  Controllable and realistic traffic simulation is critical for developing and
verifying autonomous vehicles. Typical heuristic-based traffic models offer
flexible control to make vehicles follow specific trajectories and traffic
rules. On the other hand, data-driven approaches generate realistic and
human-like behaviors, improving transfer from simulated to real-world traffic.
However, to the best of our knowledge, no traffic model offers both
controllability and realism. In this work, we develop a conditional diffusion
model for controllable traffic generation (CTG) that allows users to control
desired properties of trajectories at test time (e.g., reach a goal or follow a
speed limit) while maintaining realism and physical feasibility through
enforced dynamics. The key technical idea is to leverage recent advances from
diffusion modeling and differentiable logic to guide generated trajectories to
meet rules defined using signal temporal logic (STL). We further extend
guidance to multi-agent settings and enable interaction-based rules like
collision avoidance. CTG is extensively evaluated on the nuScenes dataset for
diverse and composite rules, demonstrating improvement over strong baselines in
terms of the controllability-realism tradeoff.
