14 citations · 17 across the 9 of their papers we have counts for
10 papers
The Reality Gap in Automated Driving Systems Testing: Landscape, Challenges, and Research Directions
Stefano Carlo Lambertenghi, Oliver Weissl, Johannes Betz +1
Simulation is essential for testing automated driving systems (ADS), but discrepancies between simulation and reality can undermine testing conclusions. It remains unclear which di…
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…
Assessing Quality Metrics for Neural Reality Gap Input Mitigation in Autonomous Driving Testing
Stefano Carlo Lambertenghi, Andrea Stocco
Simulation-based testing of automated driving systems (ADS) is the industry standard, being a controlled, safe, and cost-effective alternative to real-world testing. Despite these…
Investigating GANsformer: A Replication Study of a State-of-the-Art Image Generation Model
Giorgia Adorni, Felix Boelter, Stefano Carlo Lambertenghi
The field of image generation through generative modelling is abundantly discussed nowadays. It can be used for various applications, such as up-scaling existing images, creating n…
Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors
Stefano Bonato, Stefano Carlo Lambertenghi, Elia Cereda +2
Precise relative localization is a crucial functional block for swarm robotics. This work presents a novel autonomous end-to-end system that addresses the monocular relative locali…