Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models

TL;DR

Text-to-Drive leverages large language models to generate diverse, human-like driving behaviors from natural language descriptions, enhancing simulation diversity and enabling interactive, preference-based behavior synthesis for autonomous vehicle training.

Contribution

The paper introduces a novel knowledge-driven framework using LLMs to synthesize diverse driving behaviors from language, without requiring large-scale annotated datasets.

Findings

T2D produces more diverse trajectories than baselines.

It enables natural language interaction for behavior customization.

The approach improves simulation realism and flexibility.

Abstract

Generating varied scenarios through simulation is crucial for training and evaluating safety-critical systems, such as autonomous vehicles. Yet, the task of modeling the trajectories of other vehicles to simulate diverse and meaningful close interactions remains prohibitively costly. Adopting language descriptions to generate driving behaviors emerges as a promising strategy, offering a scalable and intuitive method for human operators to simulate a wide range of driving interactions. However, the scarcity of large-scale annotated language-trajectory data makes this approach challenging. To address this gap, we propose Text-to-Drive (T2D) to synthesize diverse driving behaviors via Large Language Models (LLMs). We introduce a knowledge-driven approach that operates in two stages. In the first stage, we employ the embedded knowledge of LLMs to generate diverse language descriptions of driving behaviors for a scene. Then, we leverage LLM's reasoning capabilities to synthesize these behaviors in simulation. At its core, T2D employs an LLM to construct a state chart that maps low-level states to high-level abstractions. This strategy aids in downstream tasks such as summarizing low-level observations, assessing policy alignment with behavior description, and shaping the auxiliary reward, all without needing human supervision. With our knowledge-driven approach, we demonstrate that T2D generates more diverse trajectories compared to other baselines and offers a natural language interface that allows for interactive incorporation of human preference. Please check our website for more examples: https://text-to-drive.github.io/