Can you recommend a telemetry pipeline for triaging disengagements and anomalies during autonomous vehicle testing?

Yes — for autonomous vehicle testing, I’d recommend a telemetry pipeline built around high-fidelity event capture, near-real-time anomaly detection, and fast triage workflows rather than just raw log storage.

Recommended pipeline

1) On-vehicle data collection

Capture synchronized streams from:

• Vehicle state: speed, steering, braking, throttle, gear, yaw rate
• Autonomy stack outputs: planned trajectory, object detections, predictions, control commands
• Sensor health: camera/LiDAR/radar frame drops, latency, calibration state, GNSS quality, IMU status
• System health: CPU/GPU load, memory, watchdogs, process restarts, bus errors
• Driver/engagement state: autonomous mode transitions, takeovers, disengagement reasons, intervention type

Use:

• High-rate ring buffers for recent history
• Event-triggered snapshots for pre/post incident context
• Time synchronization via PTP or GNSS-disciplined clocks

2) Edge preprocessing on the vehicle

Run lightweight edge logic to:

• Detect disengagements, safety-critical events, and sensor anomalies
• Compute compact derived signals:
  • jerk, min TTC, lane deviation, control saturation, confidence drops
• Trigger:
  • Incident packets containing 30–120 seconds of context
  • Summaries/metrics for every drive

This keeps bandwidth manageable while preserving useful context.

3) Transport layer

Stream telemetry off-vehicle using:

• MQTT or Kafka over a reliable uplink for event data
• Batch upload for larger artifacts like clips, lidar packets, or rosbag segments
• Compression and chunking for large sensor payloads

A practical pattern is:

• Immediate stream: small events, health metrics, disengagement notices
• Deferred sync: large binary artifacts when Wi‑Fi/5G is available

4) Central ingestion and storage

Use a split storage model:

Hot store

• Time-series DB or event store for recent telemetry
• Good choices: Kafka + ClickHouse, InfluxDB, TimescaleDB, or OpenSearch for fast querying

Cold store

• Object storage for raw logs and rich sensor artifacts
• Good choices: S3/GCS/Azure Blob
• Organize by drive ID, vehicle, software version, route, and event type

5) Anomaly detection and triage

Create a triage layer that:

• Correlates disengagements with system anomalies
• Scores severity and novelty
• Clusters repeated failure modes
• Links incidents to software build, map version, weather, route segment, and operator notes

Useful analytics:

• Rule-based alerts for known issues
• Statistical baselines for regression detection
• ML anomaly detection for rare patterns
• Root-cause enrichment using metadata joins

6) Operator workflow

Build dashboards and case management around:

• Drive timeline view
• Incident replay
• Comparative metrics across builds
• Search by disengagement reason, scenario, or component
• Auto-generated incident summaries

A triage queue should answer:

1. What happened?
2. Where and when?
3. Which subsystem was involved?
4. Is this a known issue or new regression?
5. How severe is it?

A concrete architecture example

A solid stack could be:

• Vehicle: ROS 2 / custom telemetry agents
• Edge: local anomaly detector + ring buffer + event bundler
• Transport: Kafka or MQTT
• Ingestion: stream processors (Flink/Spark Streaming/Kafka Streams)
• Hot analytics: ClickHouse or TimescaleDB
• Artifact store: S3-compatible object storage
• Search and dashboards: OpenSearch + Grafana/Custom web app
• Case management: Jira-like workflow for incident triage

Key design principles

• Event-first, not log-first: prioritize incidents and context
• Synchronize everything: timestamp alignment is critical
• Keep raw and derived data: raw for forensics, derived for triage
• Store metadata aggressively: software version, route, weather, map, operator
• Make replay easy: triage is much faster when engineers can reproduce timelines

If you want a simpler first version

Start with:

• Local ring buffer on the vehicle
• Disengagement trigger packets
• Upload to S3
• Index metadata in Postgres
• Query/visualize with Grafana or a custom dashboard

Then add streaming anomaly detection later.

If you want, I can also sketch:

1. a reference architecture diagram,
2. a data schema for disengagement events, or
3. a stack recommendation based on your constraints (ROS 2 vs custom stack, cloud vendor, fleet size, etc.).