As the widespread adoption of autonomous vehicles is anticipated, a greater diversity of occupant postures is expected. However, current vehicle safety systems are not adequately designed to accommodate these varied postures, particularly relaxed seating positions, in which severe injuries have been reported during frontal collisions. Therefore, it is essential to investigate the biomechanical responses and injury tolerances of occupants in relaxed postures. In this study, the head kinematics and injury responses of the Hybrid-III and THOR anthropomorphic test devices, as well as the THUMS human body model, in a relaxed posture are analyzed using frontal impact sled simulations equipped with the sled system model developed in the previous study. The simulation results are evaluated through comparison with post-mortem human surrogate data. The findings of this study are expected to contribute to the fundamental design of new head restraint systems for human heads in autonomous vehicles.

This study addresses the limitations of traditional Failure Modes and Effects Analysis (FMEA), which heavily relies on expert judgment and lacks the ability to effectively incorporate unstructured failure history data such as warranty claims and maintenance records into the design stage. To overcome these challenges, we propose an automated FMEA framework based on a Retrieval-Augmented Generation (RAG) architecture integrated with a Local Large Language Model (LLM) in a secure, locally managed environment. The proposed system stores claim and test data in a vector database and leverages the LLM to retrieve and analyze relevant information, enabling automatic extraction of new failure modes and dynamic updates to FMEA documents. Additionally, the system recalculates Risk Priority Number (RPN) by adjusting severity, occurrence, and detection ratings when recurring failures are detected. To improve response quality, we applied prompt engineering and optimized chunking parameters during data retrieval. This research demonstrates the feasibility of achieving a life cycle-integrated quality enhancement framework throughout the product lifecycle while ensuring data security.