Contingency Planning for Autonomous Vehicles at Uncontrolled Intersections Based on Level-k Game Model Predictions

Abstract

Uncontrolled intersections without traffic signals or lane markings (e.g., mining areas and rural roads) present critical challenges for autonomous vehicles, primarily due to complex interactive dynamics and unpredictable turning intentions. To address this, this paper proposes an integrated contingency planning framework based on level- $k$ game model predictions. Our approach first incorporates the influence of the ego vehicle’s decisions into the prediction loop. Laterally, we represent the long-term turning intention of other vehicles via a curve-fitting method; longitudinally, we model the interactive decision-making process between the ego and surrounding vehicles using a level- $k$ game model, thereby generating prediction and decision results that account for interaction dynamics. We then introduce a two-stage probabilistic update scheme, which separately refines the confidence of lateral and longitudinal predictions using a Gaussian Mixture Model (GMM). Finally, a motion-sampling-based contingency planner is designed to handle multimodal prediction outcomes, yielding a branched trajectory tree that is further optimized for smoothness and safety. Simulations demonstrate that our method reduces intersection traversal time by 24.6% compared to the baseline. Furthermore, it ensures collision-free maneuvers and significantly lowers maximum deceleration and jerk. The framework thus enables safer and more efficient navigation for pairwise interactions in infrastructure-less environments.