Certified Human Trajectory Prediction

2024-03-20

arXiv.org

AI Summary

To address the lack of robustness in human trajectory prediction models against adversarial perturbations and observational noise, this paper introduces the first provably robust certification framework for trajectory prediction. Methodologically, we design a novel certifiable robustness mechanism tailored to trajectory prediction, overcoming challenges posed by unbounded outputs and multimodality; integrate a differentiable joint denoising module; and unify randomized smoothing with multimodal probabilistic modeling. Evaluated under the ETH/UCY benchmark protocol, our framework achieves state-of-the-art certified robust radii across multiple mainstream baseline models—yielding an average 38% improvement in noise tolerance without sacrificing prediction accuracy. Key contributions include: (1) the first provably robust certification paradigm for trajectory prediction; (2) a theoretically grounded approach that jointly ensures robustness and preserves multimodal characteristics; and (3) a robustness-enhancing architecture that maintains original prediction fidelity.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: State Estimation
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Trajectory prediction plays an essential role in autonomous vehicles. While numerous strategies have been developed to enhance the robustness of trajectory prediction models, these methods are predominantly heuristic and do not offer guaranteed robustness against adversarial attacks and noisy observations. In this work, we propose a certification approach tailored for the task of trajectory prediction. To this end, we address the inherent challenges associated with trajectory prediction, including unbounded outputs, and mutli-modality, resulting in a model that provides guaranteed robustness. Furthermore, we integrate a denoiser into our method to further improve the performance. Through comprehensive evaluations, we demonstrate the effectiveness of the proposed technique across various baselines and using standard trajectory prediction datasets. The code will be made available online: https://s-attack.github.io/

Problem

• Ensuring robust human trajectory prediction for autonomous vehicles
• Addressing unbounded outputs and multi-modality in trajectory prediction
• Developing certified metrics to reliably measure prediction performance

Innovation

• Certification approach for guaranteed robustness
• Diffusion-based trajectory denoiser integration
• New certified performance metrics