7 papers
Real-World Perturbation Testing of Autonomous Driving Systems
Stefano Carlo Lambertenghi, Matthias Weil, Andrea Stocco
Autonomous Driving Systems (ADS) must operate reliably under diverse conditions, yet representative data for rare or adverse scenarios is difficult to obtain. Perturbation-based te…
Cam2Sim: Neural Scenario Reconstruction for Closed-Loop Autonomous Driving Simulation
Davide Jannussi, Stefano Carlo Lambertenghi, Constantin Carste +1
Simulation-based testing enables safe and repeatable evaluation of autonomous driving systems, but its effectiveness is limited by the gap between synthetic simulator outputs and r…
PerturbationDrive: A Framework for Perturbation-Based Testing of ADAS
Hannes Leonhard, Stefano Carlo Lambertenghi, Andrea Stocco
Advanced driver assistance systems (ADAS) often rely on deep neural networks to interpret driving images and support vehicle control. Although reliable under nominal conditions, th…
Misbehavior Forecasting for Focused Autonomous Driving Systems Testing
M M Abid Naziri, Stefano Carlo Lambertenghi, Andrea Stocco +1
Simulation-based testing is the standard practice for assessing the reliability of self-driving cars' software before deployment. Existing bug-finding techniques are either unrelia…
A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems
Stefano Carlo Lambertenghi, Mirena Flores Valdez, Andrea Stocco
Simulation-based testing is a cornerstone of Autonomous Driving System (ADS) development, offering safe and scalable evaluation across diverse driving scenarios. However, discrepan…
Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems
Stefano Carlo Lambertenghi, Hannes Leonhard, Andrea Stocco
Advanced Driver Assistance Systems (ADAS) based on deep neural networks (DNNs) are widely used in autonomous vehicles for critical perception tasks such as object detection, semant…