Core Insights - The article emphasizes the importance of accurately predicting the motion of road agents for the safety of autonomous driving, introducing a reward-driven intent reasoning mechanism to enhance trajectory prediction reliability and interpretability [3][5][10]. Summary by Sections Introduction - Trajectory prediction is a critical component of advanced autonomous driving systems, linking upstream perception with downstream planning modules. Current data-driven models often lack sufficient consideration of driving behavior, limiting their interpretability and reliability [5][10]. Methodology - The proposed method adopts a "reasoning first, then predict" strategy, where intent reasoning provides prior guidance for accurate and reliable multimodal motion prediction. The framework is structured as a Markov Decision Process (MDP) to model agent behavior [8][10][12]. - A reward-driven intent reasoning mechanism is introduced, utilizing Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) to learn agent-specific reward distributions from demonstrations and relevant driving environments [8][9][10]. - A new query-centered IRL framework, QIRL, is developed to efficiently aggregate contextual features into a structured representation, enhancing the overall prediction performance [9][10][18]. Experiments and Results - The proposed method, referred to as FiM, is evaluated on large-scale public datasets such as Argoverse and nuScenes, demonstrating competitive performance against state-of-the-art models [28][30][32]. - In the Argoverse 1 dataset, FiM achieved a minimum average displacement error (minADE) of 0.8296 and a minimum final displacement error (minFDE) of 1.2048, outperforming several leading models [32][33]. - The results indicate that the intent reasoning module significantly enhances prediction confidence and reliability, confirming the effectiveness of the proposed framework in addressing complex motion prediction challenges [34][36]. Conclusion - The work redefines the trajectory prediction task from a planning perspective, highlighting the critical role of intent reasoning in motion prediction. The proposed framework establishes a promising baseline for future research in trajectory prediction [47].