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.

翻译：SLEDGE是首个基于真实驾驶日志训练的车辆运动规划生成式模拟器。其核心组件是一个能够生成智能体边界框与车道图的学习模型。该模型的输出作为规则化交通仿真的初始状态。SLEDGE所需生成实体具有独特属性（如连接性及场景中可变数量），使得多数现代生成模型直接应用于此任务变得困难。为此，我们在系统研究现有车道图表示方法的基础上，提出了一种新颖的栅格-矢量自编码器。该编码器将智能体与车道图编码至栅格化潜在映射的不同通道中，这既支持了车道条件约束的智能体生成，也实现了车道与智能体通过扩散Transformer的联合生成。在SLEDGE中使用生成实体可增强对仿真的控制能力，例如提升转弯场景采样率或增加交通密度。此外，SLEDGE能够支持长达500米的路线规划，这是nuPlan等现有数据驱动模拟器所不具备的能力。该模拟器为规划算法带来了新挑战：2023年nuPlan挑战赛冠军算法PDM在测试我们模型生成的复杂路线与密集交通场景时，失败率超过40%。与nuPlan相比，SLEDGE仅需1/500的存储空间（<4GB）即可部署，使其成为更易获取的研究选项，有助于推动该领域未来研究的普及化发展。