Terrain traversability prediction for off-road vehicles based on multi-source transfer learning

In this paper, a novel terrain traversability prediction method is proposed for new operation environments. When an off-road vehicle is operated on rough terrains or slopes made up of unconsolidated materials, it is crucial to accurately predict terrain traversability to ensure efficient operations and avoid critical mobility risks. However, the prediction of traversability in new environments is challenging, especially for possibly risky terrains, because the traverse data available for such terrains is either limited or non-existent. To address this limitation, this study proposes an adaptive terrain traversability prediction method based on multi-source transfer Gaussian process regression. The proposed method utilizes the limited data available on low-risk terrains of the target environment to enhance the prediction accuracy on untraversed, possibly higher-risk terrains by leveraging past traverse experiences on multiple types of terrain surface. The effectiveness of the proposed method is demonstrated in scenarios where vehicle slippage and power consumption are predicted using a dataset of various terrain surfaces and geometries. In addition to predicting terrain traversability as continuous values, the utility of the proposed method is demonstrated in binary risk level classification of yet to be traversed steep terrains from limited data on safer terrains.