Stealthy Adversarial Attacks on Trajectory Prediction Models for Autonomous Vehicles

Conceitos Básicos

The authors propose a speed-adaptive stealthy adversarial attack method (SA-Attack) that can effectively mislead trajectory prediction models used in autonomous vehicles while maintaining the naturalness and feasibility of the generated adversarial trajectories.

Resumo

The authors propose a novel adversarial attack method called SA-Attack for trajectory prediction models used in autonomous vehicles. The method consists of two stages:

1. Reference Trajectory Generation:

   • The authors generate multiple sets of randomly initialized perturbations to explore model-sensitive trajectory shapes.
   • They use a white-box optimization method based on Projected Gradient Descent (PGD) to update the perturbations and obtain a reference trajectory that combines the sensitive trajectory points with the real future trajectory.

2. Feasible Trajectory Reconstruction:

   • The authors use a continuous curvature model to characterize the trajectories and a pure-pursuit method to reconstruct feasible adversarial trajectories from the reference trajectory.
   • This approach ensures the smoothness and physical feasibility of the generated adversarial trajectories, making them more stealthy and harder to detect as anomalous.
   • The method also incorporates information about the upcoming future trajectory to ensure a natural transition between the adversarial and real trajectories.

The authors evaluate the proposed SA-Attack method on the nuScenes and Apolloscape datasets using two state-of-the-art trajectory prediction models, Trajectron++ and Grip++. The results demonstrate that SA-Attack outperforms the baseline search-based attack method in terms of attack effectiveness and trajectory feasibility, while maintaining a high level of stealthiness.

Estatísticas

The authors report the following key metrics before and after the adversarial attack:

Average Displacement Error (ADE): 120% increase
Final Displacement Error (FDE): 82% increase
Miss Rates (MR): Increased from 19% to 56% for Trajectron++
Off Road Rates (ORR): Increased from 1.6% to 8.6% for Trajectron++

Citações

"Our proposed SA-Attack method achieves considerable attack performance on the nuScenes and Apolloscape datasets."
"The adversarial trajectories generated by SA-Attack are more realistic and feasible compared to the baseline search-based attack method."

Principais Insights Extraídos De

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

by Huilin Yin,J... às arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12612.pdf

Perguntas Mais Profundas

How can the proposed SA-Attack method be extended to black-box attack scenarios, where the adversary does not have access to the model parameters

To extend the SA-Attack method to black-box attack scenarios, where the adversary lacks access to the model parameters, several approaches can be considered. One method is to utilize transferability of adversarial examples. By crafting adversarial examples on a surrogate model with similar characteristics to the target model, the perturbations can still be effective even without direct access to the target model's parameters. Another approach is to employ optimization techniques such as genetic algorithms or reinforcement learning to explore the model's response to perturbations without needing explicit parameter information. Additionally, meta-learning strategies can be employed to adapt attack methods to new models without direct parameter access, enhancing the versatility of the attack strategy.

What defense mechanisms can be developed to improve the robustness of trajectory prediction models against such stealthy adversarial attacks

Several defense mechanisms can be developed to enhance the robustness of trajectory prediction models against stealthy adversarial attacks like SA-Attack. One approach is to integrate adversarial training during the model training phase, where the model is exposed to adversarial examples to improve its resilience. Robust optimization techniques, such as adversarial training with gradient regularization or feature denoising, can also be effective in mitigating the impact of adversarial perturbations. Furthermore, ensemble methods that combine multiple models or incorporate uncertainty estimation can help detect and mitigate adversarial attacks. Additionally, incorporating physical constraints explicitly into the model architecture can enhance the model's ability to generate realistic trajectories and resist adversarial perturbations.

How can the insights from this work on adversarial attacks be applied to enhance the safety and reliability of autonomous driving systems in real-world scenarios

The insights gained from studying adversarial attacks on trajectory prediction models can be applied to enhance the safety and reliability of autonomous driving systems in real-world scenarios. By understanding the vulnerabilities exposed by stealthy adversarial attacks, developers can implement robustness testing procedures to evaluate the model's resilience against such attacks. Integrating anomaly detection mechanisms that can identify anomalous trajectories generated by adversarial attacks can help prevent unsafe behaviors in autonomous vehicles. Moreover, incorporating diverse and adversarially trained models into the decision-making process of autonomous systems can improve their ability to handle unexpected scenarios and malicious inputs. By leveraging the lessons learned from adversarial attacks, autonomous driving systems can be better equipped to ensure safety and reliability in complex real-world environments.