Surrogate-Assisted Pedestrian Protection Design via a Foundation Model–Orchestrated Workflow

Osamu Ito, Akihiko Katagiri, Yoshikazu Nakagawa, Shin Saeki, Jun Shiraishi, Masato Sasaki

Keywords: Foundation Models, Surrogate Modeling, Conformal Prediction, Multi-objective Optimization, Computational Engineering, Hybrid AI Systems, Workflow Automation

TL;DR: We present a foundation model-orchestrated workflow that integrates surrogate prediction, evolutionary search, and 3D morphing for crash safety design, generating 35 feasible alternatives in seconds instead of weeks.

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.

Submission Number: 39