  Trajectory prediction is a critical component for autonomous vehicles (AVs)
to perform safe planning and navigation. However, few studies have analyzed the
adversarial robustness of trajectory prediction or investigated whether the
worst-case prediction can still lead to safe planning. To bridge this gap, we
study the adversarial robustness of trajectory prediction models by proposing a
new adversarial attack that perturbs normal vehicle trajectories to maximize
the prediction error. Our experiments on three models and three datasets show
that the adversarial prediction increases the prediction error by more than
150%. Our case studies show that if an adversary drives a vehicle close to the
target AV following the adversarial trajectory, the AV may make an inaccurate
prediction and even make unsafe driving decisions. We also explore possible
mitigation techniques via data augmentation and trajectory smoothing. The
implementation is open source at
https://github.com/zqzqz/AdvTrajectoryPrediction.
