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].

预测行驶中的交通参与者的轨迹运动，对于确保自动驾驶系统的安全性而言，既是一项重大挑战，也是一 项至关重要的需求。与大多数现有的、直接预测未来轨迹的数据驱动方法不同，我们从 规划（planning） 的视角 重新思考这一任务，提出一种" 先推理，后预测（First Reasoning, Then Forecasting） "的策略，该 策略显式地将行为意图作为轨迹预测的空间引导。为实现这一目标，进一步引入了一种可解释的、基于奖 励的意图推理器（intention reasoner），其建立在一种新颖的 以查询为中心的逆强化学习（query-centric Inverse Reinforcement Learning, IRL） 框架之上。我们的方法首先将交通参与者和场景元素编码为统一的 向量化表示，然后通过以查询为中心的范式聚合上下文特征。进而推导出一个 奖励分布（reward distribution） ——一种紧凑但信息丰富的表示，用于刻画目标参与者在给定场景上下文中的行为。在该奖 励启发式（reward heuristic）的引导下，我们进行策略 rollout，以推理多种可能的意图，从而为后续的轨迹 生 ...