二段式SOTA！港科大FiM：从Planning的角度重新思考轨迹预测

Core Insights - 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][48]. Group 1: Methodology - The proposed method introduces an intention reasoner based on a query-centric Inverse Reinforcement Learning (IRL) framework, which captures the behavior of traffic participants and their intentions in a compact representation [2][6][48]. - A bidirectional selective state space model (Bi-Mamba) is developed to improve trajectory decoding, effectively capturing the sequential dependencies of trajectory states [7][9][48]. - The framework utilizes a grid-level graph to represent the driving context, allowing for efficient modeling of participant behavior and intentions [5][6][20]. Group 2: Experimental Results - Extensive experiments on large datasets such as Argoverse and nuScenes demonstrate that the proposed method significantly enhances prediction confidence and achieves competitive performance compared to state-of-the-art models [9][34][38]. - In the Argoverse 1 dataset, the proposed method (FiM) outperformed several strong baseline methods in key metrics such as Brier score and minFDE6, indicating its robust predictive capabilities [34][35]. - The results from Argoverse 2 further validate the effectiveness of the intention reasoning strategy, showing that longer-term intention supervision improves prediction reliability [36][37]. Group 3: Challenges and Innovations - The article highlights the inherent challenges in modeling intentions due to the complexity of driving scenarios, advocating for the use of large reasoning models (LRMs) to enhance intention inference [5][6][12]. - The integration of a dense occupancy grid map (OGM) prediction head is introduced to model future interactions among participants, which enhances the overall prediction performance [7][25][41]. - The study emphasizes the importance of intention reasoning in motion prediction, establishing a promising baseline for future research in trajectory prediction [48].