The purpose of this study is to predict the dynamic behavior of clamshell hood system on the harsh road driving condition, and secure the safety and durability performance of the system. The equation of motion of hood system is derived and the numerical analysis is implemented to obtain the lateral movement of the hood system. Also, the actual Belgian road test results are correlated to the predicted ones, and confirm the reliability of the system. Then, the parameter study is conducted to figure out the sensitive factors to affect the dynamic behavior, and the engineering design guide to make the system robust to confine the minimum friction force generated from hood latch and maximum hood weight is suggested from this research.

Recently, the importance of simulation in a virtual driving environment as well as real road-based tests for autonomous vehicle testing is increasing. Real road tests are being actively conducted at K-City, an autonomous driving test bed located at the Korea Automobile Safety Test & Research Institute of the Transportation Safety Authority. In addition, the need to advance the K-City virtual driving environment and build a virtual environment similar to the autonomous driving system test environment in real road tests is increasing. In this study, for K-City of Korea Automobile Safety Test & Research Institute, using detailed drawings and actual field data, K-City virtual driving environment was advanced, and similarity verification was verified through comparative analysis with actual K-City.

The registration rate of eco-friendly vehicles, such as hydrogen vehicles, is increasing rapidly, however, few first responders have experienced related accidents. Accident scenarios at hydrogen refueling stations and hydrogen vehicles on a road were investigated, and the relative importance of each scenario was analyzed using AHP analysis. Leakage, jet flame, and explosion that occurred inside and outside the hydrogen refueling station were reviewed, and the hydrogen gas explosion in the compartment showed the highest importance value. In case of the hydrogen vehicle, traffic accident statistics and actual accidents were used. It was analyzed that the hydrogen vessel explosion on the road due to the failure of TPRD and the leakage in the underground parking area were difficult to respond. The developed accident scenarios are expected to be used for first responder training.

The purpose of this study is to predict how the actual accident changes by reconstructing the accident with an Autonomous Emergency Braking system (AEB) based on the actual accident of the LTAP-OD (Left Turn Crossing Path - Opponent Direction) intersection. A virtual AEB sensor was developed, and 150 head-on collision accident reports were secured to the insurance company to reconstruct the accident. As a result of the accident type analysis, a total of 13 types of head-on collision accidents were derived, and it is the LTAP-OD intersection accident with the highest frequency. In the LTAP-OD intersection accident, the simulation was conducted by applying the virtual AEB of each vehicle, the accident rate decreased by 90% or more when the AEB of the left-turn vehicle was applied, and the accident rate decreased by 50%. In addition, the most frequent collision types in LTAP-OD accidents were the front bumper on the driver's side of a vehicle going straight and the front bumper on the passenger's side of a vehicle turning left.

In the commercialization stage of level 4 or higher autonomous driving, the need for new legal system related to drive safely has increased in order to meet the improved level of technological development. Especially human drivers should not be legally accountable for road safety in the era of autonomous vehicles and thus safety standards for operation of autonomous vehicles are significant. To address this issue, the German Road Traffic Act was revised in 2021, adding provisions corresponding to the commercialization of self-driving vehicle of level 4 and in the similar context the Japanese Road Traffic Ac was amended in 2022. This Article draws implications for legislative discussions on driving-related responsibilities of driverless autonomous vehicle to ensure driving safety in Korea through recent amendments in Germany and Japan.

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.

A study on the need for a safety system using driver's biometric information, vehicle automatic emergency braking system, and the e-call system that in the event of an unexpected situation such as loss of driving ability due to sudden physical abnormality of elderly drivers and drivers with health problems, and the improvement of laws for the spread of this system were studied.