{
  "id": "AIBD-0244",
  "title": "Lime rolls out its in-house “Lime Vision” camera AI that chirps at sidewalk riders to half of its 7,000-scooter Seattle fleet by 1 June 2026",
  "summary": "Seattle Times (20 March 2026): Lime Vision uses cameras and AI to detect sidewalk riding and emit repeating audible alerts; 3,500 scooters by 1 June 2026, whole fleet by year-end; no outcome data yet.",
  "date_observed": "2026-03-20",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
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  "source_entry": null,
  "account": "Nicholas Deshais of The Seattle Times reported on 20 March 2026 that Lime would fit “Lime Vision” to its Seattle scooters. The Spokesman-Review syndicated the report. Lime Vision is a system of cameras and AI software developed in-house to detect sidewalk riding. It emits audible alerts that repeat until the scooter leaves the sidewalk or the trip ends. Lime would fit it to about 3,500 vehicles, half its Seattle fleet, by 1 June 2026, and to the whole fleet by year's end. New and retrofitted scooters were to arrive from mid-April. Parker Dawson, Lime's regional government relations lead, said the technology had been tested in Seattle in recent months. He said Seattle would get it before any other city, and that it coaches riders rather than slowing the scooter. He would not say why the scooters do not slow down on sidewalks as they do under geofencing at Seattle Center. Jon Scholes of the Downtown Seattle Association called sidewalk riding a safety issue. Lime shares data with a Seattle Department of Transportation (SDOT) dashboard. The article gives no sidewalk-riding or complaint figures before or after deployment.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "Lime Vision was built to detect sidewalk riding and deter it through audible alerts.",
  "system": "Lime Vision (on-scooter cameras with AI sidewalk-riding detection and audible alerts)",
  "model_version": null,
  "developer": "Lime",
  "deployer": "Lime (Seattle shared-scooter fleet)",
  "location": "Seattle, United States",
  "domain": "Micromobility safety",
  "segment": "Shared e-scooters / pedestrian conflict",
  "benefit_type": "Increased Product Safety",
  "taxonomy_note": "Launch only; sidewalk-riding alerts are a safety feature with no outcome yet.",
  "registry_ids": {},
  "ratings": [
    {
      "group": "societal",
      "depth": null,
      "reason": "No outcome is documented; the article describes a rollout."
    },
    {
      "group": "organizational",
      "depth": null,
      "reason": "No outcome for Lime or SDOT is documented."
    },
    {
      "group": "personal",
      "depth": null,
      "reason": "No individual outcome is documented."
    }
  ],
  "ai_role": {
    "status": "unassessed",
    "statement": "The article describes the intended function. No detection counts, accuracy or behavioral results are reported, and Lime declined to share many details."
  },
  "evidence": [
    {
      "kind": "news report",
      "citation": "Deshais N (20 Mar 2026). AI to warn Seattle Lime scooters to stay off sidewalks. The Seattle Times, syndicated by The Spokesman-Review.",
      "title": "AI to warn Seattle Lime scooters to stay off sidewalks",
      "url": "https://www.spokesman.com/stories/2026/mar/20/ai-to-warn-seattle-lime-scooters-to-stay-off-sidew/",
      "date": "2026-03-20",
      "written_by": "independent",
      "quote": "Lime Vision will be installed on half the scooter fleet, about 3,500 vehicles, by June 1 and all of the fleet by year’s end."
    }
  ],
  "replication": {
    "produced_by": [
      "Camera-equipped scooters with on-device sidewalk classification",
      "Audible alerts that persist until the rider leaves the sidewalk",
      "A city where sidewalk riding is barred and where the operator shares data with the transport department"
    ],
    "must_hold": [
      "Classification works on Seattle's mixed sidewalk and parking-corral layout",
      "Riders respond to alerts rather than ignoring them"
    ],
    "echoes": [
      {
        "date": "2024-11-20",
        "setting": "Randomized test of a camera-based feedback system on 100 Spin scooters in Santa Monica: 22% less sidewalk time with feedback on",
        "model_version": null,
        "outcome": "recurred",
        "source": "https://findingspress.org/article/125892-ai-driven-feedback-systems-reduce-sidewalk-riding-on-shared-e-scooters"
      }
    ]
  },
  "paired_incident": null,
  "headline": "Lime fits Lime Vision sidewalk alerts to 3,500 Seattle scooters",
  "metric": null,
  "comparator": null,
  "later": {
    "status": "unknown",
    "date": null,
    "note": "No source after the 20 March 2026 report was available to confirm whether the 1 June 2026 rollout target was met.",
    "quote": null,
    "url": null
  },
  "value": null,
  "developer_country": "United States",
  "deployer_country": "United States",
  "unrated_because": null,
  "brief_notes": null,
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  "revisions": [
    {
      "date": "2026-10-20",
      "change": "Record drafted from the cited sources.",
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      "editor": "AIBD"
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    {
      "date": "2026-10-21",
      "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility 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/Lime_(transportation_company); https://en.wikipedia.org/wiki/Lime_(transportation_company)",
      "editor": "AIBD"
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    {
      "date": "2026-10-21",
      "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
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      "source": null,
      "editor": "AIBD"
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  "confidence": "reported",
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  "claim_status": "Independently reported",
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            "account": "Nicholas Deshais of The Seattle Times reported on 20 March 2026 that Lime would fit “Lime Vision” to its Seattle scooters. The Spokesman-Review syndicated the report. Lime Vision is a system of cameras and AI software developed in-house to detect sidewalk riding. It emits audible alerts that repeat until the scooter leaves the sidewalk or the trip ends. Lime would fit it to about 3,500 vehicles, half its Seattle fleet, by 1 June 2026, and to the whole fleet by year's end. New and retrofitted scooters were to arrive from mid-April. Parker Dawson, Lime's regional government relations lead, said the technology had been tested in Seattle in recent months. He said Seattle would get it before any other city, and that it coaches riders rather than slowing the scooter. He would not say why the scooters do not slow down on sidewalks as they do under geofencing at Seattle Center. Jon Scholes of the Downtown Seattle Association called sidewalk riding a safety issue. Lime shares data with a Seattle Department of Transportation (SDOT) dashboard. The article gives no sidewalk-riding or complaint figures before or after deployment.",
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              },
              {
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              {
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                "A city where sidewalk riding is barred and where the operator shares data with the transport department"
              ],
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                "Classification works on Seattle's mixed sidewalk and parking-corral layout",
                "Riders respond to alerts rather than ignoring them"
              ],
              "echoes": [
                {
                  "date": "2024-11-20",
                  "setting": "Randomized test of a camera-based feedback system on 100 Spin scooters in Santa Monica: 22% less sidewalk time with feedback on",
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              ]
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              {
                "date": "2026-10-21",
                "change": "Benefit type set to Increased Product Safety under Trustible's full taxonomy of 23 types; it was Improved Accessibility 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.",
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              {
                "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"
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              {
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        "family": "strategic assessments"
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            "account": "Nicholas Deshais of The Seattle Times reported on 20 March 2026 that Lime would fit “Lime Vision” to its Seattle scooters. The Spokesman-Review syndicated the report. Lime Vision is a system of cameras and AI software developed in-house to detect sidewalk riding. It emits audible alerts that repeat until the scooter leaves the sidewalk or the trip ends. Lime would fit it to about 3,500 vehicles, half its Seattle fleet, by 1 June 2026, and to the whole fleet by year's end. New and retrofitted scooters were to arrive from mid-April. Parker Dawson, Lime's regional government relations lead, said the technology had been tested in Seattle in recent months. He said Seattle would get it before any other city, and that it coaches riders rather than slowing the scooter. He would not say why the scooters do not slow down on sidewalks as they do under geofencing at Seattle Center. Jon Scholes of the Downtown Seattle Association called sidewalk riding a safety issue. Lime shares data with a Seattle Department of Transportation (SDOT) dashboard. The article gives no sidewalk-riding or complaint figures before or after deployment."
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        "coverage": "partial"
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        "value": {
          "kind": "record_fields",
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          "record_fields": [
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          "source_refs": []
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          "fields": {
            "account": "Nicholas Deshais of The Seattle Times reported on 20 March 2026 that Lime would fit “Lime Vision” to its Seattle scooters. The Spokesman-Review syndicated the report. Lime Vision is a system of cameras and AI software developed in-house to detect sidewalk riding. It emits audible alerts that repeat until the scooter leaves the sidewalk or the trip ends. Lime would fit it to about 3,500 vehicles, half its Seattle fleet, by 1 June 2026, and to the whole fleet by year's end. New and retrofitted scooters were to arrive from mid-April. Parker Dawson, Lime's regional government relations lead, said the technology had been tested in Seattle in recent months. He said Seattle would get it before any other city, and that it coaches riders rather than slowing the scooter. He would not say why the scooters do not slow down on sidewalks as they do under geofencing at Seattle Center. Jon Scholes of the Downtown Seattle Association called sidewalk riding a safety issue. Lime shares data with a Seattle Department of Transportation (SDOT) dashboard. The article gives no sidewalk-riding or complaint figures before or after deployment.",
            "replication": {
              "produced_by": [
                "Camera-equipped scooters with on-device sidewalk classification",
                "Audible alerts that persist until the rider leaves the sidewalk",
                "A city where sidewalk riding is barred and where the operator shares data with the transport department"
              ],
              "must_hold": [
                "Classification works on Seattle's mixed sidewalk and parking-corral layout",
                "Riders respond to alerts rather than ignoring them"
              ],
              "echoes": [
                {
                  "date": "2024-11-20",
                  "setting": "Randomized test of a camera-based feedback system on 100 Spin scooters in Santa Monica: 22% less sidewalk time with feedback on",
                  "outcome": "recurred",
                  "source": "https://findingspress.org/article/125892-ai-driven-feedback-systems-reduce-sidewalk-riding-on-shared-e-scooters"
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        },
        "review_state": "record_mapped",
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      }
    },
    "summary": {
      "total": 91,
      "addressed": 91,
      "known": 27,
      "reviewed": 0,
      "record_mapped": 27,
      "not_assessed": 64,
      "complete": false,
      "by_state": {
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        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 64,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}