  As machine learning models become increasingly prevalent in motion
forecasting for autonomous vehicles (AVs), it is critical to ensure that model
predictions are safe and reliable. However, exhaustively collecting and
labeling the data necessary to fully test the long tail of rare and challenging
scenarios is difficult and expensive. In this work, we construct a new
benchmark for evaluating and improving model robustness by applying
perturbations to existing data. Specifically, we conduct an extensive labeling
effort to identify causal agents, or agents whose presence influences human
drivers' behavior in any format, in the Waymo Open Motion Dataset (WOMD), and
we use these labels to perturb the data by deleting non-causal agents from the
scene. We evaluate a diverse set of state-of-the-art deep-learning model
architectures on our proposed benchmark and find that all models exhibit large
shifts under even non-causal perturbation: we observe a 25-38% relative change
in minADE as compared to the original. We also investigate techniques to
improve model robustness, including increasing the training dataset size and
using targeted data augmentations that randomly drop non-causal agents
throughout training. Finally, we release the causal agent labels (at
https://github.com/google-research/causal-agents) as an additional attribute to
WOMD and the robustness benchmarks to aid the community in building more
reliable and safe deep-learning models for motion forecasting.
