AI-Native Requirements Management for Automotive Engineering

ASIL, or Automotive Safety Integrity Level, is the risk classification ISO 26262 assigns to a hazard, from ASIL A at the lowest to ASIL D at the highest. The level sets how much verification a safety requirement needs. Trace.Space maintains traceability from safety goals through functional, technical, and software safety requirements to verification evidence, so each requirement carries the evidence its ASIL demands and gaps show up in real time.

ASPICE and ISO 26262 differ in what they assess: ISO 26262 governs functional safety of the vehicle, while ASPICE measures the maturity of the development process behind it. A program often has to satisfy both, since ASPICE wants process and traceability evidence while ISO 26262 wants safety requirements traced to verification. Trace.Space produces ISO 26262 safety-case traceability and ASPICE coverage reports from the same requirement data.

Yes, one platform can manage ISO 26262, ASPICE, and ISO 21434 together, and Trace.Space tracks all three against the same requirements. Functional safety, process evidence, and cybersecurity traceability share a single data model. That removes the manual work of reconciling safety, security, and process evidence from separate tools when an audit arrives.

Trace.Space manages software-defined-vehicle requirements by tracing them across mechanical, electrical, software, and electronics domains in one view, including the thousands of safety-critical requirements and dozens of ECUs a modern car carries. When an ECU software requirement changes, the platform shows every affected hardware interface, calibration input, and safety case instantly. Variant management then keeps those requirements straight across platforms, trim levels, and regional configurations.

Trace.Space supports SOTIF, or ISO/SAE 21448, by tracing safety-of-the-intended-functionality requirements alongside ISO 26262 functional safety requirements in the same structure. SOTIF covers hazards that come from performance limitations rather than system faults, which matters most for ADAS and automated driving. The platform links those requirements to scenario analysis and verification evidence, so coverage stays visible as the functionality evolves.

AI helps catch requirement issues by mapping the ripple effects of a change across every domain, so a single ECU requirement edit shows its impact on hardware interfaces, calibration, and system-level safety cases at once. It also flags missing context, conflicts, and coverage gaps that manual reviews tend to miss across dozens of ECUs. Engineers stay in control and decide what to accept, since the AI suggests rather than dictates.