Surrogate Assisted Pedestrian Protection Design via a Foundation Model Orchestrated Workflow

• URL: http://arxiv.org/abs/2606.17577v1
• Date: Tue, 16 Jun 2026 06:27:38 GMT
• Title: Surrogate Assisted Pedestrian Protection Design via a Foundation Model Orchestrated Workflow
• Abstract summary: We present the first foundation model--orchestrated workflow for crash safety design.<n>It enables surrogate-assisted exploration for pedestrian protection, reducing evaluation time from hours per CAE simulation to seconds.<n>In an automotive front-bumper case study, the workflow produces 35 distinct safety-compliant alternatives.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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