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Documentation Index

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Autonomous logistics platforms — delivery robots, autonomous vehicles, drone fleets — share a common dependency: they need accurate, continuously updated knowledge of the physical environment around them. Vangrid gives your systems that foundation by streaming verified spatial ground truth from a dense network of edge nodes already operating in urban environments, so your vehicles can route safely, detect obstacles in real time, and coordinate across a fleet without relying on stale map data.

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Key challenges Vangrid solves

Real-time obstacle awareness

Detect transient obstacles — parked vehicles, construction zones, pedestrian congestion — the moment they appear, not on the next map update cycle.

Route ground truth

Validate planned routes against live spatial conditions before and during traversal. Know whether a corridor is passable before your vehicle commits to it.

Multi-vehicle coordination

Give every vehicle in your fleet access to the same real-time spatial picture so they can negotiate intersections, share lanes, and avoid redundant paths.

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How Vangrid fits into a logistics stack

Vangrid sits between your physical fleet and your routing or decision layer. Your vehicles issue spatial queries to define their area of interest, receive ground truth responses with confidence scores, and update their onboard world models continuously. Because Vangrid edge nodes operate at tactical urban density, even narrow corridors and interior loading docks are covered.
Urban-density edge node coverage is available in supported metro areas. Contact hello@vangrid.io to confirm coverage for your deployment zone.

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Use case walkthrough: last-mile delivery corridor

The following walkthrough shows how to integrate Vangrid into a last-mile delivery system operating in a dense urban block.
1

Define your delivery corridor as an AOI

Model your delivery route as a GeoJSON polygon that encloses the street corridor your vehicle will traverse. Use a polygon tight enough to avoid pulling data from unrelated blocks, but wide enough to cover the full width of the path plus a safety margin.
delivery-corridor.json
{
  "type": "Polygon",
  "coordinates": [[
    [-73.9865, 40.7480],
    [-73.9845, 40.7480],
    [-73.9845, 40.7510],
    [-73.9865, 40.7510],
    [-73.9865, 40.7480]
  ]]
}
2

Query spatial ground truth for the corridor

Submit a spatial query against /v1/spatial/query with your corridor polygon and a ground_truth_score threshold to filter out low-confidence observations. Setting max_age_seconds keeps the response current — for active navigation, values between 10 and 30 seconds are typical.
curl -X POST https://api.vangrid.io/v1/spatial/query \
  -H "Authorization: Bearer $VANGRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "aoi": {
      "type": "Polygon",
      "coordinates": [[
        [-73.9865, 40.7480],
        [-73.9845, 40.7480],
        [-73.9845, 40.7510],
        [-73.9865, 40.7510],
        [-73.9865, 40.7480]
      ]]
    },
    "max_age_seconds": 15,
    "min_ground_truth_score": 0.90
  }'
3

Interpret the response

The response includes a top-level ground_truth_score for the entire AOI and individual observations from each contributing edge node. Your routing layer should inspect both the aggregate score and the spatial distribution of data_points to identify specific segments with blocked or degraded conditions.
{
  "query_id": "q_2d7b9f4e1a3c8605",
  "timestamp": "2026-05-22T09:14:33.201Z",
  "node_count": 312,
  "ground_truth_score": 0.96,
  "geometry": {
    "type": "Polygon",
    "coordinates": [[
      [-73.9865, 40.7480],
      [-73.9845, 40.7480],
      [-73.9845, 40.7510],
      [-73.9865, 40.7510],
      [-73.9865, 40.7480]
    ]]
  },
  "data_points": [
    {
      "node_id": "node_9c3f1a",
      "lat": 40.7492,
      "lon": -73.9857,
      "observation": "clear",
      "confidence": 0.98,
      "captured_at": "2026-05-22T09:14:32.774Z",
      "provenance_hash": "sha256:b7e2d4f1a9c3605ef1d8b4a2c7f9e3b1d4a7f2c9"
    },
    {
      "node_id": "node_2a8e5c",
      "lat": 40.7501,
      "lon": -73.9851,
      "observation": "obstruction_detected",
      "confidence": 0.94,
      "captured_at": "2026-05-22T09:14:33.012Z",
      "provenance_hash": "sha256:c4f1a9e2d7b3605fa1d9c4b2f7e4a1c9d3b7f1a2"
    }
  ],
  "provenance_hash": "sha256:a1c9d3b7f4e2605fb9d1c4a2e7f3b1a9c4d7e2f1"
}
Parse data_points for observations with observation: "obstruction_detected" and route around their coordinates before dispatching your vehicle. A ground_truth_score below 0.85 across the full AOI suggests the corridor has high environmental uncertainty — consider holding the vehicle until scores recover.
4

Switch to streaming for active traversal

Once a vehicle is in motion, replace periodic queries with a streaming subscription so your onboard system receives updates as conditions change rather than on a fixed polling interval.
python
import os
import json
import requests

api_key = os.environ["VANGRID_API_KEY"]

params = {
    "geometry": "POLYGON((-73.9865 40.7480,-73.9845 40.7480,-73.9845 40.7510,-73.9865 40.7510,-73.9865 40.7480))",
    "ground_truth_score_min": "0.90",
}

with requests.get(
    "https://api.vangrid.io/v1/spatial/stream",
    headers={
        "Authorization": f"Bearer {api_key}",
        "Accept": "text/event-stream",
    },
    params=params,
    stream=True,
) as response:
    for line in response.iter_lines():
        if line:
            event = json.loads(line.decode("utf-8").lstrip("data: "))
            # Feed each update into your onboard world model
            process_spatial_update(event)

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Relevant concepts

Edge nodes

How urban-density edge node coverage is structured and how nodes are selected for your queries.

Continuous ground truth

What ground_truth_score means, how it is calculated, and when to trust or reject a response.

Data pipeline

How data moves from edge capture through multi-view ingestion to your API response.