Contextual Observability of Software-Defined Vehicles
Software-defined vehicles (SDVs) address rising software complexity, reduce electronic control units (ECUs), and separate hardware from software, allowing for easier updates and enhanced vehicle lifecycle management. Over-the-air (OTA) updates offer dynamic functionality and improved user interaction, while SDVs provide cost efficiency, weight reduction, and faster time to market. However, challenges include achieving comprehensive observability in distributed architectures, cybersecurity risks, software maintenance complexities, high development costs, and data privacy concerns. This project aims to develop a testbed for SDV contextual observability. This testbed will enable collecting multimodal telemetry data, facilitating continuous monitoring, advanced analytics, causal inference and incident response to proactively detect and mitigate issues.
Funding agency: IRC for Smart Mobility and Logistics (SML) at KFUPM
Duration: 2025-2027
Topics: Software-defined vehicles, contextual observability, adavanced analytics, causal inference, automated incident response.