  SLEDGE is the first generative simulator for vehicle motion planning trained
on real-world driving logs. Its core component is a learned model that is able
to generate agent bounding boxes and lane graphs. The model's outputs serve as
an initial state for rule-based traffic simulation. The unique properties of
the entities to be generated for SLEDGE, such as their connectivity and
variable count per scene, render the naive application of most modern
generative models to this task non-trivial. Therefore, together with a
systematic study of existing lane graph representations, we introduce a novel
raster-to-vector autoencoder. It encodes agents and the lane graph into
distinct channels in a rasterized latent map. This facilitates both
lane-conditioned agent generation and combined generation of lanes and agents
with a Diffusion Transformer. Using generated entities in SLEDGE enables
greater control over the simulation, e.g. upsampling turns or increasing
traffic density. Further, SLEDGE can support 500m long routes, a capability not
found in existing data-driven simulators like nuPlan. It presents new
challenges for planning algorithms, evidenced by failure rates of over 40% for
PDM, the winner of the 2023 nuPlan challenge, when tested on hard routes and
dense traffic generated by our model. Compared to nuPlan, SLEDGE requires
500$\times$ less storage to set up (<4 GB), making it a more accessible option
and helping with democratizing future research in this field.
