can improve the quality of the learning process. 3) The application of YouTube media in Indonesian language learning material "Listen to Children's Stories" can improve student learning outcomes. The increase in learning outcomes was marked by an increase in the number of students who reached the limits of completeness from pre-action, cycle I, to cycle II.

Multi-Modal and Multi-Scale Fusion 3D Object Detection of 4D Radar and LiDAR for Autonomous Driving

Multi-modal fusion plays a critical role in 3D object detection, overcoming the inherent limitations of single-sensor perception in autonomous driving. Most fusion methods require data from high-resolution cameras and LiDAR sensors, which are less robust and the detection accuracy drops drastically with the increase of range as the point cloud density decreases. Alternatively, the fusion of Radar and LiDAR alleviates these issues but is still a developing field, especially for 4D Radar with a more robust and broader detection range. Nevertheless, different data characteristics and noise distributions between two sensors hinder performance improvement when directly integrating them. Therefore, we are the first to propose a novel fusion method termed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M^{2}$</tex-math></inline-formula> -Fusion for 4D Radar and LiDAR, based on Multi-modal and Multi-scale fusion. To better integrate two sensors, we propose an Interaction-based Multi-Modal Fusion (IMMF) method utilizing a self-attention mechanism to learn features from each modality and exchange intermediate layer information. Specific to the current single-resolution voxel division's precision and efficiency balance problem, we also put forward a Center-based Multi-Scale Fusion (CMSF) method to first regress the center points of objects and then extract features in multiple resolutions. Furthermore, we present a data preprocessing method based on Gaussian distribution that effectively decreases data noise to reduce errors caused by point cloud divergence of 4D Radar data in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$x$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$z$</tex-math></inline-formula> plane. To evaluate the proposed fusion method, a series of experiments were conducted using the Astyx HiRes 2019 dataset, including the calibrated 4D Radar and 16-line LiDAR data. The results demonstrated that our fusion method compared favorably with state-of-the-art algorithms. When compared to PointPillars, our method achieves mAP (mean average precision) increases of 5.64 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 13.57 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> for 3D and BEV (bird's eye view) detection of the car class at a moderate level, respectively.

An object detection algorithm combining semantic and geometric information of the 3D point cloud

Accurately detect vehicles or pedestrians from 3D point clouds (3D object detection) is a fast developing research topic in autonomous driving and other domains. The fundamental component for feature extraction in 3D object detection is Set Abstraction (SA), which can downsample points while aggregating points to extract features. However, the current SA ignores the geometric and semantic properties of point clouds and may miss to detect remote small objects. In this paper, FocusSA is proposed, which consists two modules for enhancing useful feature extraction in the SA layer to improve 3D object detection accuracy. At first, Focused FPS (FocFPS) is proposed to evaluate the foreground and boundary scores of the points and reweighs the Furthest Point Sampling (FPS) using the evaluated scores to retain more contextual points in downsampling. Then a Geometry-aware Feature Extraction (GeoFE) module is proposed to add geometric information to enrich the awareness of geometric structure in feature aggregation. To evaluate the performances of the proposed methods, we conduct extensive experiments on three difficulty levels of Car class in KITTI dataset. The experimental results show that on “moderate” instances, our results outperform the state-of-the-art method by 1.08%. Moreover, FocusSA is easy to be plugged in popular architectures.

RESULTS OF MODELING OF COMPONENTS AND WORKING PROCESSES OF A HYBRID CAR WITH AN ASYNCHRONOUS MOTOR

The work presents simulation models of the control and monitoring system, the study of the dynamics of the parameters of the elements of a hybrid car using simulation modeling in Matlab / Simulink environment is performed. A series-parallel drive of driving wheels is considered. The electric drive is carried out from an asynchronous electric motor. The model includes energy consumption regulation and control subsystems, calculation and state control of the battery, internal combustion engine, electric drive, mechanical model of the car, simulator of driver's influence on the fuel pedal, driving mode control system. The changes in the operating parameters of the hybrid vehicle components and the vehicle itself on the given driving modes are presented. The results obtained correspond to the real values for the selected class of cars.

Real Objects Understanding Using 3D Haptic Virtual Reality for E-Learning Education

In the past two decades, there has been a lot of work on computer vision technology that incorporates many tasks which implement basic filtering to image classification. The major research areas of this field include object detection and object recognition. Moreover, wireless communication technologies are presently adopted and they have impacted the way of education that has been changed. There are different phases of changes in the traditional system. Perception of three-dimensional (3D) from two-dimensional (2D) image is one of the demanding tasks. Because human can easily perceive but making 3D using software will take time manually. Firstly, the blackboard has been replaced by projectors and other digital screens so such that people can understand the concept better through visualization. Secondly, the computer labs in schools are now more common than ever. Thirdly, online classes have become a reality. However, transferring to online education or e-learning is not without challenges. Therefore, we propose a method for improving the efficiency of e-learning. Our proposed system consists of two-and-a-half dimensional (2.5D) features extraction using machine learning and image processing. Then, these features are utilized to generate 3D mesh using ellipsoidal deformation method. After that, 3D bounding box estimation is applied. Our results show that there is a need to move to 3D virtual reality (VR) with haptic sensors in the field of e-learning for a better understanding of real-world objects. Thus, people will have more information as compared to the traditional or simple online education tools. We compare our result with the ShapeNet dataset to check the accuracy of our proposed method. Our proposed system achieved an accuracy of 90.77% on plane class, 85.72% on chair class, and car class have 72.14%. Mean accuracy of our method is 70.89%.

Penerapan Model Pemilihan Kartu (Card Short) Dalam Pembelajaran Fiqih Untuk Meningkatkan Keaktifan Dan Prestasi Belajar Siswa Siswa Kelas V C MIN 2 Indragiri Hulu

This study aims to increase student achievement in class V C MIN 2 INHU in the subject of Jurisprudence through a card sort model, and this research uses CAR research methods or class action research (classroom action research). with the learning model type sorting cards (card sort) managed to improve student achievement. In cycle 1 students of class V C obtained an average score of 71.72 with the lowest score being 60 and the highest score being 95. There were also 17 students who had not yet reached the KKM with a classical completeness score of only 41%. In cycle II the average score was 79.82, the lowest score was 70 and the highest score was 100. Meanwhile, there were only 4 students who had not reached the KKM with classical completeness of 88%. succeeded in increasing the activity and achievement of student learning. This can be seen in the development of each cycle that is held. Namely in cycle 1 only 41% and increased in cycle II 86%. Based on the average value per cycle which is in the very good category

Precrash vehicle velocity determination using inverse system and tensor product of Legendre polynomials - subcompact car class

Presented paper discusses new approach to EES parameter determination in frontal car crash based on the tensor product of Legendre polynomials. In this paper Subcompact Car Class was analyzed using that method. Data that was used to perform analyses introduced in this paper was taken from National Highway Traffic Safety Administration (NHTSA) database. Such database consists of considerate number of test cases along with various information including vehicle mass, crash velocity, chassis deformation etc. New approach to the problem of determining the EES parameter was necessary due to the low accuracy of the currently used methods. Linear models used up till now for accident reconstruction show significant error as the relationship between mass, velocity and deformation cannot be well approximated with a flat plane. Proposed model produces better results, because of the nonlinear dependence of said parameters. This paper also includes a calculation example presenting a comparison of linear and nonlinear method on an actual crash test.

BUILDING A LOGICAL NETWORK FOR SOLVING THE PROBLEM OF CAR RENTAL BY MEANS ALGEBRA OF FINITE PREDICATES

The article is devoted to the research of the tools of algebra of finite predicates for the system analysis and formalization of the task of automating car rental according to the selected parameters. In particular, the process of optimal car selection depends on the following parameters: car class, brand, availability of driver, type of trip and its duration, fuel type, tariff type, as well as season and weather conditions. Each of these criteria has its own area of definition, where you need to take into account all the relationships and influences between the values of the entered variables (criteria). The aim of the work is to increase the speed of data processing in the problem of car rental by dividing the input multi-place ratio into a binary composition. The technique is based on the means and methods of algebra of finite predicates. Introduction of the predicate of object recognition in the specified subject area allowed to formally describe data of any type, and the applied method of construction of logical networks provides increase in speed of information processing due to parallelization of processing processes. Thus, a complex multi-place relation was divided into a composition of binary relations described in the language of predicate algebra, taking into account the detailed system analysis of the subject area. A scientific novelty is the constructed mathematical model of the car rental problem, which is represented by a predicate that depends on thirteen variables. This predicate is characterized by a system of twelve binary relations, which are represented in the article by dual graphs and formulas of the corresponding predicates. The model predicate is a composition of all constructed binary predicates. The practical significance is due to the logical network built on the basis of a mathematical model, which allows from the relationship "many to many" to move to the relationship "to each other" and parallelize the process of information processing. The result is a logical network of car rental problems, which works iteratively until it receives stable results in two consecutive steps and allows you to solve problems of analysis and synthesis for car rental according to selected parameters.

Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset.

A robust visual understanding of complex urban environments using passive optical sensors is an onerous and essential task for autonomous navigation. The problem is heavily characterized by the quality of the available dataset and the number of instances it includes. Regardless of the benchmark results of perception algorithms, a model would only be reliable and capable of enhanced decision making if the dataset covers the exact domain of the end-use case. For this purpose, in order to improve the level of instances in datasets used for the training and validation of Autonomous Vehicles (AV), Advanced Driver Assistance Systems (ADAS), and autonomous driving, and to reduce the void due to the no-existence of any datasets in the context of railway smart mobility, we introduce our multimodal hybrid dataset called ESRORAD. ESRORAD is comprised of 34 videos, 2.7 k virtual images, and 100 k real images for both road and railway scenes collected in two Normandy towns, Rouen and Le Havre. All the images are annotated with 3D bounding boxes showing at least three different classes of persons, cars, and bicycles. Crucially, our dataset is the first of its kind with uncompromised efforts on being the best in terms of large volume, abundance in annotation, and diversity in scenes. Our escorting study provides an in-depth analysis of the dataset’s characteristics as well as a performance evaluation with various state-of-the-art models trained under other popular datasets, namely, KITTI and NUScenes. Some examples of image annotations and the prediction results of our 3D object detection lightweight algorithms are available in ESRORAD dataset. Finally, the dataset is available online. This repository consists of 52 datasets with their respective annotations performed.

PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE TALKING STICK UNTUK MENINGKATKAN KETERAMPILAN BERBICARA DAN PRESTASI BELAJAR SISWA

This research is conducted to describe the application of talking stick type of cooperative learning model to increase the speaking skill and the students’ achievement at English Lesson of the tenthgrade at SMA Negeri 1 Kikim Timur. The research methods that used were Classroom Action Research and Quasi Experiment. The subjects of this research were students of the tenth grade SMA Negeri 1 Kikim Timur at the second semester of the academic year 2020/2021,for CAR class was X IPS 1,Experiment class X IPS 3,and Control Class X IPS 2. Each class totaling 27 students. The Research instruments used observation sheet and test. The data analysis used the average score analysis and test consisting of inter-cycle different test and unrelated two sample different test. The result showed that the application of cooperative type of learning model talking stick can increase the speaking skill and the students’ achievement of the tenthgradestudents of English lesson at SMA Negeri 1 Kikim Timur

PENERAPAN MODEL PEMBELAJARAN COOPERATIVE TIPE TEAM ACCELERATED INTRUCTION (TAI) UNTUK MENINGKATKAN TANGGUNG JAWAB DAN PRESTASI BELAJAR

This study aims to describe the application of the Cooperative Learning Model Type Team Accelerated Intruction (TAI) learning model and to describe the effectiveness of the Cooperative Learning Model Type Team Accelerated Intruction (TAI) to increase the responsibility and learning achievement of students in class XI SMA Negeri 1 Kikim Timur Lahat. The research method used was Classroom Action Research (PTK) and quasi-experimental. The research subjects were students of class XI IPS 1 semester one academic year 2020/2021 SMA Negeri 1 Kikim Timur Lahat. The quasi-experimental sample is class XI IPS 3 and class XI IPS 2. The number of students for the CAR class is 31 students, for the Experiment class there are 30 students, and for the control class there are 30 students. The data collection method in this study used an observation sheet for the implementation of the Cooperative Learning Model Type Team Accelerated Intruction (TAI), an observation sheet for Increasing Responsibility, and a learning outcome test. The data analysis used tests which consisted of different tests between cycles and different tests of two unrelated samples. The results showed that the application of the Cooperative Learning Model Type Team Accelerated Intruction (Tai) can increase the responsibility and learning achievement of students in class XI SMA Negeri 1 Kikim Timur Lahat and effectively improve student learning outcomes in Economics subjects

Nonlinear method of precrash vehicle velocity determination based on tensor product of Legendre polynomials - luxury class

Presented paper discusses a new, nonlinear approach to EES (Equivalent Energy Speed) parameter determination in frontal car collisions. This method is based on tensor product of Legendre polynomials and in this case considers Luxury car class. Methods that are used up till now are based on a linear dependency between mass, velocity and deformation. This is of course a simplification that was necessary, due to limitation in computation power of computers when this method was introduced decades ago. The contemporary resources allowed Authors to develop a much more sophisticated method. The mathematical model was developed using data shared by National Highway Traffic Safety Administration (NHTSA). This database covers a large number of test cases along with various information including vehicle mass, crash velocity, chassis deformation etc. New method proves to be more accurate than the currently used approach utilizing linear dependency of deformation force and deformation of the vehicle.

Preclinical Development and Evaluation of Allogeneic CAR T Cells Targeting CD70 for the Treatment of Renal Cell Carcinoma.

These findings demonstrate the efficacy and safety of fratricide-resistant, allogeneic anti-CD70 CAR T cells targeting renal cell carcinoma and the impact of CAR epitope on functional activity. See related commentary by Adotévi and Galaine, p. 2517.