Enhancing the Transferability of Adversarial Attacks for End-to-End Autonomous Driving Systems

Adversarial attacks play an important role in testing and enhancing the reliability of deep learning (DL) systems. Most existing attacks for DL-based autonomous driving systems (ADSs) demonstrate strong performance under the white-box setting but struggle with black-box transferability, while black-box attacks are more practical in real-world scenarios as they operate without full model access. Numerous transferability-enhancement techniques have been proposed in other fields (e.g., image classification), however, they remain unexplored for end-to-end (E2E) ADSs.
Our study fills the gap by conducting the first comprehensive empirical analysis of nine transferability-enhancement methods on E2E ADSs, covering two types: three input transformation enhancements and six attack objective enhancements. We evaluate their effectiveness on two datasets with four steering models. Our findings reveal that, out of nine enhancements, Resizing+Translation delivers the best black-box transferability, producing up to 9.39° increase in MAE. Pred+Attn serves as the best objective enhancement, producing a maximum of 5.55° (white-box) and 6.21° (black-box) increase in MAE. Through attention heatmap visualizations, we discover that different models focus on similar regions when predicting, thereby enhancing the transferability of attention-based attacks.
In conclusion, our study provides valuable results and insights into the transferability-enhancement techniques for E2E ADSs, which also serve as a robust benchmark for further advancements in the autonomous driving field.

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