Generating Transferable Adversarial Simulation Scenarios for Self-Driving via Neural RenderingDownload PDF

Published: 30 Aug 2023, Last Modified: 27 Oct 2023CoRL 2023 PosterReaders: Everyone
Keywords: robotics, adversarial attacks, simulation
TL;DR: Adversarial attacks for autonomous driving using differentiable surrogate simulators
Abstract: Self-driving software pipelines include components that are learned from a significant number of training examples, yet it remains challenging to evaluate the overall system's safety and generalization performance. Together with scaling up the real-world deployment of autonomous vehicles, it is of critical importance to automatically find simulation scenarios where the driving policies will fail. We propose a method that efficiently generates adversarial simulation scenarios for autonomous driving by solving an optimal control problem that aims to maximally perturb the policy from its nominal trajectory. Given an image-based driving policy, we show that we can inject new objects in a neural rendering representation of the deployment scene, and optimize their texture in order to generate adversarial sensor inputs to the policy. We demonstrate that adversarial scenarios discovered purely in the neural renderer (surrogate scene) can often be successfully transferred to the deployment scene, without further optimization. We demonstrate this transfer occurs both in simulated and real environments, provided the learned surrogate scene is sufficiently close to the deployment scene.
Student First Author: yes
Supplementary Material: zip
Instructions: I have read the instructions for authors (
Publication Agreement: pdf
Poster Spotlight Video: mp4
6 Replies