Core Insights - The article discusses the application of large language models (LLMs) and multimodal large language models (MLLMs) in the paradigm shift for autonomous driving trajectory prediction, enhancing the understanding of complex traffic scenarios to improve safety and efficiency [1][20]. Summary by Sections Introduction and Overview - The integration of LLMs into autonomous driving systems allows for a deeper understanding of traffic scenarios, transitioning from traditional methods to LFM-based approaches [1]. - Trajectory prediction is identified as a core technology in autonomous driving, utilizing historical data and contextual information to infer future movements of traffic participants [5]. Traditional Methods and Challenges - Traditional vehicle trajectory prediction methods include physics-based approaches (e.g., Kalman filters) and machine learning methods (e.g., Gaussian processes), which struggle with complex interactions [8]. - Deep learning methods improve long-term prediction accuracy but face challenges such as high computational demands and poor interpretability [9]. - Reinforcement learning methods excel in interactive scene modeling but are complex and unstable [9]. LLM-Based Vehicle Trajectory Prediction - LFM introduces a paradigm shift by discretizing continuous motion states into symbolic sequences, leveraging LLMs' semantic modeling capabilities [11]. - Key applications of LLMs include trajectory-language mapping, multimodal fusion, and constraint-based reasoning, enhancing interpretability and robustness in long-tail scenarios [11][13]. Evaluation Metrics and Datasets - The article categorizes datasets for pedestrian and vehicle trajectory prediction, highlighting the importance of datasets like Waymo and ETH/UCY for evaluating model performance [16]. - Evaluation metrics for vehicles include L2 distance and collision rates, while pedestrian metrics focus on minADE and minFDE [17]. Performance Comparison - A performance comparison of various models on the NuScenes dataset shows that LLM-based methods significantly reduce collision rates and improve long-term prediction accuracy [18]. Discussion and Future Directions - The widespread application of LFMs indicates a shift from local pattern matching to global semantic understanding, enhancing safety and compliance in trajectory generation [20]. - Future research should focus on developing low-latency inference techniques, constructing motion-oriented foundational models, and advancing world perception and causal reasoning models [21].

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].