arXiv subjects

Abhishek Buragohain

Publications and source records attributed to Abhishek Buragohain.

4 recordsLinked to original sources

arXiv2026

Connected Dependability Cage: Run-Time Function and Anomaly Monitoring for the Development and Operation of Safe Automated Vehicles

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.

cs.RO↗

arXiv2024

A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System

Environment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving Systems (ADS). Artificial Intelligence (AI)-based approaches have prevailed over classical techniques for realizing the environment perception. Current safety-relevant standards for automotive systems, International Organization for Standardization (ISO) 26262 and ISO 21448, assume the existence of comprehensive requirements specifications. These specifications serve as the basis on which the functionality of an automotive system can be rigorously tested and checked for compliance with safety regulations. However, AI-based perception systems do not have complete requirements specification. Instead, large datasets are used to train AI-based perception systems. This paper presents a function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS, based respectively on camera and LiDAR sensors. To evaluate the applicability of the function monitor, we conduct a qualitative scenario-based evaluation in a controlled laboratory environment using a model car. The evaluation results then are discussed to provide insights into the monitor's performance and its suitability for real-world applications.

cs.RO↗

arXiv2023

Runtime Safety Assurance of Autonomous Vehicles used for Last-mile Delivery in Urban Environments

Last-mile delivery of goods has gained a lot of attraction during the COVID-19 pandemic. However, current package delivery processes often lead to parking in the second lane, which in turn has negative effects on the urban environment in which the deliveries take place, i.e., traffic congestion and safety issues for other road users. To tackle these challenges, an effective autonomous delivery system is required that guarantees efficient, flexible and safe delivery of goods. The project LogiSmile, co-funded by EIT Urban Mobility, pilots an autonomous delivery vehicle dubbed the Autonomous Hub Vehicle (AHV) that works in cooperation with a small autonomous robot called the Autonomous Delivery Device (ADD). With the two cooperating robots, the project LogiSmile aims to find a possible solution to the challenges of urban goods distribution in congested areas and to demonstrate the future of urban mobility. As a member of Niedersächsische Forschungszentrum für Fahrzeugtechnik (NFF), the Institute for Software and Systems Engineering (ISSE) developed an integrated software safety architecture for runtime monitoring of the AHV, with (1) a dependability cage (DC) used for the on-board monitoring of the AHV, and (2) a remote command control center (CCC) which enables the remote off-board supervision of a fleet of AHVs. The DC supervises the vehicle continuously and in case of any safety violation, it switches the nominal driving mode to degraded driving mode or fail-safe mode. Additionally, the CCC also manages the communication of the AHV with the ADD and provides fail-operational solutions for the AHV when it cannot handle complex situations autonomously. The runtime monitoring concept developed for the AHV has been demonstrated in 2022 in Hamburg. We report on the obtained results and on the lessons learned.

cs.SE↗

arXiv2023

Towards exploring adversarial learning for anomaly detection in complex driving scenes

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.

cs.CV↗