SA-Attack: Speed-adaptive stealthy adversarial attack on trajectory prediction

TL;DR

SA-Attack introduces a speed-adaptive, stealthy adversarial attack on trajectory prediction models for autonomous vehicles, effectively exploiting model sensitivities while remaining inconspicuous across various speed scenarios.

Contribution

This paper presents a novel, adaptive adversarial attack method that considers vehicle speed and trajectory smoothness, enhancing attack stealthiness and realism in trajectory prediction systems.

Findings

Effective attack success on nuScenes and Apolloscape datasets.

High adaptability across different vehicle speed scenarios.

Maintains stealthiness by ensuring trajectory smoothness.

Abstract

Trajectory prediction is critical for the safe planning and navigation of automated vehicles. The trajectory prediction models based on the neural networks are vulnerable to adversarial attacks. Previous attack methods have achieved high attack success rates but overlook the adaptability to realistic scenarios and the concealment of the deceits. To address this problem, we propose a speed-adaptive stealthy adversarial attack method named SA-Attack. This method searches the sensitive region of trajectory prediction models and generates the adversarial trajectories by using the vehicle-following method and incorporating information about forthcoming trajectories. Our method has the ability to adapt to different speed scenarios by reconstructing the trajectory from scratch. Fusing future trajectory trends and curvature constraints can guarantee the smoothness of adversarial trajectories, further ensuring the stealthiness of attacks. The empirical study on the datasets of nuScenes and Apolloscape demonstrates the attack performance of our proposed method. Finally, we also demonstrate the adaptability and stealthiness of SA-Attack for different speed scenarios. Our code is available at the repository: https://github.com/eclipse-bot/SA-Attack.