We provide a standardized graph dataset for traffic based on the large-scale NuPlan v1.1 dataset, converted to a PyTorch-Geometric dataset using our CommonRoad-Geometric tool. The dataset is collected from 3 megacities: Singapore, Boston and Pittsburgh.

nuPlan is the world's first large-scale planning benchmark for autonomous driving. While there is a growing body of ML-based motion planners, the lack of established datasets, simulation frameworks and metrics has limited the progress in this area. Existing benchmarks (Argoverse, Lyft, Waymo) for autonomous vehicle motion prediction have focused on short-term motion forecasting of other agents, rather than long-term planning of the ego vehicle. This has led previous works to use open-loop evaluation with L2-based metrics, which are not suitable for fairly evaluating long-term planning. Our benchmark overcomes these Limitation by providing a training framework to develop machine learning based planners, a lightweight closed-loop simulator, motion-planning specific metrics and an interactive tool to visualize the results.