二段式端到端新SOTA！港科大FiM：从Planning的角度重新思考轨迹预测（ICCV'25）

Core Viewpoint - The article presents a novel approach to trajectory prediction in autonomous driving, emphasizing a "First Reasoning, Then Forecasting" strategy that integrates intention reasoning to enhance prediction accuracy and reliability [2][4][47]. Group 1: Methodology - The proposed method introduces an intention reasoner based on a query-centric Inverse Reinforcement Learning (IRL) framework, which explicitly incorporates behavioral intentions as spatial guidance for trajectory prediction [2][5][47]. - A bidirectional selective state space model (Bi-Mamba) is developed to improve the accuracy and confidence of trajectory predictions by capturing sequential dependencies in trajectory states [9][47]. - The approach utilizes a grid-level graph representation to model participant behavior, formalizing the task as a Markov Decision Process (MDP) to define future intentions [5][6][21]. Group 2: Experimental Results - Extensive experiments on large-scale datasets such as Argoverse and nuScenes demonstrate that the proposed method significantly enhances trajectory prediction confidence, achieving competitive performance compared to state-of-the-art models [2][33][36]. - The method outperforms existing models in various metrics, including Brier score and minFDE6, indicating its robustness in complex driving scenarios [33][35][36]. - The integration of a spatial-temporal occupancy grid map (S-T OGM) enhances the model's ability to predict future interactions among participants, further improving prediction quality [9][39]. Group 3: Contributions - The article highlights the critical role of intention reasoning in motion prediction, establishing a promising baseline model for future research in trajectory prediction [47]. - The introduction of a reward-driven intention reasoning mechanism provides valuable prior information for trajectory generation, addressing the inherent uncertainties in driving behavior [8][47]. - The work emphasizes the potential of reinforcement learning paradigms in modeling driving behavior, paving the way for advancements in autonomous driving technology [5][47].