Multimodal Trajectory Prediction

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Frozen LLMs as Map-Aware Spatio-Temporal Reasoners for Vehicle Trajectory Prediction
Apr 23, 2026Yanjiao Liu, Jiawei Liu, Xun Gong +1Multi-Agent Trajectory Prediction Autonomous Driving
School of Artificial Intelligence, Jilin University, Changchun 130012, PR China.
MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
Apr 11, 2026Wenchang Duan, Zhenguo Gao, Jinguo Xian +1Model Compression Multi-Agent Trajectory Prediction
School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China · Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai 200030, China · Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai 200030, China
Hi-FLoop: Hierarchical State-Feedback Loops for Multi-Timescale World Modeling
Date pendingRx Fan, Z HanMulti-Agent Trajectory Prediction Multimodal Trajectory Prediction
School of Systems Science, Beijing Normal University

Apr 23, 2026, cs.CVJ/K move · Enter open · S save

Frozen LLMs as Map-Aware Spatio-Temporal Reasoners for Vehicle Trajectory Prediction

Yanjiao Liu, Jiawei Liu, Xun Gong, Zifei Nie

School of Artificial Intelligence, Jilin University, Changchun 130012, PR China.

Multi-Agent Trajectory Prediction Autonomous Driving Multimodal Trajectory Prediction Trajectory Prediction Vehicle Trajectory Prediction

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Large language models (LLMs) have recently demonstrated strong reasoning capabilities and attracted increasing research attention in the field of autonomous driving (AD). However, safe application of LLMs on AD perception and prediction still requires a thorough understanding of both the dynamic traffic agents and the static road infrastructure. To this end, this study introduces a framework to evaluate the capability of LLMs in understanding the behaviors of dynamic traffic agents and the topology of road networks. The framework leverages frozen LLMs as the reasoning engine, employing a traffic encoder to extract spatial-level scene features from observed trajectories of agents, while a lightweight Convolutional Neural Network (CNN) encodes the local high-definition (HD) maps. To assess the intrinsic reasoning ability of LLMs, the extracted scene features are then transformed into LLM-compatible tokens via a reprogramming adapter. By residing the prediction burden with the LLMs, a simpler linear decoder is applied to output future trajectories. The framework enables a quantitative analysis of the influence of multi-modal information, especially the impact of map semantics on trajectory prediction accuracy, and allows seamless integration of frozen LLMs with minimal adaptation, thereby demonstrating strong generalizability across diverse LLM architectures and providing a unified platform for model evaluation.

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Latest papers 33

Frozen LLMs as Map-Aware Spatio-Temporal Reasoners for Vehicle Trajectory Prediction

MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction

Hi-FLoop: Hierarchical State-Feedback Loops for Multi-Timescale World Modeling