Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction

TL;DR

This paper introduces a semi-supervised semantics-guided adversarial training method to enhance the robustness of trajectory prediction models against adversarial attacks, significantly reducing attack impact and improving generalization.

Contribution

It proposes a novel semi-supervised adversarial autoencoder framework that models semantic features and latent labels to improve robustness in trajectory prediction.

Findings

Mitigates adversarial attack impact by up to 73%

Outperforms other defense methods in robustness

Enhances generalization to unseen attack patterns

Abstract

Predicting the trajectories of surrounding objects is a critical task for self-driving vehicles and many other autonomous systems. Recent works demonstrate that adversarial attacks on trajectory prediction, where small crafted perturbations are introduced to history trajectories, may significantly mislead the prediction of future trajectories and induce unsafe planning. However, few works have addressed enhancing the robustness of this important safety-critical task.In this paper, we present a novel adversarial training method for trajectory prediction. Compared with typical adversarial training on image tasks, our work is challenged by more random input with rich context and a lack of class labels. To address these challenges, we propose a method based on a semi-supervised adversarial autoencoder, which models disentangled semantic features with domain knowledge and provides additional latent labels for the adversarial training. Extensive experiments with different types of attacks demonstrate that our Semisupervised Semantics-guided Adversarial Training (SSAT) method can effectively mitigate the impact of adversarial attacks by up to 73% and outperform other popular defense methods. In addition, experiments show that our method can significantly improve the system's robust generalization to unseen patterns of attacks. We believe that such semantics-guided architecture and advancement on robust generalization is an important step for developing robust prediction models and enabling safe decision-making.