Long-term Traffic Simulation with Interleaved Autoregressive Motion and
  Scenario Generation
Authors: name: Xiuyu Yang, name: Shuhan Tan, name: Philipp Krähenbühl

An ideal traffic simulator replicates the realistic long-term point-to-point
trip that a self-driving system experiences during deployment. Prior models and
benchmarks focus on closed-loop motion simulation for initial agents in a
scene. This is problematic for long-term simulation. Agents enter and exit the
scene as the ego vehicle enters new regions. We propose InfGen, a unified
next-token prediction model that performs interleaved closed-loop motion
simulation and scene generation. InfGen automatically switches between
closed-loop motion simulation and scene generation mode. It enables stable
long-term rollout simulation. InfGen performs at the state-of-the-art in
short-term (9s) traffic simulation, and significantly outperforms all other
methods in long-term (30s) simulation. The code and model of InfGen will be
released at https://orangesodahub.github.io/InfGen