Surrogate Assisted Pedestrian Protection Design via a Foundation Model Orchestrated Workflow

Researchers introduced the first foundation model-orchestrated workflow for crash safety design, enabling surrogate-assisted pedestrian protection exploration that reduces evaluation time from hours to seconds. The workflow integrates a surrogate model, multiobjective evolutionary search, a morphing-based geometry generator, and a natural-language interface, producing 35 safety-compliant alternatives in a single exploration. This approach demonstrates that foundation models can serve as integration layers between ML surrogates and physics-based simulation in safety-critical engineering.

arXiv:2606.17577v1 Announce Type: new Abstract: AI-driven engineering workflows face particular challenges in crash safety design: unlike aerodynamics, crash events involve highly nonlinear contact dynamics, material nonlinearity, and discrete state transitions that are difficult to capture with data-driven surrogate models. To the best of our knowledge, we present the first foundation model--orchestrated workflow for crash safety design that enables surrogate-assisted exploration for pedestrian protection, reducing evaluation time from hours per CAE simulation to seconds. The workflow integrates four components: 1 a surrogate trained on CAE crash simulations to predict pedestrian leg injury metrics from design parameters, achieving an average $R^2=0.87$ and providing distribution-free conformal prediction intervals; 2 multiobjective evolutionary search NSGA-II to discover diverse feasible parameter sets under user-specified constraints; 3 a morphing-based geometry generator that maps parameters to topology-preserving 3D shapes; and 4 a natural-language interface in which an LLM orchestrates the workflow and a vision--language model supports semantic comparison of generated designs. In an automotive front-bumper case study, the workflow produces 35 distinct safety-compliant alternatives from a single exploration, a process that would require weeks with conventional CAE iteration. These results suggest that foundation models can serve as integration layers between ML surrogates and physics-based simulation, helping bring AI capabilities to safety-critical engineering domains.