Surrogate Assisted Pedestrian Protection Design via a Foundation Model Orchestrated Workflow
Quick Answer
This study presents a foundation model-orchestrated workflow for crash safety design, significantly reducing evaluation time from hours to seconds.
Quick Take
This study presents a foundation model-orchestrated workflow for crash safety design, significantly reducing evaluation time from hours to seconds. It integrates a surrogate model predicting pedestrian leg injury metrics with an NSGA-II evolutionary search, a geometry generator, and a natural-language interface, yielding 35 safety-compliant design alternatives in a case study, showcasing the potential of AI in safety-critical engineering.
Key Points
- Workflow reduces evaluation time from hours to seconds for pedestrian safety design.
- Surrogate model achieves an average R² of 0.87 for injury prediction.
- Generates 35 distinct safety-compliant alternatives in a single exploration.
- Integrates multiobjective evolutionary search with a natural-language interface.
- Demonstrates AI's role in enhancing crash safety design efficiency.
Paper Resources
Article Content
From source RSS / original summaryarXiv: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.
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