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en:safeav:as:vreq [2026/04/08 10:12] – airien:safeav:as:vreq [2026/05/18 15:03] (current) – pczekalski
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 ====== Validation Requirements across Domains ====== ====== Validation Requirements across Domains ======
-{{:en:iot-open:czapka_b.png?50| Bachelors (1st level) classification icon }}+<todo @pczekalski>Introduce cybersecurity chapter - add V&V relation for cybersecurity</todo>
  
-<todo @rahulrazdan #raivo.sell:2025-09-18></todo> +In terms of domains, the Operational Design Domain (ODD) is the driving factor, and typically has two dimensions. The first is the operational model, and the second is the physical domain (ground, airborne, marine, space). On the ground, Passenger AVs are perhaps the most well-known face of autonomy, with robo-taxi services and self-driving consumer vehicles gradually entering urban environments. Companies like Waymo, Cruise, and Tesla have taken different approaches to ODDs. Waymo’s fully driverless cars operate in sunny, geo-fenced suburbs of Phoenix with detailed mapping and remote supervision. Cruise began service in San Francisco, originally operating only at night to reduce complexity. Tesla’s Full Self Driving (FSD) Beta aims for broader generalization, but it still relies heavily on driver supervision and is limited by weather and visibility challenges. 
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-In terms of domains, the Operational Design Domain (ODD) is the driving factor, and typically have two dimensions. The first is the operational model and the second is the physical domain (ground, airborne, marine, space). In terms of ground, Passenger AVs are perhaps the most well-known face of autonomy, with robo-taxi services and self-driving consumer vehicles gradually entering urban environments. Companies like Waymo, Cruise, and Tesla have taken different approaches to ODDs. Waymo’s fully driverless cars operate in sunny, geo-fenced suburbs of Phoenix with detailed mapping and remote supervision. Cruise began service in San Francisco, originally operating only at night to reduce complexity. Tesla’s Full Self Driving (FSD) Beta aims for broader generalization, but it still relies heavily on driver supervision and is limited by weather and visibility challenges. +
  
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 Autonomous aircraft (e.g., drones, urban air mobility platforms, and optionally piloted systems) must operate in highly structured, safety-critical environments. Validation involves rigorous formal methods, fault tolerance analysis, and conformance with aviation safety standards such as DO-178C (software), DO-254 (hardware), and emerging guidance like ASTM F38 and EASA's SC-VTOL. Airspace governance is centralized and mature, often requiring type certification and airworthiness approvals. Unlike automotive systems, airborne autonomy must prove reliability in loss-of-link scenarios and demonstrate fail-operational capabilities across flight phases. Autonomous aircraft (e.g., drones, urban air mobility platforms, and optionally piloted systems) must operate in highly structured, safety-critical environments. Validation involves rigorous formal methods, fault tolerance analysis, and conformance with aviation safety standards such as DO-178C (software), DO-254 (hardware), and emerging guidance like ASTM F38 and EASA's SC-VTOL. Airspace governance is centralized and mature, often requiring type certification and airworthiness approvals. Unlike automotive systems, airborne autonomy must prove reliability in loss-of-link scenarios and demonstrate fail-operational capabilities across flight phases.
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 Autonomous surface and underwater marine systems face unstructured and communication-constrained environments. They must operate reliably in GPS-denied or RF-blocked conditions while detecting obstacles like buoys, vessels, or underwater terrain. Validation is more empirical, often involving extended sea trials, redundancy in navigation systems, and adaptive mission planning. IMO (International Maritime Organization) and classification societies like DNV are working on Maritime Autonomous Surface Ship (MASS) regulatory frameworks, though global standards are still nascent. The dual-use nature of marine autonomy (civil and defense) adds governance complexity. Space-based autonomous systems (e.g., planetary rovers, autonomous docking spacecraft, and space tugs) operate under extreme constraints: communication delays, radiation exposure, and no real-time human oversight. Validation occurs through rigorous testing on Earth-based analog environments, formal verification of critical software, and fail-safe design principles. Governance falls under national space agencies (e.g., NASA, ESA) and international frameworks like the Outer Space Treaty. Assurance relies on mission-specific autonomy envelopes and pre-defined decision trees rather than reactive autonomy. Autonomous surface and underwater marine systems face unstructured and communication-constrained environments. They must operate reliably in GPS-denied or RF-blocked conditions while detecting obstacles like buoys, vessels, or underwater terrain. Validation is more empirical, often involving extended sea trials, redundancy in navigation systems, and adaptive mission planning. IMO (International Maritime Organization) and classification societies like DNV are working on Maritime Autonomous Surface Ship (MASS) regulatory frameworks, though global standards are still nascent. The dual-use nature of marine autonomy (civil and defense) adds governance complexity. Space-based autonomous systems (e.g., planetary rovers, autonomous docking spacecraft, and space tugs) operate under extreme constraints: communication delays, radiation exposure, and no real-time human oversight. Validation occurs through rigorous testing on Earth-based analog environments, formal verification of critical software, and fail-safe design principles. Governance falls under national space agencies (e.g., NASA, ESA) and international frameworks like the Outer Space Treaty. Assurance relies on mission-specific autonomy envelopes and pre-defined decision trees rather than reactive autonomy.
  
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