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Inference strategy - Guidance for an Automotive Data Platform on AWS
Why two approachesDaily batch: slow leak detectionReal-time: highway blowout riskTraining dataConnected Mobility integration

Inference strategy

This section explains the dual inference approach used for tire health prediction and the cost-driven reasoning behind each choice.

Why two approaches

Tire failures have fundamentally different time scales that require different detection strategies:

Failure Mode Time Scale Detection Window Inference Needed

Slow leak

Days to weeks

3-7 days before threshold

Daily batch

Valve failure

Intermittent over weeks

Days

Daily batch

Highway blowout

Minutes

Seconds to minutes

Real-time

Daily batch: slow leak detection

A slow leak drops 0.5-1.2 PSI per day. The window from "detectable trend" to the 28 PSI alert threshold is 3-7 days. Checking once per day gives 4+ days of advance warning. Checking every 15 minutes gives the same 4+ days of warning — the extra granularity adds cost without adding value for a condition that changes over days.

The daily batch Lambda queries the last 7 days of tire telemetry from DynamoDB, computes a linear regression slope per tire, and writes predictive warnings for tires losing pressure consistently (> 0.3 PSI/day with current pressure below 30 PSI).

When the ML model adds value over simple trend detection:

  • Temperature-related pressure changes (cold morning vs warm afternoon) look like leaks to a simple trend line, but the ML model accounts for ambient temperature correlation

  • Intermittent valve failures show irregular pressure patterns that linear regression misses

  • Altitude changes during mountain routes cause temporary pressure drops that are not leaks

Cost:

Approach Daily Cost Monthly Cost

Daily batch (Lambda + DynamoDB query)

$0.02

$0.60

Real-time endpoint (ml.m5.large)

$2.76

$83.00

At 50 vehicles with approximately 2 slow leaks per year, the real-time approach costs $1,000/year to save $2,000-3,400. The daily batch costs $7/year for the same outcome.

Real-time: highway blowout risk

A tire under combined stress — high speed, high temperature, low tread, and borderline pressure — can fail catastrophically in minutes. Each signal individually is within normal range: pressure at 29 PSI (above the 28 PSI threshold), temperature at 140°F (elevated but not alarming alone), tread at 3.5mm (above the 3mm threshold). But the combination is dangerous.

A rule-based system cannot catch this because no single threshold is crossed. The Random Cut Forest model recognizes the multi-signal pattern that preceded blowouts in the training data.

Pre-filtering to control cost:

The SageMaker endpoint is not called for every telemetry message. It is only invoked when:

  1. Vehicle speed > 60 mph (highway driving), AND

  2. Any tire pressure < 30 PSI OR tire temperature > 120°F

This filters over 90% of telemetry. Instead of 19,200 inferences per day (50 vehicles × 4 tires × 4 readings/hour × 24 hours), the endpoint receives approximately 50-100 inferences per day for vehicles in active risk conditions.

Cost justification:

Item Cost

SageMaker endpoint (ml.m5.large)

$83/month

Highway blowout (tow, tire, cargo damage, downtime, liability)

$10,000+ per incident

At 5,000 vehicles: ~5 blowouts/year prevented

$50,000+ saved

ROI

50x

Training data

The model is trained on a synthetic dataset generated from realistic fleet patterns:

  • 721,024 records across 50 vehicles over 6 months

  • Normal driving patterns with seasonal temperature effects (Gay-Lussac’s law), city-specific climate variation (Dallas, Atlanta, Chicago, Phoenix, Seattle), rear tire load differential, and natural tread wear

  • Injected anomalies: slow leaks (8%), punctures (4%), valve failures (3%), overinflation (2%)

  • Features: pressure, temperature, delta_pressure, delta_temp

  • Model: SageMaker Random Cut Forest (unsupervised anomaly detection), trained on normal data only

The training dataset can be regenerated with scripts/generate_training_data.py and the model retrained with scripts/train_model.py in the source repository.

Connected Mobility integration

When used with the Connected Mobility Guidance, the prediction pipeline integrates as follows:

  1. The CMS simulator or real FWE agent sends tire telemetry through IoT Core → MSK → Flink

  2. The Flink MaintenanceProcessor evaluates rule-based thresholds from the event catalog (fires at 28 PSI)

  3. The daily batch Lambda queries the CMS telemetry table and writes prediction.tire_slow_leak warnings

  4. For highway vehicles with concerning signals, the real-time Lambda calls the SageMaker endpoint and writes prediction.blowout_risk alerts

  5. Both prediction alert types appear in the CMS Fleet Manager UI alongside rule-based maintenance alerts

  6. The CMS adapter (source/lambda/cms_adapter.py) transforms CMS canonical field names (tire_pressure_fl) to the per-tire format expected by the model