LLM-based and Game-Theoretic Traffic Trajectory Generation for Autonomous Driving

Autonomous driving significantly benefits from datadriven deep neural networks that require large-scale and highquality datasets for training. To achieve higher performance or conduct critical testing, trajectories with game-theoretic properties are necessary. However, defining such scenarios and collecting large-scale data from reality or simulation is hard and costly. In this work, we propose a novel two-step method that generates traffic trajectories with game-theoretic properties, based on the Large Language Model (LLM). We apply the method to generate a challenging traffic trajectory dataset, which is validated on many downstream tasks, including perception and prediction. The experimental results show that the generated trajectories can achieve high-difficulty testing and advanced training. We conclude that our method successfully generates the challenging traffic trajectories, which contribute to collecting such critical data.