Nour Habib

Nour Habib, Yunsu Cho, Abhishek Buragohain et al.

One of the many Autonomous Systems (ASs), such as autonomous driving cars, performs various safety-critical functions. Many of these autonomous systems take advantage of Artificial Intelligence (AI) techniques to perceive their environment. But these perceiving components could not be formally verified, since, the accuracy of such AI-based components has a high dependency on the quality of training data. So Machine learning (ML) based anomaly detection, a technique to identify data that does not belong to the training data could be used as a safety measuring indicator during the development and operational time of such AI-based components. Adversarial learning, a sub-field of machine learning has proven its ability to detect anomalies in images and videos with impressive results on simple data sets. Therefore, in this work, we investigate and provide insight into the performance of such techniques on a highly complex driving scenes dataset called Berkeley DeepDrive.

Iqra Aslam, Nour Habib, Abhishek Buragohain et al.

The advancement of automated vehicles introduces complex safety challenges, particularly in dynamic and unpredictable environments where AI-enabled perception systems must operate reliably. Ensuring compliance with safety standards such as ISO 26262 and ISO/PAS 21448 (SOTIF) is essential for addressing system malfunctions and mitigating unsafe behavior in unknown scenarios. However, as automation levels increase, vehicles must go beyond conventional functional safety by incorporating fail-operational capabilities that enable continued safe operation during system or component failures and the handling of unfamiliar or degraded operational conditions. To address these safety concerns, we propose the Connected Dependability Cage, an architectural framework designed to enable hierarchical fail-operational behavior in AI-enabled perception systems. This framework integrates two complementary monitoring mechanisms: a Function Monitor that oversees multiple heterogeneous AI-based perception pipelines and detects inconsistencies through a voting mechanism, and an Anomaly Monitor that evaluates the reliability of AI perception by detecting unknown or novel objects in scenes that may be excluded from the training dataset. In the presence of critical discrepancies, the system supports graceful degradation, ultimately enabling a transition to a minimal-risk maneuver strategy. Furthermore, whenever either monitor raises a safety flag, an automated data recording process is initiated to facilitate iterative system development and continuous improvement. Both monitors have been implemented and validated through extensive vehicle testing, demonstrating their practical effectiveness in real-world applications.