Study of the Far Side Occupant Behavior in Side Impact in the Case that the Occupants seated in Driver and Front Passenger Seats

Yoshinori Tanaka･Yashiro Matsui･Naruyuki Hosokawa･Masatoshi Usui (National Agency for Automobile and Land Transport Technorogy Natrional Trarfic Safety and Environment Laboratory)

We conducted the side impact experiments under the condition that the dummies were seated on driver and passenger seats in accordance with UNR95 test procedure.
As a result, in a kei-car, the heads of the occupants impacted with each other, and the HPC exceeded the threshold. In a small passenger vehicle, the head of the far-side dummy impacted with the shoulder of the near-side dummy.

Latest Technique in Airbag Folding Model

Rie Kodama･Akifumi Daidoh･Chiharu Murase･Kazuo Imura (Toyota Motor)

Recently, Virtual Testing (VT) has been introduced in automotive crash safety assessment. Car manufactures must have confidence that CAE model predicts occupant response in VT various load cases. Airbag model folding is a key factor for occupant restraint simulation, however, creating the folding model is technically complex and time consuming in the VT process. In this study, the factors consuming a significant amount of time in the creation of folding model was identified, and techniques to improve them were developed. As a result, a folding method was established in approximately 40% of the time compared to the conventional methods.

Evaluation of the Effects of Restraint Conditions on Pelvic Kinematics of Rear Seat Occupants in Frontal Impact

Toshiharu Azuma･Yuqing Zhao･Koji Mizuno (Nagoya University)･Kei Nagasaka･Takahiro Suzuki･Idemitsu Masuda (Suzuki Motor)

FE simulations reproducing frontal impact sled tests were conducted using crash test dummies (THOR, Hybrid III) and human body models (THUMS) in rear seats. An L18 orthogonal array was used to evaluate the effects of seatbelt anchor position, the presence of restraint devices, and seat pan angle on pelvic rearward rotation and forward displacement.

Generation of Vehicle Body Deformation Data for Occupant Lower Extremity Injury Prediction in Crash using Machine Learning

Kyohei Noguchi (University of Yamanashi)･Kei Nagasaka･Idemitsu Masuda (Suzuki Motor)･Yuta Yokoyama (Diver Technology Corporation)･Hirofumi Sugiyama (University of Yamanashi)･Shigenobu Okazawa (University of Yamanashi/Diver Technology Corporation)

To develop a machine learning model for predicting lower extremity injuries of occupants based on dashboard deformation behavior during a crash, time-series data of various dashboard deformations are required as input for numerical analysis. As a preliminary step, we predict the time-series deformation of simplified structural components. Numerical experiments are conducted to investigate the effects of different input factor combinations and data structures. Finally, the performance of the proposed models is evaluated.

Safety performance evaluation of transport wheelchairs with simplified locking systems using sled test

Keisuke Fukuyama･Yoshihiro Sukegawa (JARI)･Yuuya Ueda (JAMA)

In Japan, the number of wheelchair users is expected to increase further due to the aging population. For this reason, there is a need for the standardization of simple and reliable wheelchair locking systems in vehicles and the dissemination of transport wheelchairs . In this report, we evaluate transport wheelchairs equipped with simplified locking systems using the sled tests in accordance with ISO standards, and discuss the required strength performance.