{
  "id": "AIBD-0247",
  "title": "Seibu Railway puts AI and 3D-image crossing anomaly detection into full operation at three crossings from 14 November 2022",
  "summary": "Response.jp (11 Nov 2022): OKI and Marubeni Network Solutions' AI monitoring and a stereo-camera 3D system detect people or vehicles left in crossings, light special signal lamps for drivers and se...",
  "date_observed": "2022-11-14",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
  "source_collection": null,
  "source_entry": null,
  "account": "Response.jp reported on 11 Nov 2022 that Seibu Railway would begin full operation of a “踏切異常検知システム” on 14 Nov 2022. It combines two technologies. One is an AI monitoring system developed by OKI and Marubeni Network Solutions. It uses skeleton recognition to detect people and objects left in crossings. The other is a 3D image-analysis system from Computer Systems Laboratory that uses stereo-camera depth. When a detection occurs, the special signal lamps that warn drivers of danger operate. Live video of the site is sent to the operations control center. Operation started at three crossings on the Ikebukuro and Shinjuku lines, with two more planned within fiscal 2022. The trial that began in December 2021 was reported to have confirmed stable operation. The sources are press-release based. The OECD AI Incidents Monitor lists the deployment as an aggregate benefit. No detection event or accident outcome is documented.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "The system is built to detect people or vehicles remaining inside a crossing and warn approaching train drivers.",
  "system": "踏切異常検知システム: 踏切滞留AI監視システム (skeleton-recognition AI) and 3D画像解析踏切監視システム (stereo-camera depth analysis)",
  "model_version": null,
  "developer": "OKI and Marubeni Network Solutions (AI system); Computer Systems Laboratory (3D image analysis)",
  "deployer": "Seibu Railway",
  "location": null,
  "domain": "Railway safety",
  "segment": "Level-crossing obstacle detection",
  "benefit_type": "Increased Product Safety",
  "taxonomy_note": null,
  "registry_ids": {},
  "ratings": [
    {
      "group": "societal",
      "depth": null,
      "reason": "No outcome is documented."
    },
    {
      "group": "organizational",
      "depth": null,
      "reason": "Only stable operation in trials is reported."
    },
    {
      "group": "personal",
      "depth": null,
      "reason": "No individual outcome is documented."
    }
  ],
  "ai_role": {
    "status": "unassessed",
    "statement": "The AI detects and the signal lamps alert human drivers, who brake. No detection statistics are given."
  },
  "evidence": [
    {
      "kind": "news report",
      "citation": "Response.jp (11 Nov 2022). 西武が踏切異常検知システムを本運用.",
      "title": "西武が踏切異常検知システムを本運用",
      "url": "https://response.jp/article/2022/11/11/363968.html",
      "date": "2022-11-11",
      "written_by": "independent",
      "quote": "検知すると運転士へ危険を知らせる特殊信号発光機が作動"
    },
    {
      "kind": "registry label",
      "citation": "OECD AI Incidents and Hazards Monitor, incident 2022-11-09-4a03: Seibu Railway Deploys AI-Based Crossing Detection to Prevent Accidents.",
      "title": "Seibu Railway Deploys AI-Based Crossing Detection to Prevent Accidents",
      "url": "https://oecd.ai/en/incidents/2022-11-09-4a03",
      "date": "2022-11-09",
      "written_by": "independent",
      "quote": "These real-time AI systems automatically alert train drivers to potential dangers, aiming to prevent accidents and enhance safety, especially at smaller pedestrian crossings."
    }
  ],
  "replication": {
    "produced_by": [
      "Camera coverage of smaller pedestrian crossings",
      "Special signal lamps already used to warn drivers",
      "A year-long trial before full operation"
    ],
    "must_hold": [
      "Detection remains reliable across weather and lighting",
      "Drivers can stop in time once the lamp operates"
    ],
    "echoes": [
      {
        "date": "2026-06-24",
        "setting": "Odakyu's AI crossing image analysis at four crossings, linked directly to train braking (ITmedia)",
        "model_version": null,
        "outcome": "recurred",
        "source": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html"
      }
    ]
  },
  "paired_incident": null,
  "headline": "Seibu puts OKI AI crossing anomaly detection into full operation",
  "metric": null,
  "comparator": null,
  "later": {
    "status": "unknown",
    "date": null,
    "note": "No source after 14 November 2022 was available; the pre-launch report only states plans for two more crossings within fiscal 2022.",
    "quote": null,
    "url": null
  },
  "value": null,
  "developer_country": "Japan",
  "deployer_country": "Japan",
  "unrated_because": null,
  "brief_notes": null,
  "brief": null,
  "revisions": [
    {
      "date": "2026-10-20",
      "change": "Record drafted from the cited sources.",
      "source": null,
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.",
      "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
      "source": "https://en.wikipedia.org/wiki/Oki_Electric_Industry; https://en.wikipedia.org/wiki/Seibu_Railway",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
      "source": null,
      "editor": "AIBD"
    }
  ],
  "confidence": "reported",
  "shade": "medium",
  "claim_status": "Independently reported",
  "claim_basis": "Sources: one independent news report and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.",
  "profile": {
    "case_id": "AIBD-0247",
    "catalog_version": "1.0.0",
    "source_fingerprint": "56ef2cfbd48eecd3f321afef991947a6df736b6cb1f825be9f355f8bbd351e87",
    "collection_refs": [
      {
        "collection_id": "aibd",
        "version": "snapshot-8bf7cfec9cb185aa"
      },
      {
        "collection_id": "aibd",
        "version": "2026-10-25"
      },
      {
        "collection_id": "chanticleer",
        "version": "2026-10-25"
      }
    ],
    "source_ids": [
      "AIBD-0247-source-0001",
      "AIBD-0247-source-0002"
    ],
    "families": {
      "strategic assessments": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "record_ids": []
      },
      "evaluations": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "record_ids": []
      },
      "monetary observations": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "record_ids": []
      },
      "institutional decisions": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "record_ids": []
      }
    },
    "fields": {
      "actors[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "developer": "OKI and Marubeni Network Solutions (AI system); Computer Systems Laboratory (3D image analysis)",
            "deployer": "Seibu Railway"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "developer",
            "deployer"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "actors[].ownership_control": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "ai_contribution": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "ai_relationship": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "category": "general benefit",
            "ai_role": {
              "status": "unassessed",
              "statement": "The AI detects and the signal lamps alert human drivers, who brake. No detection statistics are given."
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ai_role",
            "category"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].alternatives": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].assessor_date": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].depth": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].display_confidence": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "confidence": "medium",
            "evidence_word": "reported",
            "claim_status": "Independently reported",
            "basis": "Sources: one independent news report and a registry entry. The cited evidence includes no independent study or report and no finding by a regulator or court.",
            "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review."
          }
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Derived from the existing case display properties; not a new evidentiary judgment.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].rationale": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "societal",
                "reason": "No outcome is documented."
              },
              {
                "group": "organizational",
                "reason": "Only stable operation in trials is reported."
              },
              {
                "group": "personal",
                "reason": "No individual outcome is documented."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ratings"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].rubric_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].status_basis": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].target": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "societal",
                "reason": "No outcome is documented."
              },
              {
                "group": "organizational",
                "reason": "Only stable operation in trials is reported."
              },
              {
                "group": "personal",
                "reason": "No individual outcome is documented."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ratings"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "availability": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "brief": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "case_kind": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "case_links[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].evidence_links": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].evidence_strength": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].missingness": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].proposition": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "cross_border_flows[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "decisions[].action_outcome": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "decisions[].grounds": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "decisions[].institution_task": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "decisions[].success_criterion": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "deployment_stage": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "economics[].amount": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].costs[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].counterfactual": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].currency_price_basis": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].gross_net": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].measure_type": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].period_unit": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].realization": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].recipient_payer": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].return_payback": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].valuation_method": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "evaluations[].ai_uplift": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].comparator": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].design": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].effect": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].exposure": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].id": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].measurement_method": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].metric": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].reproduction_materials": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].setting_sample": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].uncertainty_limitations": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "external_references[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "follow_up[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "later": {
              "status": "unknown",
              "note": "No source after 14 November 2022 was available; the pre-launch report only states plans for two more crossings within fiscal 2022."
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "later"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "human_oversight": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "id": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "id": "AIBD-0247"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "id"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "infrastructure_dependencies[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].beneficiaries": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].beneficiary_count": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].benefit_type": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "benefit_type": "Increased Product Safety"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "benefit_type"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].claim": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "account": "Response.jp reported on 11 Nov 2022 that Seibu Railway would begin full operation of a “踏切異常検知システム” on 14 Nov 2022. It combines two technologies. One is an AI monitoring system developed by OKI and Marubeni Network Solutions. It uses skeleton recognition to detect people and objects left in crossings. The other is a 3D image-analysis system from Computer Systems Laboratory that uses stereo-camera depth. When a detection occurs, the special signal lamps that warn drivers of danger operate. Live video of the site is sent to the operations control center. Operation started at three crossings on the Ikebukuro and Shinjuku lines, with two more planned within fiscal 2022. The trial that began in December 2021 was reported to have confirmed stable operation. The sources are press-release based. The OECD AI Incidents Monitor lists the deployment as an aggregate benefit. No detection event or accident outcome is documented.",
            "benefit_type": "Increased Product Safety"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "account",
            "benefit_type"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].distribution": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].duration": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].id": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].stakeholder_group": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "societal",
                "reason": "No outcome is documented."
              },
              {
                "group": "organizational",
                "reason": "Only stable operation in trials is reported."
              },
              {
                "group": "personal",
                "reason": "No individual outcome is documented."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ratings"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].status": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].tradeoffs": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].unexpected": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "unexpected": false,
            "purpose": "The system is built to detect people or vehicles remaining inside a crossing and warn approaching train drivers."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "unexpected",
            "purpose"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "places[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "developer_country": "Japan",
            "deployer_country": "Japan"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "developer_country",
            "deployer_country"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "purpose": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "purpose": "The system is built to detect people or vehicles remaining inside a crossing and warn approaching train drivers."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "purpose"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "record_dates": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "date_observed": "2022-11-14",
            "revisions": [
              {
                "date": "2026-10-20",
                "change": "Record drafted from the cited sources.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.",
                "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
                "source": "https://en.wikipedia.org/wiki/Oki_Electric_Industry; https://en.wikipedia.org/wiki/Seibu_Railway",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
                "editor": "AIBD"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "date_observed",
            "revisions"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "replication.attempts[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "replication": {
              "produced_by": [
                "Camera coverage of smaller pedestrian crossings",
                "Special signal lamps already used to warn drivers",
                "A year-long trial before full operation"
              ],
              "must_hold": [
                "Detection remains reliable across weather and lighting",
                "Drivers can stop in time once the lamp operates"
              ],
              "echoes": [
                {
                  "date": "2026-06-24",
                  "setting": "Odakyu's AI crossing image analysis at four crossings, linked directly to train braking (ITmedia)",
                  "outcome": "recurred",
                  "source": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html"
                }
              ]
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "replication"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "replication.conditions": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "replication": {
              "produced_by": [
                "Camera coverage of smaller pedestrian crossings",
                "Special signal lamps already used to warn drivers",
                "A year-long trial before full operation"
              ],
              "must_hold": [
                "Detection remains reliable across weather and lighting",
                "Drivers can stop in time once the lamp operates"
              ],
              "echoes": [
                {
                  "date": "2026-06-24",
                  "setting": "Odakyu's AI crossing image analysis at four crossings, linked directly to train braking (ITmedia)",
                  "outcome": "recurred",
                  "source": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html"
                }
              ]
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "replication"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "revision_history[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "revisions": [
              {
                "date": "2026-10-20",
                "change": "Record drafted from the cited sources.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.",
                "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
                "source": "https://en.wikipedia.org/wiki/Oki_Electric_Industry; https://en.wikipedia.org/wiki/Seibu_Railway",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
                "editor": "AIBD"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "revisions"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sector": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "domain": "Railway safety",
            "segment": "Level-crossing obstacle detection"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "domain",
            "segment"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].access_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "sources[].citation_locator": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "evidence": [
              {
                "kind": "news report",
                "citation": "Response.jp (11 Nov 2022). 西武が踏切異常検知システムを本運用.",
                "title": "西武が踏切異常検知システムを本運用",
                "url": "https://response.jp/article/2022/11/11/363968.html",
                "date": "2022-11-11",
                "written_by": "independent",
                "quote": "検知すると運転士へ危険を知らせる特殊信号発光機が作動"
              },
              {
                "kind": "registry label",
                "citation": "OECD AI Incidents and Hazards Monitor, incident 2022-11-09-4a03: Seibu Railway Deploys AI-Based Crossing Detection to Prevent Accidents.",
                "title": "Seibu Railway Deploys AI-Based Crossing Detection to Prevent Accidents",
                "url": "https://oecd.ai/en/incidents/2022-11-09-4a03",
                "date": "2022-11-09",
                "written_by": "independent",
                "quote": "These real-time AI systems automatically alert train drivers to potential dangers, aiming to prevent accidents and enhance safety, especially at smaller pedestrian crossings."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "evidence"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].id": {
        "state": "value_present",
        "value": {
          "kind": "identifiers",
          "identifiers": [
            "AIBD-0247-source-0001",
            "AIBD-0247-source-0002"
          ],
          "role": "Existing evidence items, in their original order"
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Stable within the fingerprinted record version; no source content or source review has been added.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "complete"
      },
      "sources[].language": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "sources[].relationship": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "evidence": [
              {
                "kind": "news report",
                "citation": "Response.jp (11 Nov 2022). 西武が踏切異常検知システムを本運用.",
                "title": "西武が踏切異常検知システムを本運用",
                "url": "https://response.jp/article/2022/11/11/363968.html",
                "date": "2022-11-11",
                "written_by": "independent",
                "quote": "検知すると運転士へ危険を知らせる特殊信号発光機が作動"
              },
              {
                "kind": "registry label",
                "citation": "OECD AI Incidents and Hazards Monitor, incident 2022-11-09-4a03: Seibu Railway Deploys AI-Based Crossing Detection to Prevent Accidents.",
                "title": "Seibu Railway Deploys AI-Based Crossing Detection to Prevent Accidents",
                "url": "https://oecd.ai/en/incidents/2022-11-09-4a03",
                "date": "2022-11-09",
                "written_by": "independent",
                "quote": "These real-time AI systems automatically alert train drivers to potential dangers, aiming to prevent accidents and enhance safety, especially at smaller pedestrian crossings."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "evidence"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].upstream_sources": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "strategic_assessments[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "strategic assessments"
      },
      "summary": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "summary": "Response.jp (11 Nov 2022): OKI and Marubeni Network Solutions' AI monitoring and a stereo-camera 3D system detect people or vehicles left in crossings, light special signal lamps for drivers and se...",
            "account": "Response.jp reported on 11 Nov 2022 that Seibu Railway would begin full operation of a “踏切異常検知システム” on 14 Nov 2022. It combines two technologies. One is an AI monitoring system developed by OKI and Marubeni Network Solutions. It uses skeleton recognition to detect people and objects left in crossings. The other is a 3D image-analysis system from Computer Systems Laboratory that uses stereo-camera depth. When a detection occurs, the special signal lamps that warn drivers of danger operate. Live video of the site is sent to the operations control center. Operation started at three crossings on the Ikebukuro and Shinjuku lines, with two more planned within fiscal 2022. The trial that began in December 2021 was reported to have confirmed stable operation. The sources are press-release based. The OECD AI Incidents Monitor lists the deployment as an aggregate benefit. No detection event or accident outcome is documented."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "summary",
            "account"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].autonomy_permissions": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].component_role": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].deployment_configuration": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].inputs_outputs": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].model_name": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].model_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].product_name": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "system": "踏切異常検知システム: 踏切滞留AI監視システム (skeleton-recognition AI) and 3D画像解析踏切監視システム (stereo-camera depth analysis)"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "system"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].provider": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "application_developer": "OKI and Marubeni Network Solutions (AI system); Computer Systems Laboratory (3D image analysis)"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Mapped from the existing case description without a new external source review.",
          "record_fields": [
            "developer"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "taxonomy_crosswalks[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "title": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "title": "Seibu Railway puts AI and 3D-image crossing anomaly detection into full operation at three crossings from 14 November 2022",
            "headline": "Seibu puts OKI AI crossing anomaly detection into full operation"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "title",
            "headline"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "version": {
        "state": "value_present",
        "value": {
          "kind": "identifiers",
          "identifiers": [
            "56ef2cfbd48eecd3f321afef991947a6df736b6cb1f825be9f355f8bbd351e87"
          ],
          "role": "SHA-256 of normalized original case data"
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Deterministic content version assigned by the local migration.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "complete"
      },
      "workflow": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "account": "Response.jp reported on 11 Nov 2022 that Seibu Railway would begin full operation of a “踏切異常検知システム” on 14 Nov 2022. It combines two technologies. One is an AI monitoring system developed by OKI and Marubeni Network Solutions. It uses skeleton recognition to detect people and objects left in crossings. The other is a 3D image-analysis system from Computer Systems Laboratory that uses stereo-camera depth. When a detection occurs, the special signal lamps that warn drivers of danger operate. Live video of the site is sent to the operations control center. Operation started at three crossings on the Ikebukuro and Shinjuku lines, with two more planned within fiscal 2022. The trial that began in December 2021 was reported to have confirmed stable operation. The sources are press-release based. The OECD AI Incidents Monitor lists the deployment as an aggregate benefit. No detection event or accident outcome is documented.",
            "replication": {
              "produced_by": [
                "Camera coverage of smaller pedestrian crossings",
                "Special signal lamps already used to warn drivers",
                "A year-long trial before full operation"
              ],
              "must_hold": [
                "Detection remains reliable across weather and lighting",
                "Drivers can stop in time once the lamp operates"
              ],
              "echoes": [
                {
                  "date": "2026-06-24",
                  "setting": "Odakyu's AI crossing image analysis at four crossings, linked directly to train braking (ITmedia)",
                  "outcome": "recurred",
                  "source": "https://www.itmedia.co.jp/news/articles/2606/22/news125.html"
                }
              ]
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "account",
            "replication"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      }
    },
    "summary": {
      "total": 91,
      "addressed": 91,
      "known": 27,
      "reviewed": 0,
      "record_mapped": 27,
      "not_assessed": 64,
      "complete": false,
      "by_state": {
        "value_present": 27,
        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 64,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}