GCRI · Inteligencia artificial y sistemas autónomos
Sistemas físicos autónomos
Autonomous vehicles, drones, industrial and service robots sense their surroundings and act in the physical world, where errors can injure people and damage property. Their safety depends on perception, decision-making, fallback behaviour and the environments they are allowed to operate in. This page sets out how autonomous physical sistemas fail, how their safety is assessed, and what evidence serves the regulators, operators and communities involved.
Autonomous physical systems on the record.
Autonomous physical systems: how the risk develops.
Robots, sensors and controls. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Perception meets the unexpected
Sensors and models misread rare situations: unusual objects, poor weather, glare or confusing road layouts. Rare cases dominate risk because they are hard to test.
Operating domains set limits
sistemas are designed for an operational design domain of roads, weather, speeds or spaces. Failures increase when systems are used outside it.
Handover to humans is hard
Supervisors lose attention when automation works most of the time, and cannot take over instantly when it fails.
Safety cases must be argued
Showing that a system is acceptably safe needs a structured argument combining design, testing, simulation and field data, because driving or operating enough to see rare failures is impractical.
Agent oversight console.
La pantalla que utilizaría un equipo para seguir esta materia: sus mediciones, estados y decisiones. Los nombres son ejemplos y los valores son ilustrativos.
Agent oversight console
Autonomous physical systems · Agent sandbox · Session AG-7276
Tool calls
- 10:27Infosearch(documents) · read-only
- 10:20Inforun_model(flood_nowcast) · sandbox
- 10:39Hitosend_message(external) · needs approval
- 10:45Errorwrite_file(outside workspace) · denied
Política
- Alcance
- workspace only
- External actions
- human approval
- Spend limit
- 11 units
- Kill switch
- Armed
Actions by approval class
Autonomous machines operate in physical domains with physical risks.
- AlimentaciónAcoplamientoAutonomous farm machinery operates near people and animals.What to watchSafety incidents
- EnergíaAcoplamientoDrones and robots inspect and repair grids.What to watchInspection reliability
- SaludAcoplamientoRobots in hospitals need safety and hygiene controls.What to watchIncident reports
Findings are designed for the serie Labs Evidence. SAE J3016 defines levels of driving automation, and the EU Machinery Regulation (2023) addresses machines with self-evolving behaviour.
- Field safety incident data.
- Test protocols for autonomy in unstructured settings.
How autonomous physical systems is modelled, measured and tested.
El modelo, sus supuestos, sus datos y su incertidumbre, en la forma en que un científico o un ingeniero los comprobaría. Las estructuras siguen métodos publicados. Los valores representados son ilustrativos, no hallazgos.
Modelo de sistema
The couplings between the systems in autonomous physical systems: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Qué se mide y con qué calidad.
Cada indicador lleva su unidad, su periodicidad, el tipo de fuente que lo respalda y la etiqueta de evidencia que llevaría un valor. No se muestran valores: un programa los aporta bajo su propia autoridad.
| Indicador | Unidad | Cadencia | Tipo de fuente | Etiqueta |
|---|---|---|---|---|
| Crashes involving automated driving sistemas | count by severity | mensual | Mandatory crash reporting | Estadísticas publicadas |
| Disengagements or interventions | count per distance or hour | anual | Operator reports to regulators | NOTIFICADO |
| Distance driven autonomously | kilometres | anual | Operator reports | DECLARADO |
| Industrial robot injuries | recuento | anual | Occupational injury statistics | Estadísticas publicadas |
| Operating domain exceedances | count of events outside design domain | per deployment | Operator logs | OBSERVADO |
| Drone incidents near aircraft | recuento | anual | Aviation safety reporting | NOTIFICADO |
De dónde procede la evidencia.
Conjuntos de datos públicos y registros institucionales identificados, cada uno con su tipo de licencia. Las fuentes obtenidas por acuerdo solo se utilizan conforme a sus condiciones y nunca se republican.
| Fuente | Lo que aporta | Tipo de licencia |
|---|---|---|
| NHTSA Standing General Order crash reports | Crashes involving automated driving and driver assistance sistemas in the US | Public domain, US government |
| California DMV autonomous vehicle reports | Collision and disengagement reports from permit holders | Registros públicos |
| UNECE regulations on automated driving, including UN Regulation No. 157 | Type approval requirements for automated lane keeping and related systems | Public, UNECE terms |
| ISO 10218 e ISO/TS 15066 | Safety requirements for industrial and collaborative robots | Licensed standards, purchase required |
| Nacional occupational injury statistics | Injuries involving machinery and robots | Public records, licence per government |
Quién decide y a qué sirve la evidencia.
Estudio de Inteligencia artificial y sistemas prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Quién decide | La pregunta | Evidencia a la que sirve esta página |
|---|---|---|
| Transport and aviation regulators | Whether and where to permit autonomous operation | Crash data with exposure; safety case reviews |
| Workplace safety regulators | What safety requirements to set for robots | Injury statistics; standards |
| Operators and manufacturers | How to define and enforce operating domains | Domain documentation; exceedance logs |
Productos que genera este tema
- Safety case review frameworks
- Analyses of crash and intervention reports with exposure stated
- Operational design domain documentation templates
- Evidence notes for regulators
What safety engineers ask.
- Does the system perceive its environment correctly across weather, lighting and unusual objects?
- How does it behave at the edges of its operating conditions, and does it know when it is outside them?
- Can it fail safely, handing over to a person or stopping without creating new hazards?
- How is safety demonstrated when learned components cannot be fully specified?
Where physical autonomy fails.
Perception is the weak link: cameras, lidar and radar each fail differently in rain, glare, dust or darkness, and learned perception models can misclassify rare objects. Planning must handle uncertain predictions of other agents’ behaviour. Control must remain stable as conditions change.
Handover to humans is difficult because people monitoring automation lose situational awareness and may not respond in time. Classical safety engineering assumes components whose behaviour can be fully specified, which learned components are not.
Assurance for systems that move.
- 01
Dominio de diseño operativo
Defining the conditions a system is designed for and detecting when it leaves them.
- 02
Simulation and scenario testing
Testing across large numbers of simulated scenarios, using robótica y automatización and high-performance, cloud and edge computing. Simulation fidelity limits what it can reveal.
- 03
Safety cases
Structured arguments, supported by evidence, that a system is acceptably safe for a use, drawing on Ingeniería de evaluación y garantía de la IA. A safety case is only as strong as its weakest evidence.
- 04
Field monitoring
Collecting incidents and near misses after deployment. Under-reporting is common.
Cómo se estudia y lo que los métodos no pueden decir.
- 01Robótica y automatizaciónPruebas basadas en escenarios · Seguridad de los robots colaborativos · Seguimiento del dominio operativoAplicado aquí
- 02Ingeniería de evaluación y garantía de la IACasos de aseguramiento para sistemas de IA · Ejercicios de equipo rojo estructurados · Seguimiento posterior al despliegueAplicado aquí
- 03Probabilidad y estadísticaIncertidumbre de las puntuaciones de referencia · Evaluación de la calibración · Muestreo de auditoríaAplicado aquí
- 04Inteligencia artificial y aprendizaje automáticoAnálisis del cambio de distribución · Ajuste fino para un uso específico · Métodos de interpretabilidadAplicado aquí
Where this page’s references lead.
Los organismos y documentos con los que se lee esta página. El estado y las ediciones cambian; cada enlace lleva al organismo emisor.
Preguntas y respuestas.
What is an operational design domain?
Are self-driving cars safer than human drivers?
What is a safety case?
Are autonomous vehicles safer than human drivers?
Can learned components be safety-assured?
Does the platform test products?
Aporte evidencia desde esta página.
serie Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
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Aporte un método, un conjunto de datos o una pregunta.
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Documentos jurídicos.
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