A longstanding challenge for self-driving development is the ability to simulate dynamic driving scenarios seeded from recorded driving logs. Given an initial scene observed during a test drive, we seek the ability to sample plausible scene-consistent future trajectories for all agents in the scene, even when the actions for a subset of agents are chosen by an external source, such as a black-box self-driving planner. In order to model the complicated spatial and temporal interaction across agents in driving scenarios, we propose to tokenize the motion of dynamic agents and use tools from language modeling to model the full sequence of multi-agent actions. Our traffic model explicitly captures intra-timestep dependence between agents, which we show is essential for simulation given only a single frame of historical scene context, as well as enabling improvements when provided longer historical context. We demonstrate competitive results sampling scenarios given initializations from the Waymo Open Dataset with full autonomy as well as partial autonomy, and show that the representations learned by our model can quickly be adapted to improve performance on nuScenes, a considerably smaller dataset. We additionally use density estimates from our model to quantify the saliency of context length and intra-timestep interaction for the traffic modeling task.
Trajeglish: Learning the Language of Driving Scenarios
Jonah Philion Xue Bin Peng Sanja Fidler