DragSolver: A Multi-Scale Transformer for Real-World Automotive Drag Coefficient Estimation

Automotive drag coefficient (C d ) is pivotal to energy efficiency, fuel consumption, and aerodynamic performance. However, costly computational fluid dynamics (CFD) simulations and wind tunnel tests struggle to meet the rapid-iteration demands of automotive design. We present Drag-Solver, a Transformer-based framework for rapid and accurate C d estimation from large-scale, diverse 3D vehicle models. DragSolver tackles four key real-world challenges: (1) multi-scale feature extraction to capture both global shape and fine local geometry; (2) heterogeneous scale normalization to handle meshes with varying sizes and densities; (3) surface-guided gating to suppress internal structures irrelevant to external aerodynamics; and (4) epistemic uncertainty estimation via Monte Carlo dropout for risk-aware design. Extensive evaluations on three industrial-scale datasets (DrivAerNet, DrivAerNet++, and Dri-vaerML) show that DragSolver outperforms existing approaches in accuracy and generalization, achieving an average reduction of relative L 2 error by 58.7% across real-world datasets. Crucially, DragSolver is the first to achieve reliable, realtime C d inference on production-level automotive geometries.