Traversability Risk Assessment and Path Planning for Off‐Road Autonomous Vehicles in Winter Conditions

ABSTRACT While off‐road autonomous vehicles have achieved substantial deployment success, winter conditions introduce unprecedented operational challenges, potentially leading to critical failure modes such as slipping, rollover, collision, and sinking. This paper presents an integrated technical solution to address these winter navigation challenges. First, a vehicle speed prediction model is developed by fitting probability distributions and training a Multilayer Perception to forecast key parameters of the probability density function. For traversability risk assessment, mechanical principles are first leveraged to analyze the inherent correlations among hazard events, terrain characteristics, and vehicle dynamics. A hybrid framework integrating a Variational Bayesian Network with a Long Short‐Term Memory network (VBN‐LSTM) is then constructed to predict the probabilities of hazard occurrences by jointly leveraging causal structural priors and temporal dynamics. Building on this, a joint probability model for hazard events and vehicle speed is established, explicitly accounting for their interdependencies to enable comprehensive traversability risk evaluation. Finally, the Hybrid A* algorithm is enhanced by integrating speed distribution prediction and traversability risk assessment, facilitating safer and more reliable navigation in winter off‐road environments. The improved algorithm is validated in real‐world winter terrains through comparisons with other algorithms. Experimental results demonstrate that the planned paths generated by the proposed approach outperform competitors in terms of estimated risk, path efficiency, and travel time.