arXiv subjects

Stefano Carlo Lambertenghi

Publications and source records attributed to Stefano Carlo Lambertenghi.

9 recordsLinked to original sources

arXiv2026

Cam2Sim: Neural Scenario Reconstruction for Closed-Loop Autonomous Driving Simulation

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 real-world camera observations. To address this problem, we present Cam2Sim, a tool that transforms real-world driving recordings into playable CARLA simulation scenarios. Starting from camera images and poses, Cam2Sim reconstructs road geometry, ego trajectories, parked vehicles, and simulation assets, and augments the reconstructed environment with Gaussian Splatting to render camera observations that resemble the original recording. The framework supports ROS-based data extraction, parked-vehicle detection, OpenStreetMap-based map generation, CARLA scenario construction, Gaussian Splatting training, trajectory replay, and closed-loop execution with a system under test. We validate Cam2Sim on a real-world urban-driving scenario with a camera-based end-to-end driving model, comparing reconstruction quality, image-generation quality, and closed-loop behavior against both a simulation-only baseline and the real-world target. Results show that Gaussian-Splatting-based rendering reduces the visual gap with respect to standard simulator rendering and improves behavioral similarity to the real-world reference runs. The artifact is publicly available at https: //github.com/ast-fortiss-tum/cam2sim, and a screencast showing the tool is available at https://youtu.be/KmZ74l1__lI

cs.SE↗

arXiv2026

Real-World Perturbation Testing of Autonomous Driving Systems

Autonomous Driving Systems (ADS) must operate reliably under diverse conditions, yet representative data for rare or adverse scenarios is difficult to obtain. Perturbation-based testing is widely used to assess robustness, but most studies focus on offline datasets or simulation, leaving open questions about how such results translate to real-world driving. We present a large-scale study of 72 camera and LiDAR perturbations, evaluated across three testing modalities: offline model-level analysis, hardware-in-the-loop execution, and closed-loop system-level testing on a full-scale autonomous vehicle. The study covers both an end-to-end vision-based driving model and a modular LiDAR-based perception and planning stack. Our results reveal a clear gap between testing levels. For camera-based systems, perturbations with limited offline impact can still induce unstable control and failures in real-world driving. For LiDAR-based systems, degradation is more consistent at the perception level but weakly predictive of system-level failures. Across both modalities, model-level metrics alone are insufficient to identify the most harmful perturbations. We further show that real-time feasibility is a key constraint in real-world testing, and that robustness observations obtained from recorded data do not consistently transfer to closed-loop behavior on a physical vehicle, highlighting the importance of complementary real-world, system-level evaluation.

cs.SE↗

arXiv2026

PerturbationDrive: A Framework for Perturbation-Based Testing of ADAS

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.

cs.SE↗

arXiv2025

Misbehavior Forecasting for Focused Autonomous Driving Systems Testing

Simulation-based testing is the standard practice for assessing the reliability of self-driving cars' software before deployment. Existing bug-finding techniques are either unreliable or expensive. We build on the insight that near misses observed during simulations may point to potential failures. We propose Foresee, a technique that identifies near misses using a misbehavior forecaster that computes possible future states of the ego-vehicle under test. Foresee performs local fuzzing in the neighborhood of each candidate near miss to surface previously unknown failures. In our empirical study, we evaluate the effectiveness of different configurations of Foresee using several scenarios provided in the CARLA simulator on both end-to-end and modular self-driving systems and examine its complementarity with the state-of-the-art fuzzer DriveFuzz. Our results show that Foresee is both more effective and more efficient than the baselines. Foresee exposes 128.70% and 38.09% more failures than a random approach and a state-of-the-art failure predictor while being 2.49x and 1.42x faster, respectively. Moreover, when used in combination with DriveFuzz, Foresee enhances failure detection by up to 93.94%.

cs.SE↗

arXiv2025

A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems

Simulation-based testing is a cornerstone of Autonomous Driving System (ADS) development, offering safe and scalable evaluation across diverse driving scenarios. However, discrepancies between simulated and real-world behavior, known as the reality gap, challenge the transferability of test results to deployed systems. In this paper, we present a comprehensive empirical study comparing four representative testing modalities: Software-in-the-Loop (SiL), Vehicle-in-the-Loop (ViL), Mixed-Reality (MR), and full real-world testing. Using a small-scale physical vehicle equipped with real sensors (camera and LiDAR) and its digital twin, we implement each setup and evaluate two ADS architectures (modular and end-to-end) across diverse indoor driving scenarios involving real obstacles, road topologies, and indoor environments. We systematically assess the impact of each testing modality along three dimensions of the reality gap: actuation, perception, and behavioral fidelity. Our results show that while SiL and ViL setups simplify critical aspects of real-world dynamics and sensing, MR testing improves perceptual realism without compromising safety or control. Importantly, we identify the conditions under which failures do not transfer across testing modalities and isolate the underlying dimensions of the gap responsible for these discrepancies. Our findings offer actionable insights into the respective strengths and limitations of each modality and outline a path toward more robust and transferable validation of autonomous driving systems.

cs.SE↗

arXiv2025

Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems

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, semantic segmentation, and lane recognition. However, these systems are highly sensitive to input variations, such as noise and changes in lighting, which can compromise their effectiveness and potentially lead to safety-critical failures. This study offers a comprehensive empirical evaluation of image perturbations, techniques commonly used to assess the robustness of DNNs, to validate and improve the robustness and generalization of ADAS perception systems. We first conducted a systematic review of the literature, identifying 38 categories of perturbations. Next, we evaluated their effectiveness in revealing failures in two different ADAS, both at the component and at the system level. Finally, we explored the use of perturbation-based data augmentation and continuous learning strategies to improve ADAS adaptation to new operational design domains. Our results demonstrate that all categories of image perturbations successfully expose robustness issues in ADAS and that the use of dataset augmentation and continuous learning significantly improves ADAS performance in novel, unseen environments.

cs.SE↗

arXiv2024

Assessing Quality Metrics for Neural Reality Gap Input Mitigation in Autonomous Driving Testing

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 advantages, virtual simulations often fail to accurately replicate real-world conditions like image fidelity, texture representation, and environmental accuracy. This can lead to significant differences in ADS behavior between simulated and real-world domains, a phenomenon known as the sim2real gap. Researchers have used Image-to-Image (I2I) neural translation to mitigate the sim2real gap, enhancing the realism of simulated environments by transforming synthetic data into more authentic representations of real-world conditions. However, while promising, these techniques may potentially introduce artifacts, distortions, or inconsistencies in the generated data that can affect the effectiveness of ADS testing. In our empirical study, we investigated how the quality of image-to-image (I2I) techniques influences the mitigation of the sim2real gap, using a set of established metrics from the literature. We evaluated two popular generative I2I architectures, pix2pix, and CycleGAN, across two ADS perception tasks at a model level, namely vehicle detection and end-to-end lane keeping, using paired simulated and real-world datasets. Our findings reveal that the effectiveness of I2I architectures varies across different ADS tasks, and existing evaluation metrics do not consistently align with the ADS behavior. Thus, we conducted task-specific fine-tuning of perception metrics, which yielded a stronger correlation. Our findings indicate that a perception metric that incorporates semantic elements, tailored to each task, can facilitate selecting the most appropriate I2I technique for a reliable assessment of the sim2real gap mitigation.

cs.SE↗

arXiv2023

Investigating GANsformer: A Replication Study of a State-of-the-Art Image Generation Model

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 non-existing objects, such as interior design scenes, products or even human faces, and achieving transfer-learning processes. In this context, Generative Adversarial Networks (GANs) are a class of widely studied machine learning frameworks first appearing in the paper "Generative adversarial nets" by Goodfellow et al. that achieve the goal above. In our work, we reproduce and evaluate a novel variation of the original GAN network, the GANformer, proposed in "Generative Adversarial Transformers" by Hudson and Zitnick. This project aimed to recreate the methods presented in this paper to reproduce the original results and comment on the authors' claims. Due to resources and time limitations, we had to constrain the network's training times, dataset types, and sizes. Our research successfully recreated both variations of the proposed GANformer model and found differences between the authors' and our results. Moreover, discrepancies between the publication methodology and the one implemented, made available in the code, allowed us to study two undisclosed variations of the presented procedures.

cs.CV↗

arXiv2023

Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors

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 localization, through deep neural networks (DNNs), of two peer nano-drones, i.e., sub-40g of weight and sub-100mW processing power. To cope with the ultra-constrained nano-drone platform, we propose a vertically-integrated framework, from the dataset collection to the final in-field deployment, including dataset augmentation, quantization, and system optimizations. Experimental results show that our DNN can precisely localize a 10cm-size target nano-drone by employing only low-resolution monochrome images, up to ~2m distance. On a disjoint testing dataset our model yields a mean R2 score of 0.42 and a root mean square error of 18cm, which results in a mean in-field prediction error of 15cm and in a closed-loop control error of 17cm, over a ~60s-flight test. Ultimately, the proposed system improves the State-of-the-Art by showing long-endurance tracking performance (up to 2min continuous tracking), generalization capabilities being deployed in a never-seen-before environment, and requiring a minimal power consumption of 95mW for an onboard real-time inference-rate of 48Hz.

cs.RO↗