SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Traffic

TL;DR

SLEDGE is a novel generative vehicle motion simulator trained on real-world data, combining learned models and rule-based traffic to enable scalable, controllable, and long-route traffic simulation for advanced planning algorithm testing.

Contribution

It introduces a new generative framework with a raster-to-vector autoencoder and Diffusion Transformer for realistic traffic scene generation, supporting longer routes and higher traffic density.

Findings

Supports 500m route simulation, surpassing existing models.

Requires 500x less storage than nuPlan.

Shows over 40% failure rate for PDM on dense traffic routes.

Abstract

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.