Efficient Sampling-Based Trajectory Planning With Dual-Layer Probabilistic Intention Prediction for Autonomous Driving in Complex Intersections

Navigating complex urban intersections remains a significant challenge for autonomous vehicles due to highly dynamic and dense traffic environments. This paper presents a novel trajectory planning framework designed to address these challenges by integrating a dual-layer probabilistic rad-lane intention prediction model with an efficient sampling-based trajectory planner. The proposed dual-layer model comprises three components: short-term vehicle kinematic prediction-based road-level intention inference, Interactive Multiple Model (IMM)-based lane-level intention inference, and target-lane-based trajectory generation. This architecture broadens the application scope and validates the effectiveness of the IMM-based multi-model probabilistic fusion framework in urban intersection scenarios. By incorporating both road-level and lane-level contextual information, the proposed trajectory prediction method significantly enhances prediction accuracy. Additionally, a longitudinal sampling strategy based on predefined maneuver modes is employed to improve the probabilistic completeness of existing parametric curve-based sampling techniques, facilitating rapid and effective obstacle avoidance. A notable advantage of the proposed sampling strategy is its ability to efficiently and probabilistically generate safe and feasible trajectories in challenging intersection scenarios. Extensive simulation results demonstrate that the proposed framework outperforms existing methods in terms of both efficiency and safety in urban intersection vehicle conflict scenarios.