GCRI · 人工知能と自律システム
自律型の物理システム
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 システム 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
システム 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.
このテーマを追跡するためにチームが使う画面：その指標、状態、決定。名前は例であり、値はイメージです。
Agent oversight console
Autonomous physical systems · Agent sandbox · Session AG-7276
Tool calls
- 10:27情報search(documents) · read-only
- 10:20情報run_model(flood_nowcast) · sandbox
- 10:39ゲートsend_message(external) · needs approval
- 10:45Errorwrite_file(outside workspace) · denied
政策
- 対象範囲
- 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 Evidence series. 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.
モデル、その仮定、データ、不確実性を、科学者やエンジニアが確認する形で示します。構造は公表された手法に従っています。描かれた値は例示であり、知見ではありません。
システム・モデル
The couplings between the systems in autonomous physical systems: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
何を、どの程度うまく測定しているか。
各指標には、その単位、更新頻度、背後にある出典の種類、値が持つことになるエビデンスのラベルが付いています。値は表示しません：プログラムが自らの権限のもとで値を提供します。
| 指標 | 単位 | 周期 | 出典の種類 | ラベル |
|---|---|---|---|---|
| Crashes involving automated driving システム | count by severity | 月次 | Mandatory crash reporting | 公表統計 |
| Disengagements or interventions | count per distance or hour | 年次 | Operator reports to regulators | 報告値 |
| Distance driven autonomously | kilometres | 年次 | Operator reports | 申告済み |
| Industrial robot injuries | 件数 | 年次 | Occupational injury statistics | 公表統計 |
| Operating domain exceedances | count of events outside design domain | per deployment | Operator logs | 観測 |
| Drone incidents near aircraft | 件数 | 年次 | Aviation safety reporting | 報告値 |
エビデンスの出所。
名前の明示された公開データセットと機関の記録。それぞれにライセンス区分があります。合意に基づく出典は、その条件のもとでのみ使用し、再公開することはありません。
| 出典 | 提供するもの | ライセンス区分 |
|---|---|---|
| NHTSA Standing General Order crash reports | Crashes involving automated driving and driver assistance システム in the US | Public domain, US government |
| California DMV autonomous vehicle reports | Collision and disengagement reports from permit holders | 公的記録 |
| 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 と ISO/TS 15066 | Safety requirements for industrial and collaborative robots | Licensed standards, purchase required |
| 国別 occupational injury statistics | Injuries involving machinery and robots | Public records, licence per government |
誰が決め、エビデンスが何に役立つか。
人工知能と自律 システム prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| 誰が決定するか | 質問 | このページが提供するエビデンス |
|---|---|---|
| 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 |
このトピックが生み出す成果物
- 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
運行設計領域
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 ロボティクスと自動化 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 評価・保証工学. 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.
どのように研究され、手法が言えないことは何か。
Where this page’s references lead.
このページの読解の基準となる機関と文書。状態と版は変わります。各リンクは発行機関へ移動します。
質問と回答。
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?
このページからエビデンスを提供する。
報告書 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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