Functionality Assessment Framework for Autonomous Driving Systems using Subjective Networks

2025-06-03

AI Summary

Functional assessment of autonomous driving (AD) systems faces challenges including isolated component evaluation, neglect of inter-component dependencies, inadequate modeling of redundancy and error propagation, and difficulty in fusing conflicting information. Method: This paper proposes the first end-to-end functional assessment framework for AD systems based on Subjective Networks (SNs). It uniformly models component functional states, dependency relations, redundancy structures, and error propagation paths, and employs uncertainty-aware inference and redundancy-sensitive fusion to achieve coherent integration of conflicting assessments and precise fault localization. Contribution/Results: Unlike conventional approaches relying solely on isolated safety or performance metrics, this work pioneers the application of SNs to AD functional assessment, overcoming the critical bottleneck of synthesizing a global functional view from local measurements. Experiments on real-world AD system data demonstrate that the framework accurately identifies faulty components and produces consistent, interpretable, end-to-end functional confidence assessments.

Technology Category

• Intelligent Robots: State Estimation
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

In complex autonomous driving (AD) software systems, the functioning of each system part is crucial for safe operation. By measuring the current functionality or operability of individual components an isolated glimpse into the system is given. Literature provides several of these detached assessments, often in the form of safety or performance measures. But dependencies, redundancies, error propagation and conflicting functionality statements do not allow for easy combination of these measures into a big picture of the functioning of the entire AD stack. Data is processed and exchanged between different components, each of which can fail, making an overall statement challenging. The lack of functionality assessment frameworks that tackle these problems underlines this complexity. This article presents a novel framework for inferring an overall functionality statement for complex component based systems by considering their dependencies, redundancies, error propagation paths and the assessments of individual components. Our framework first incorporates a comprehensive conversion to an assessment representation of the system. The representation is based on Subjective Networks (SNs) that allow for easy identification of faulty system parts. Second, the framework offers a flexible method for computing the system's functionality while dealing with contradicting assessments about the same component and dependencies, as well as redundancies, of the system. We discuss the framework's capabilities on real-life data of our AD stack with assessments of various components.

Problem

• Assessing overall functionality of autonomous driving systems with dependencies
• Combining isolated component assessments into a unified system view
• Handling conflicting functionality statements and error propagation paths

Innovation

• Uses Subjective Networks for fault identification
• Integrates component dependencies and redundancies
• Flexible computation for contradicting assessments