  Realistic trajectory generation with natural language control is pivotal for
advancing autonomous vehicle technology. However, previous methods focus on
individual traffic participant trajectory generation, thus failing to account
for the complexity of interactive traffic dynamics. In this work, we propose
InteractTraj, the first language-driven traffic trajectory generator that can
generate interactive traffic trajectories. InteractTraj interprets abstract
trajectory descriptions into concrete formatted interaction-aware numerical
codes and learns a mapping between these formatted codes and the final
interactive trajectories. To interpret language descriptions, we propose a
language-to-code encoder with a novel interaction-aware encoding strategy. To
produce interactive traffic trajectories, we propose a code-to-trajectory
decoder with interaction-aware feature aggregation that synergizes vehicle
interactions with the environmental map and the vehicle moves. Extensive
experiments show our method demonstrates superior performance over previous
SoTA methods, offering a more realistic generation of interactive traffic
trajectories with high controllability via diverse natural language commands.
Our code is available at https://github.com/X1a-jk/InteractTraj.git
