arXiv subjects

Iqra Aslam

Publications and source records attributed to Iqra Aslam.

5 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↗

arXiv2024

Towards Selection and Transition Between Behavior-Based Neural Networks for Automated Driving

Autonomous driving technology is progressing rapidly, largely due to complex End To End systems based on deep neural networks. While these systems are effective, their complexity can make it difficult to understand their behavior, raising safety concerns. This paper presents a new solution a Behavior Selector that uses multiple smaller artificial neural networks (ANNs) to manage different driving tasks, such as lane following and turning. Rather than relying on a single large network, which can be burdensome, require extensive training data, and is hard to understand, the developed approach allows the system to dynamically select the appropriate neural network for each specific behavior (e.g., turns) in real time. We focus on ensuring smooth transitions between behaviors while considering the vehicles current speed and orientation to improve stability and safety. The proposed system has been tested using the AirSim simulation environment, demonstrating its effectiveness.

cs.RO↗

arXiv2023

Connected Dependability Cage Approach for Safe Automated Driving

Automated driving systems can be helpful in a wide range of societal challenges, e.g., mobility-on-demand and transportation logistics for last-mile delivery, by aiding the vehicle driver or taking over the responsibility for the dynamic driving task partially or completely. Ensuring the safety of automated driving systems is no trivial task, even more so for those systems of SAE Level 3 or above. To achieve this, mechanisms are needed that can continuously monitor the system's operating conditions, also denoted as the system's operational design domain. This paper presents a safety concept for automated driving systems which uses a combination of onboard runtime monitoring via connected dependability cage and off-board runtime monitoring via a remote command control center, to continuously monitor the system's ODD. On one side, the connected dependability cage fulfills a double functionality: (1) to monitor continuously the operational design domain of the automated driving system, and (2) to transfer the responsibility in a smooth and safe manner between the automated driving system and the off-board remote safety driver, who is present in the remote command control center. On the other side, the remote command control center enables the remote safety driver the monitoring and takeover of the vehicle's control. We evaluate our safety concept for automated driving systems in a lab environment and on a test field track and report on results and lessons learned.

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↗