CuraScene: Interaction-Aware Scenario Curation for Data-Efficient Autonomous Driving

Trajectory forecasting models for autonomous driving are trained on scenario corpora that now reach hundreds of thousands of samples, yet the marginal value of an additional scenario is far from uniform. We argue that much of this redundancy is structural: what makes a driving scenario informative is largely the pattern of predictive influence among its agents—whose past states help forecast whose futures—and large corpora repeat a small number of interaction structures while varying mostly in surface trajectory geometry. We present CuraScene, which makes this structure explicit and selects on it. For each scenario, CuraScene infers—without interaction supervision—a directed graph over the agents and, where annotated, the traffic signals that control them. An edge $i\to j$ indicates that the lagged state of $i$ improves prediction of $j$. The graph is learned with an encoder–decoder trained under a sparsity penalty and a physically admissible edge mask. Scenarios are then selected greedily by the topological novelty of their ego-centric subgraph relative to the already-selected set, so a fixed budget covers interaction structures rather than raw samples. We do not claim a new graph structure learning algorithm; our claim is that inferred interaction structure is a useful and inexpensive selection signal for driving data. At a matched 50% budget on Waymo Open Motion and Argoverse 2, two forecasting models trained on CuraScene-selected scenarios reduce ${\text{minADE}}_6$ by 10.7–14.6% relative to budget-matched random subsampling, and an ablation shows that the learned graph and its admissibility prior are both required in order to recover the agents Waymo annotates as interacting.