Trajectory prediction models in autonomous driving are vulnerable to perturbations from non-causal agents
whose actions should not affect the ego-agent’s behavior. Such perturbations can lead to incorrect
predictions of other agents’ trajectories, potentially compromising the safety and efficiency of the
ego-vehicle’s decision-making process. Motivated by this challenge, we propose Causal tRajecTory
predICtion (CRiTIC), a novel model that utilizes a Causal Discovery Network to identify
inter-agent causal relations over a window of past time steps. To incorporate discovered causal
relationships, we propose a novel Causal Attention Gating mechanism to selectively filter
information in the proposed Transformer-based architecture.
We conduct extensive experiments on two autonomous driving benchmark datasets to evaluate the robustness
of our model against non-causal perturbations and its generalization capacity. Our results indicate that
the robustness of predictions can be improved by up to 54% without a significant detriment
to prediction accuracy. Lastly, we demonstrate the superior domain generalizability of the proposed
model, which achieves up to 29% improvement in cross-scenario setting. These results underscore the
potential of our model to enhance both robustness and generalization capacity for trajectory prediction in
diverse autonomous driving domains.
