GCRI · 인공지능과 자율 시스템
자율 물리 시스템
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 시스템 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
시스템 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.
팀이 이 주제를 따라가는 데 사용할 화면으로, 그 지표, 상태, 결정을 보여 줍니다. 이름은 예시이며 값은 설명용입니다.
Agent oversight console
Autonomous physical systems · Agent sandbox · Session AG-7276
Tool calls
- 10:27정보search(documents) · read-only
- 10:20정보run_model(flood_nowcast) · sandbox
- 10:39관문send_message(external) · needs approval
- 10:45Errorwrite_file(outside workspace) · denied
정책
- 범위
- 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.
Findings are designed for the Labs 증거 시리즈. 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.
모형, 그 가정, 데이터, 불확실성을 과학자나 엔지니어가 확인할 형태로 제시합니다. 구조는 발행된 방법을 따릅니다. 제시된 값은 예시이며 결과가 아닙니다.
시스템 모형
The couplings between the systems in autonomous physical systems: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
측정되는 것과 그 정확도.
각 지표에는 단위, 주기, 그 뒤의 출처 종류, 값에 붙을 근거 표시가 있습니다. 값은 표시하지 않습니다. 값은 프로그램이 자체 권한으로 제공합니다.
| 지표 | 단위 | 주기 | 출처 유형 | 라벨 |
|---|---|---|---|---|
| Crashes involving automated driving 시스템 | count by severity | 월별 | Mandatory crash reporting | 공표 통계 |
| Disengagements or interventions | count per distance or hour | 연간 | Operator reports to regulators | 보고됨 |
| Distance driven autonomously | kilometres | 연간 | Operator reports | 신고됨 |
| Industrial robot injuries | 건수 | 연간 | Occupational injury statistics | 공표 통계 |
| Operating domain exceedances | count of events outside design domain | per deployment | Operator logs | 관측 |
| Drone incidents near aircraft | 건수 | 연간 | Aviation safety reporting | 보고됨 |
근거의 출처.
이름이 명시된 공개 데이터셋과 기관 기록으로, 각각 라이선스 등급이 있습니다. 협약에 따른 출처는 그 조건에 따라서만 사용하며 결코 재게시하지 않습니다.
| 출처 | 제공하는 것 | 라이선스 등급 |
|---|---|---|
| NHTSA Standing General Order crash reports | Crashes involving automated driving and driver assistance 시스템 in the US | Public domain, US government |
| California DMV autonomous vehicle reports | Collision and disengagement reports from permit holders | 공공 기록 |
| 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 및 ISO/TS 15066 | Safety requirements for industrial and collaborative robots | Licensed standards, purchase required |
| 국가 occupational injury statistics | Injuries involving machinery and robots | Public records, licence per government |
누가 결정하며 근거는 무엇에 쓰이는지.
인공지능과 자율 시스템 prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| 결정 주체 | 질문 | 이 페이지가 제공하는 근거 |
|---|---|---|
| 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 |
이 주제가 생산하는 산출물
- 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
운행 설계 영역
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 로봇공학과 자동화 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 AI 평가 및 보증 공학. 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.
연구되는 방식과 방법이 말할 수 없는 것.
Where this page’s references lead.
이 페이지를 읽을 때 기준이 되는 기구와 문서. 상태와 판은 바뀌며, 각 링크는 발행 기구로 연결됩니다.
질문과 답변.
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?
이 페이지에서 증거를 기여하십시오.
보고서 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.
- 이메일게이트웨이 GW-GCRI이 페이지에서 보내기증거, 정정, 사례, 방법, 검토, 번역 또는 프로필 입력 자료. 이 페이지의 태그가 붙어 도착하므로, 이를 담당하는 편집자가 받게 됩니다.
- Zenodo개방형 예치기탁하고 커뮤니티에 제출하기ORCID iD로 Zenodo에 직접 기록을 게시한 뒤, 그곳의 Reports 커뮤니티에 제출하십시오. 큐레이터가 검토하며, DOI는 귀하의 것으로 남습니다.
- 보고서GCRI모든 기여 방법 보기기여 유형, 각각의 검토 방식, 받게 되는 크레디트, 라이선스, 그리고 절대 받지 않는 것.
개인정보, 민감한 인프라 세부 정보, 공유할 권리가 없는 자료는 절대 보내지 마십시오. 기여물은 사용 전에 검토됩니다. 기여물을 보낸다고 해서 저작자 지위나 편집 권한이 부여되지 않으며, 후원으로 조사 결과를 얻을 수 없습니다.
방법, 데이터세트 또는 질문을 가져오십시오.
정보와 문의는 오늘부터 열려 있습니다. 문의에는 참조 번호가 부여되고 다음 단계가 안내됩니다. 신청, 결제, 컴퓨팅 할당, 등록은 해당 팀, 약관, 심사 역량이 갖추어졌을 때만 열립니다.
법률 문서.
이 사이트의 지도, 데이터와 그림은 맥락과 일반 정보로 제시됩니다. 조언, 예보 또는 경보가 아니며, 정보의 한계는 면책 고지와 데이터 고지에 나와 있습니다.