Enhanced Motion Forecasting with Plug-and-Play Multimodal Large Language Models

2025-10-20

AI Summary

Current motion prediction models for autonomous driving exhibit limited generalization, particularly in long-tail real-world scenarios. To address this, we propose Plug-and-Forecast (PnF), the first framework to integrate multimodal large language models (MLLMs) into motion prediction in a zero-shot, plug-and-play manner—requiring no fine-tuning. PnF leverages prompt engineering to elicit structured scene understanding from an off-the-shelf MLLM and distills this knowledge into learnable embeddings, which are then end-to-end fused with existing prediction backbones. This enables explicit modeling of complex driving semantics—including natural-language-described interaction intentions, occlusion relationships, and contextual reasoning—beyond conventional geometric or rasterized inputs. Evaluated on Waymo Open Motion and nuScenes, PnF consistently improves performance across diverse state-of-the-art prediction architectures (e.g., LaneGCN, HiFi, PGP), demonstrating strong generalizability and effectiveness without architectural modification or task-specific adaptation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Current autonomous driving systems rely on specialized models for perceiving and predicting motion, which demonstrate reliable performance in standard conditions. However, generalizing cost-effectively to diverse real-world scenarios remains a significant challenge. To address this, we propose Plug-and-Forecast (PnF), a plug-and-play approach that augments existing motion forecasting models with multimodal large language models (MLLMs). PnF builds on the insight that natural language provides a more effective way to describe and handle complex scenarios, enabling quick adaptation to targeted behaviors. We design prompts to extract structured scene understanding from MLLMs and distill this information into learnable embeddings to augment existing behavior prediction models. Our method leverages the zero-shot reasoning capabilities of MLLMs to achieve significant improvements in motion prediction performance, while requiring no fine-tuning -- making it practical to adopt. We validate our approach on two state-of-the-art motion forecasting models using the Waymo Open Motion Dataset and the nuScenes Dataset, demonstrating consistent performance improvements across both benchmarks.

Problem

• Enhancing motion forecasting generalization in autonomous driving
• Using multimodal language models for complex scenario adaptation
• Improving prediction without fine-tuning via plug-and-play integration

Innovation

• Plug-and-play MLLMs augment motion forecasting models
• Natural language prompts extract structured scene understanding
• Zero-shot reasoning improves prediction without fine-tuning