Problem
Research questions and friction points this paper is trying to address.
ADAS
robustness
generalization
input perturbations
out-of-distribution
PerturbationDrive: A Framework for Perturbation-Based Testing of ADAS
📝 Abstract
Advanced driver assistance systems (ADAS) often rely on deep neural networks to interpret driving images and support vehicle control. Although reliable under nominal conditions, these systems remain vulnerable to input variations and out-of-distribution data, which can lead to unsafe behavior. To this aim, this tool paper presents the architecture and functioning of PerturbationDrive, a testing framework to perform robustness and generalization testing of ADAS. The framework features more than 30 image perturbations from the literature that mimic changes in weather, lighting, or sensor quality and extends them with dynamic and attention-based variants. PerturbationDrive supports both offline evaluation on static datasets and online closed-loop testing in different simulators. Additionally, the framework integrates with procedural road generation and search-based testing, enabling systematic exploration of diverse road topologies combined with image perturbations. Together, these features allow PerturbationDrive to evaluate robustness and generalization capabilities of ADAS across varying scenarios, making it a reproducible and extensible framework for systematic system-level testing.
Innovation
Methods, ideas, or system contributions that make the work stand out.
perturbation-based testing
ADAS robustness
dynamic perturbations
closed-loop simulation
procedural road generation
🔎 Similar Papers
No similar papers found.
