Innovative applications of big data and simulation technologies in the optimization design of crash safety for autonomous vehicles: a systematic review from a biomechanical aspect

Abstract

The rapid development of autonomous vehicles (AVs) has intensified the demand for advanced strategies to guarantee crash safety in increasingly complex traffic environments. Traditional design methods, reliant on physical crash tests and limited empirical data, are insufficient to capture the full spectrum of biomechanical responses during collisions. This systematic review synthesizes recent advances in the integration of big data analytics and simulation technologies for optimizing collision safety, with a particular focus on biomechanical modeling. Big data enables the large-scale collection and analysis of heterogeneous data sources-including vehicle sensors, physiological signals, and traffic dynamics-supporting the construction of high-fidelity injury prediction models. Simulation methods, such as finite element analysis (FEA), multi-body dynamics (MBD), and parametric optimization, facilitate precise evaluation of occupant kinematics, stress distributions, and tissue-level injury mechanisms. Furthermore, emerging applications of machine learning, digital twin systems, and biomimetic design demonstrate substantial potential for improving active and passive safety. This review highlights the synergistic role of biomechanics, data science, and simulation technologies in shaping the next generation of collision protection systems. Finally, it identifies key challenges— including data privacy, model accuracy, and computational efficiency — and proposes future directions toward multi-scale biomechanical modeling, AI-driven optimization, and cross-disciplinary integration for safer and more adaptive autonomous driving systems.