• Journal of Auto-vehicle Safety Association
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• v.8 no.2
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• pp.11-16
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• 2016

The objective of this study was to construct the spring-mass models for the car-to-car offset frontal impact crash. The SISAME software was utilized to extract the spring-mass models using the data from the offset frontal crash test. The spring-mass model of the passenger car could effectively approximate the crash characteristics for the offset frontal barrier impact and the car-to-car offset frontal impact scenarios.

• Journal of Auto-vehicle Safety Association
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• v.9 no.2
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• pp.20-25
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• 2017

KNCAP is a program to evaluate the automobile safety, providing consumer vehicle safety assessment results. The safety evaluation tests are Frontal Impact, Offset Frontal Crash, Side Crash, Side Pole Crash, Rear Impact. This is the study of the offset frontal impact safety evaluation. Currently, IIHS is performing a small overlap test. NHTSA plans to implement the oblique moving deformable barrier test. Euro-NCAP plans to implement a mobile frontal impact test. Simulation is used to compare occupant behavior and injury. We have investigated whether the introduction of the test at KNCAP is necessary. The dummy model used in the simulation was the 50th percentile male Hybrid III dummy.

• Journal of Auto-vehicle Safety Association
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• v.9 no.1
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• pp.13-18
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• 2017

In recent years, NCAP regulations of many countries have induced automaker to improve the vehicle crashworthiness. But, the current NCAP regulations don't cover all types of traffic accidents. And rapid-increasing market share of compact cars and SUVs has brought for both consumer and automaker to pay more attention on crash compatibility. So, many countries have tried to develop the new crash test mode and update the present crash test mode. Especially, Euro NCAP has been developing a new impact protocol of the car-to-car frontal offset impact including the crash compatibility assessment. There are plans to introduce this new protocol in 2020, and it will be replaced the current Euro NCAP frontal offset impact. The test dummy in the front seats of this new test mode will be changed from 50% Hybrid-III male to 50% THOR male. This paper will address the vehicle responses, the occupant responses and the vehicle compatibility performance from a full vehicle crash test using the new car-to-car frontal offset test protocol of Euro NCAP.

• Journal of Auto-vehicle Safety Association
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• v.8 no.3
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• pp.5-9
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• 2016

The objective of this study was to find the factors affecting the results of full frontal barrier impact test for the NCAP (New Car Assessment Program). To find the factors, the frontal NCAP test results of the NHTSA (National Highway Traffic Safety Administration) were utilized. The three tested vehicle were same model year. It was observed the second peak value of barrier force affected the occupant injury risk. As the second peak value of the barrier force increases, the injury risk of the driver side occupant increases as well.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.6
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• pp.179-187
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• 2004

In this study, for a safety assessment of wheelchair occupant in frontal and side impact of wheelchair loaded vehicle, a sled impact test was perfumed. Each test was carried out total 6 times, by using Hybrid III 50th-percentile male dummy in light weight and electric wheelchair. We estimate MC(Motion Criteria), CIC(Combined Injury Criteria), HIC(Head Injury Criteria), HNIC(Head and Neck Injury Criteria) based on measured data. Through this study, we make an assessment of risk analysis of wheelchair occupant and wheelchair. Through this study, safety standard of wheelchair is to be evaluated.

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.31-38
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• 2020

This study is to develop the prediction model for the HIC15 in frontal vehicle crash tests. The 28 frontal impact test results of the MY2019 and MY2020 USNCAP are utilized. The metrics for evaluating the crash pulse severity such as moving average acceleration, Restraint Quotient (RQ) and ride-down efficiency are reviewed to find out whether the metrics can predict the HIC15. It is observed that the R² values based on the linear regression of all pairs between the existing metrics and the occupant injuries such as the HIC15, 3 ms chest g's and chest deflection are very low. In this study, using the vehicle crash pulses, the linear regression model for estimating the HIC15 is developed. The vehicle crash pulse is splitted seven 10 ms intervals in 70 ms after impact for extracting the average accelerations in each intervals. The prediction model can predict effectively not only the HIC15 but also the maximum head g's, chest deflection and 3 ms chest g's of 13 vehicles out of 28 vehicles.

• Journal of Auto-vehicle Safety Association
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• v.11 no.4
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• pp.6-15
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• 2019

Since introducing the offset frontal impact test in EuroNCAP in 1997, the vehicle has been constantly changing according to its usage and purpose. As of 2019, many vehicles have been released to the public, which has led to a large structural mass difference between small, medium and large vehicles. Also, the geometry of the front of the vehicle is completely different for each vehicle and tends not to be perfectly aligned at frontal collisions. The difference in mass of each of these vehicles and less performing structures for offset crashes have led to dramatically worse outcome in a car to car offset frontal impact tests. Even though a decade later passenger cars have become much safer due to consumer test programs and regulatory requirements, the aggressiveness and compatibility that can cause damage to the opponent car in the event of car to car collision is not considered in the above-mentioned section, and therefore much improvement is needed. After many years of study to solve this problem, EuroNCAP has developed a new mode MPDB offset front test that considers the aggressiveness and compatibility that can affect the opponent cars that have collided. This paper introduces the development process of aggressiveness and compatibility evaluation method of MPDB in EuroNCAP which will be implemented from 2020. Several impact tests have been conducted at different test conditions to rate the vehicle structure performance only focused on aggressiveness and compatibility of MPDB.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.2
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• pp.49-57
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• 2008

The performance of crash cushion systems is certified through the full scale crash tests by the standard for installation and maintenance guidelines for roadside safety appurtenance. The impact severities of impacting vehicles in collision with crash cushion systems are rated by indices THIV and PHD. Crash test results are considered to study the performance of three crash cushion systems. In case of the frontal impact or the offset frontal impact, the results show that THIV values of three systems are very close to the threshold limit for the occupant protection. Also, the results show that PHD would be improper for the occupant protection performance index. In order to improve the occupant protection performance of crash cushions, ASI needs to be included in the impact severity index.

• Journal of Auto-vehicle Safety Association
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• v.15 no.1
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• pp.55-62
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• 2023

In this study, a chest deflection is predicted by introducing a deep learning technique with the results of the frontal impact of the USNCAP conducted for 110 car models from MY2018 to MY2020. The 120 data are divided into training data and test data, and the training data is divided into training data and validation data to determine the hyperparameters. In this process, the deceleration data of each vehicle is averaged in units of 10 ms from crash pulses measured up to 100 ms. The performance of the deep learning model is measured by the indices of the mean squared error and the mean absolute error on the test data. A DNN (Deep Neural Network) model can give different predictions for the same hyperparameter values at every run. Considering this, the mean and standard deviation of the MSE (Mean Squared Error) and the MAE (Mean Absolute Error) are calculated. In addition, the deep learning model performance according to the inclusion of CVW (Curb Vehicle Weight) is also reviewed.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.1
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• pp.211-216
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• 2003

A frontal offset impact model Oat can simulate the 40% offset frontal impact into deformable barrier regulated in EU Directive 96/79 EC has been developed. Engine rotation effects are also considered in the model. Distributed 11 masses and characteristics of 23 nonlinear springs comprising the model are determined based on both the stick-model analysis under the general specification of car and the dynamic characteristics of car structure. It is demonstrated that simulated acceleration-time curve for passenger part is in good agreement with test data obtained by NHTSA.