How Bumpy Is It? Incremental Online Learning of Terrain-Induced Bumpiness Costs for Off-Road Vehicles

Abstract

Stable autonomous driving in unstructured off-road environments remains a longstanding challenge. In the absence of structured roads and in the presence of uneven terrain, vegetation, and soil slopes, vehicles must rely on LiDAR–Camera fusion to identify stable and traversable roads. However, existing terrain perception methods largely remain at the level of semantic segmentation and struggle to capture physical attributes such as surface roughness and load-bearing capacity. Meanwhile, constructing datasets annotated with accurate physical properties is prohibitively costly and inherently limited in class diversity, making it difficult to cover unseen terrains. To address these limitations, we propose an online ground bumpiness cost learning framework for off-road vehicles, which enables continuous and direct learning of terrain-specific bumpiness costs during operation without the need for manual annotation. The framework consists of four key components: (i) ground bumpiness cost computation, (ii) a lightweight multimodal terrain segmentation model, (iii) an instance-level incremental update strategy, and (iv) a bumpiness cost mapping module. Extensive experiments on the EV-56 vibroseis truck demonstrate that the proposed framework can finely discriminate terrains with varying bumpiness costs and incrementally estimate costs for previously unseen terrains, thereby providing strong support for safe and reliable off-road autonomous driving.