CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships

Machine learning models are increasingly prevalent in trajectory prediction and motion planning tasks for autonomous vehicles (AVs) [5, 6, 7, 10, 38, 30, 20, 16, 31, 39, 23, 18, 21]. To safely deploy such models, they must have reliable, robust predictions across a diverse range of scenarios and they must be insensitive to spurious features, or patterns in the data that fail to generalize to new environments. However, collecting and labeling the required data to both evaluate and improve model robustness is often expensive and difficult, in part due to the long tail of rare and difficult scenarios [22]. In this work, we propose perturbing existing data via agent deletions to evaluate and improve model robustness to spurious features. To be useful in our setting, the perturbations must preserve the correct labels and not change the ground truth trajectory of the AV. Since generating such perturbations requires high-level scene understanding as well as causal reasoning, we propose using human labelers to identify irrelevant agents. Specifically, we define a non-causal agent as an agent whose deletion does not cause the ground truth trajectory of a given target agent to change. We then construct a robustness evaluation dataset that consists of perturbed examples where we remove all non-causal agents from each scene, and we study model behavior under alternate perturbations, such as removing causal agents, removing a subset of non-causal agents, or removing stationary agents. Using our perturbed datasets, we then conduct an extensive experimental study exploring how factors such as model architecture, dataset size, and data augmentation effect model sensitivity.