Large Language Models Powered Context-aware Motion Prediction in Autonomous Driving

TL;DR

This paper leverages Large Language Models to improve traffic scene understanding and motion prediction accuracy in autonomous driving by integrating rich context information through prompt engineering and a cost-effective deployment strategy.

Contribution

It introduces a novel method of using LLMs with prompt engineering to enhance traffic context understanding for motion prediction in autonomous driving.

Findings

LLMs can effectively visualize complex traffic environments into image prompts.

Integrating LLM-derived context improves motion prediction accuracy.

A cost-effective strategy enhances prediction performance with minimal LLM-augmented data.

Abstract

Motion prediction is among the most fundamental tasks in autonomous driving. Traditional methods of motion forecasting primarily encode vector information of maps and historical trajectory data of traffic participants, lacking a comprehensive understanding of overall traffic semantics, which in turn affects the performance of prediction tasks. In this paper, we utilized Large Language Models (LLMs) to enhance the global traffic context understanding for motion prediction tasks. We first conducted systematic prompt engineering, visualizing complex traffic environments and historical trajectory information of traffic participants into image prompts -- Transportation Context Map (TC-Map), accompanied by corresponding text prompts. Through this approach, we obtained rich traffic context information from the LLM. By integrating this information into the motion prediction model, we demonstrate that such context can enhance the accuracy of motion predictions. Furthermore, considering the cost associated with LLMs, we propose a cost-effective deployment strategy: enhancing the accuracy of motion prediction tasks at scale with 0.7\% LLM-augmented datasets. Our research offers valuable insights into enhancing the understanding of traffic scenes of LLMs and the motion prediction performance of autonomous driving. The source code is available at \url{https://github.com/AIR-DISCOVER/LLM-Augmented-MTR} and \url{https://aistudio.baidu.com/projectdetail/7809548}.