Artificial intelligence

Modern connected vehicle platforms generate billions of telemetry events daily: GPS coordinates, engine diagnostics, fuel metrics, driver behavior, CAN bus signals, camera metadata, and predictive maintenance alerts. Traditional relational systems struggle with ingestion throughput, schema evolution, and multi-modal analytics.

This blog explains how to design a production-grade telematics architecture using MongoDB Atlas for real-time ingestion, analytics, AI search, and fleet intelligence.

Why MongoDB for Telematics?

Telematics workloads require:

• Massive write throughput
• Flexible schemas
• Time-series optimization
• Geo-spatial indexing
• Streaming analytics
• AI/ML integration
• Low-latency operational queries

MongoDB provides:

Reference Architecture

Designing Time Series Collections

MongoDB Time Series collections internally optimize storage using bucket compression.

Create Time Series Collection

db.createCollection("vehicleTelemetry", {
timeseries: {
timeField: "eventTime",
metaField: "vehicleMeta",
granularity: "seconds"
}
})

Sample Telemetry Document

{
  "vehicleMeta": {
    "vehicleId": "MH12AB1234",
    "fleetId": "fleet-west-01",
    "vehicleType": "truck"
  },
  "eventTime": ISODate("2026-05-18T12:00:00Z"),
  "speed": 82,
  "engineTemp": 91,
  "fuelLevel": 42,
  "gps": {
    "type": "Point",
    "coordinates": [77.5946, 12.9716]
  },
  "tirePressure": {
    "frontLeft": 33,
    "frontRight": 34
  }
}

GeoSpatial Indexing

db.vehicleTelemetry.createIndex({
gps: "2dsphere"
})

Find Vehicles Near a Region

db.vehicleTelemetry.find({
gps: {
$near: {
$geometry: {
type: "Point",
coordinates: [77.5946, 12.9716]
},
$maxDistance: 5000
}
}
})

Real-Time Fleet Analytics

Average Speed Per Fleet

db.vehicleTelemetry.aggregate([
{
$group: {
_id: "$vehicleMeta.fleetId",
avgSpeed: { $avg: "$speed" },
maxTemp: { $max: "$engineTemp" }
}
}
])

Window Functions for Driving Behavior

db.vehicleTelemetry.aggregate([
{
$setWindowFields: {
partitionBy: "$vehicleMeta.vehicleId",
sortBy: { eventTime: 1 },
output: {
avgSpeedRolling: {
$avg: "$speed",
window: {
range: [-5, 0],
unit: "minute"
}
}
}
}
}
])

This enables:

• Rash driving detection
• Fuel optimization
• Driver scoring
• Anomaly detection

Predictive Maintenance Using Vector Search

Store maintenance logs as embeddings.

Maintenance Record

{
"vehicleId": "MH12AB1234",
"issue": "Engine vibration during uphill acceleration",
"embedding": [0.123, -0.882, …]
}

Create Vector Search Index

{
"fields": [
{
"type": "vector",
"path": "embedding",
"numDimensions": 1024,
"similarity": "cosine"
}
]
}

Similar Issue Search

db.maintenanceLogs.aggregate([
{
$vectorSearch: {
index: "maintenanceVectorIndex",
path: "embedding",
queryVector: queryEmbedding,
numCandidates: 200,
limit: 5
}
}
])

This powers:

• Root cause analysis
• AI-assisted diagnostics
• Failure prediction
• Service recommendations

Kafka Integration

Kafka Sink Connector

{
"name": "mongodb-telematics-sink",
"config": {
"connector.class":
"com.mongodb.kafka.connect.MongoSinkConnector",
"topics": "vehicle-events",
"connection.uri":
"mongodb+srv://cluster.mongodb.net",
"database": "fleet",
"collection": "vehicleTelemetry"
}
}

Sharding Strategy

For billion-event workloads:

sh.shardCollection(
"fleet.vehicleTelemetry",
{
"vehicleMeta.fleetId": 1,
"eventTime": 1
}
)

Atlas Stream Processing

MongoDB Atlas Stream Processing enables near real-time transformations.

Example:

• Detect overspeeding
• Trigger emergency alerts
• Detect geofence violations

Security Architecture

Cloud Deployment Patterns

Key Takeaways

MongoDB is exceptionally suited for telematics because it combines:

• Time-series optimization
• Flexible schemas
• Geo queries
• Streaming ingestion
• AI vector search
• Massive scalability

This enables a single operational platform for:

• Connected vehicles
• Fleet intelligence
• Predictive maintenance
• Driver analytics
• AI-powered mobility systems