Software-Defined Vehicles: Challenges and Orchestrating Mixed-Criticality Services Using Lingua Franca

Software-Defined Vehicles (SDVs) consolidate safety-and non-safety-critical functions onto centralized platforms, forming mixed-criticality systems (MCSes) where applications like ADAS and infotainment coexist. While this reduces cost and complexity, it also introduces challenges in isolation, timing determinism, and safety certification. Lingua Franca (LF) offers deterministic coordination but lacks mechanisms for per-application monitoring and recovery. To address this, we (1) derive orchestration requirements from the “Safety First for Automated Driving” white paper, and (2) propose an LF-compatible framework with external monitors that detect faults and enable selective restarts of faulty applications without disrupting others. This work presents the first practical per-application failover solution for LF, improving the reliability of SDV mixed-criticality orchestration.