Beyond the Limitations of Existing Safety Designs Have you ever dozed off on a bus or subway, only to suddenly hit your head against the window? Even a small impact can make you flinch and recoil in surprise. Impacts like these occur everywhere in our daily lives. The problem is that in major impacts, such as car accidents, our bodies may not recover easily. This is precisely why automotive and safety design are so crucial. But do you think current automotive safety designs have protected everyone 'equally'? A research team at the University of Michigan Transportation Research Institute (UMTRI) is planning an exciting project that aims to break existing stereotypes about automotive safety design. This research is titled 'Adaptive Safety Design for Injury Prevention'. This study, part of the 2026 Summer Research Program (SURE), goes beyond simply making cars stronger or adding new technologies. Its objective is to maximally reflect the diversity of 'people' actually inside the car. Until now, Finite Element (FE) human body models used in automotive safety tests have been criticized for being primarily developed and validated based on average-sized males. These models fail to adequately reflect the significant morphological and biomechanical variations within the population. For instance, the physical characteristics of children, the elderly, women, or individuals with non-standard body types can differ significantly from those of an average male, but existing models have not properly accounted for these differences. The research team aims to address this issue by developing new, precise models that reflect the diverse morphological characteristics and biomechanical differences of the human body. The core objective of this project is to develop parametric human body shape and FE models that account for geometric variations across the population. Through this, they plan to conduct population-based simulations to evaluate the impact of human morphological variations on human body responses not only in car crashes but also in sports-related head impacts, and to validate the feasibility of establishing improved occupant protection strategies. The research team is developing 3D anatomical models that reflect the diverse body shapes of the real population, utilizing Statistical Shape Modeling and Mesh Morphing techniques based on medical imaging data. Specifically, student researchers will be tasked with generating 3D anatomical models from CT data. These models will be applied to virtual crash simulations and biomechanical analyses to evaluate and improve occupant protection strategies. Leveraging this will not only make car crash research much more realistic, but also enable the proposal of design directions to protect populations with diverse body types and characteristics. Professor Jingwen Hu of UMTRI describes this research as "a significant turning point in introducing a personalized approach to automotive safety design." Human Body Models Reflecting Diversity: A Paradigm Shift in Safety This multidisciplinary project aims to advance occupant safety and personalized transportation research by integrating engineering, data analysis, and biomechanics. This approach, combining medical image analysis techniques with engineering simulations, has the potential for applications beyond just automotive safety, extending to areas such as fall prevention and sports injury prevention. Indeed, this research is part of a comprehensive effort to address the global problem of unintentional injuries, including car crashes, falls, and sports-related accidents. Another reason this research is drawing attention is its utilization of Artificial Intelligence (AI) and Large Language Models (LLMs). The research team aims to analyze the complex correlations between driver behavior and vehicle accidents through AI, and to derive more effective accident prevention strategies. Specifically, AI and LLMs will be used to decipher the interrelationships between complex factors such as driver cognitive load, engagement in secondary tasks, environmental conditions, and road characteristics. This will enable the identification of root causes for distraction-related collisions and the proposal of more effective countermeasures. For instance, if AI analysis reveals patterns of increased driver cognitive load and decreased attention in specific road environments or driving conditions, the problem can be addressed by providing appropriate warnings through in-vehicle systems or by improving road design. This goes beyond merely applying AI technology to individual vehicles; it is drawing attention as a new paradigm in automotive safety because it helps to understand human behavior more deeply and use this understanding to improve design and policy. The research team emphasizes that this will be a significant case study for applying AI to explore driver behavior and safety impacts. Of course, there are counterarguments to this type of research. Some
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