Are socially-aware trajectory prediction models really socially-aware?
This addresses the adversarial robustness problem for trajectory predictors in applications like autonomous navigation, though it is incremental as it builds on existing attack methods.
The paper tackles the problem of assessing whether socially-aware trajectory prediction models truly understand social interactions by introducing a socially-attended attack that crafts perturbations to test collision avoidance. The result shows that current models have limitations in social understanding, and the attack can be used to improve state-of-the-art models.
Our field has recently witnessed an arms race of neural network-based trajectory predictors. While these predictors are at the core of many applications such as autonomous navigation or pedestrian flow simulations, their adversarial robustness has not been carefully studied. In this paper, we introduce a socially-attended attack to assess the social understanding of prediction models in terms of collision avoidance. An attack is a small yet carefully-crafted perturbations to fail predictors. Technically, we define collision as a failure mode of the output, and propose hard- and soft-attention mechanisms to guide our attack. Thanks to our attack, we shed light on the limitations of the current models in terms of their social understanding. We demonstrate the strengths of our method on the recent trajectory prediction models. Finally, we show that our attack can be employed to increase the social understanding of state-of-the-art models. The code is available online: https://s-attack.github.io/