Abstract
Autonomous vehicles (AVs) increasingly rely on advanced sensing, Machine Learning (ML), and AI to operate in complex, dynamic environments. Rapid technological change, environmental variability, and evolving regulatory demands create volatility in functional and non-functional requirements, challenging conventional development processes. This exploratory qualitative study examines how requirements evolve in Autonomous Vehicle Perception (AVP) systems and the implications for engineering practice, based on semi-structured interviews with 16 professionals from international automotive companies and research institutes. Findings show functional requirements shifting toward AI-enabled perception, real-time processing, advanced sensor fusion, improved localisation, and region-specific adaptations, while non-functional requirements, including safety, cybersecurity, robustness, reliability, transparency, and scalability, grow more demanding under regulatory, operational, and societal pressures. This volatility creates challenges including sensor uncertainty in adverse conditions, rising development and computational costs, greater compliance burdens, and increased complexity in real-time decision-making. We examine how organisations respond through adaptive practices: Scrum supports rapid ML experimentation, Kanban manages continuous data and annotation workflows, SAFe enables large-scale cross-organisational coordination, and hybrid Agile approaches combined with V-model or Waterfall elements preserve safety, traceability, and compliance. The study contributes empirical insight into adapting Agile and hybrid practices to manage evolving requirements in safety-critical, AI-enabled AV perception development.
Paper Type
Full Paper
DOI
10.62036/ISD.2026.21
Agile Methods for Safety-Critical Systems: Managing Evolving Requirements in Autonomous Vehicle Perception Systems Development
Autonomous vehicles (AVs) increasingly rely on advanced sensing, Machine Learning (ML), and AI to operate in complex, dynamic environments. Rapid technological change, environmental variability, and evolving regulatory demands create volatility in functional and non-functional requirements, challenging conventional development processes. This exploratory qualitative study examines how requirements evolve in Autonomous Vehicle Perception (AVP) systems and the implications for engineering practice, based on semi-structured interviews with 16 professionals from international automotive companies and research institutes. Findings show functional requirements shifting toward AI-enabled perception, real-time processing, advanced sensor fusion, improved localisation, and region-specific adaptations, while non-functional requirements, including safety, cybersecurity, robustness, reliability, transparency, and scalability, grow more demanding under regulatory, operational, and societal pressures. This volatility creates challenges including sensor uncertainty in adverse conditions, rising development and computational costs, greater compliance burdens, and increased complexity in real-time decision-making. We examine how organisations respond through adaptive practices: Scrum supports rapid ML experimentation, Kanban manages continuous data and annotation workflows, SAFe enables large-scale cross-organisational coordination, and hybrid Agile approaches combined with V-model or Waterfall elements preserve safety, traceability, and compliance. The study contributes empirical insight into adapting Agile and hybrid practices to manage evolving requirements in safety-critical, AI-enabled AV perception development.
Recommended Citation
Saeeda, H. & Abraham, S.(2026). Agile Methods for Safety-Critical Systems: Managing Evolving Requirements in Autonomous Vehicle Perception Systems Development. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.21