TerrFlat: Physics-Driven Geometry Representation for Structure-Aware Freespace Detection

Abstract

Freespace detection in autonomous driving is limited by the lack of explicit geometric modeling, hindering generalization across complex terrains. Existing approaches are predominantly data-driven and neglect the physical structure of drivable surfaces. We propose Terrain Flat (TerrFlat), a physics-driven geometric representation that models road surfaces along three interpretable dimensions: lateral smoothness, longitudinal consistency, and vertical deviation. TerrFlat is constructed through geometric reasoning and projected into pixel-aligned maps via a differentiable projection, ensuring geometric–visual consistency. Building on this representation, we introduce a symmetric feature fusion module (SFFM) to integrate TerrFlat with visual features through bidirectional recalibration, improving semantic discrimination and boundary localization. Together, TerrFlat and SFFM form TerrFlat-Seg, a unified framework for physics-aware freespace perception. Experiments on KITTI-Road, Semantic-KITTI, and ORFD datasets demonstrate consistent improvements over existing baselines. Real-world validation on an automated guided vehicle platform further confirms the robustness of our approach.