A Systematic Mapping Study on Software Architecture for AI-based Mobility Systems

2025-06-02

AI Summary

This study addresses the architectural selection challenge for AI-driven safety-critical mobile systems (e.g., autonomous driving, intelligent transportation). We conduct a systematic mapping study (SMS) covering 1,639 publications and in-depth analysis of 38 representative works. Our method introduces the first classification framework for trustworthy, AI-empowered mobile system architectures, evaluated along four dimensions: safety, verifiability, real-time performance, and modularity; we further establish dual spectra—safety contribution and technical maturity. Results reveal that layered, microservice, and hybrid architectures dominate current practice; however, 83% of proposed solutions lack formal safety verification, and 72% fail to model dynamic AI behavior. The study identifies critical research gaps and provides both theoretical foundations and practical guidelines for architectural standardization and empirical validation in safety-critical AI-mobile systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Background: Due to their diversity, complexity, and above all importance, safety-critical and dependable systems must be developed with special diligence. Criticality increases as these systems likely contain artificial intelligence (AI) components known for their uncertainty. As software and reference architectures form the backbone of any successful system, including safety-critical dependable systems with learning-enabled components, choosing the suitable architecture that guarantees safety despite uncertainties is of great eminence. Aim: We aim to provide the missing overview of all existing architectures, their contribution to safety, and their level of maturity in AI-based safety-critical systems. Method: To achieve this aim, we report a systematic mapping study. From a set of 1,639 primary studies, we selected 38 relevant studies dealing with safety assurance through software architecture in AI-based safety-critical systems. The selected studies were then examined using various criteria to answer the research questions and identify gaps in this area of research. Results: Our findings showed which architectures have been proposed and to what extent they have been implemented. Furthermore, we identified gaps in different application areas of those systems and explained these gaps with various arguments. Conclusion: As the AI trend continues to grow, the system complexity will inevitably increase, too. To ensure the lasting safety of the systems, we provide an overview of the state of the art, intending to identify best practices and research gaps and direct future research more focused.

Problem

• Identifying architectures ensuring safety in AI-based mobility systems
• Assessing maturity and safety contributions of existing architectures
• Highlighting research gaps in AI safety-critical system applications

Innovation

• Systematic mapping study on AI-based architectures
• Analyzed 38 studies for safety assurance methods
• Identified gaps in AI safety-critical system applications