Autonomous Systems

Autonomy systems need to replay more than sensor frames. They need the full chain: sensor state, scenario context, retrieved memory, model/tool calls, decisions, and outcomes. ZeptoDB keeps that chain on one timeline.

Why AV & autonomy teams choose ZeptoDB

Nanosecond time sync

Align LiDAR, camera, radar, IMU, GPS, and CAN with nanosecond-precision ASOF JOIN. Reproducible perception, frame after frame.

Driving-log replay

Query historical episodes from Parquet on S3 at full fidelity. Replay at wall-clock speed, accelerated, or scenario-sliced.

Zero-copy ML

522ns from query result to PyTorch tensor. Same SQL feeds research notebooks, regression tests, and online perception models.

Fleet scale

Multi-node cluster handles telemetry from thousands of vehicles concurrently. Symbol partitioning keeps cardinality bounded.

Scenario memory

Store prior interventions, edge cases, triage notes, and agent decisions as searchable memory linked to the driving log.

Where it fits

Multi-sensor fusion

Time-align LiDAR, camera, radar, IMU, GPS, and vehicle CAN at nanosecond precision
Window JOIN with ±N ms tolerance to absorb sensor clock drift
Production-ready pipeline for online perception and HIL bench

Scenario mining & triage

SQL over petabytes of driving logs in Parquet on S3
Find the scenarios that matter: “left turns at unprotected intersections with a pedestrian in rain”
Correlate vehicle state with map attributes, weather, and disengagement events

Training data & dataset curation

Query → NumPy → PyTorch / JAX in microseconds via zero-copy
Batch extraction via Arrow Flight for distributed training clusters
Reproducible dataset versioning through immutable Parquet snapshots

Fleet operations & safety

Real-time health monitoring across vehicles and edge compute
Disengagement and safety-driver-intervention analytics with window functions
Forensic replay when an incident occurs — exact event, exact timing

Autonomy agent workflows

Retrieve similar scenarios before labeling, triage, or remediation
Cache repeated explanations for common disengagement patterns
Store safety-driver notes, agent summaries, and model/tool calls beside raw logs
Reconstruct the decision path from sensor stream to retrieved context to action

Example: multi-sensor alignment

-- Align all sensor streams to LiDAR timestamps for a single vehicle
SELECT
    l.ts AS lidar_ts,
    l.points,
    c.frame AS camera_frame,
    r.detections AS radar_detections,
    i.accel_x, i.accel_y, i.accel_z,
    i.yaw_rate,
    g.lat, g.lon, g.heading
FROM lidar_scans l
ASOF JOIN camera_frames c ON l.vehicle_id = c.vehicle_id AND l.ts >= c.ts
ASOF JOIN radar_returns r ON l.vehicle_id = r.vehicle_id AND l.ts >= r.ts
ASOF JOIN imu_readings  i ON l.vehicle_id = i.vehicle_id AND l.ts >= i.ts
ASOF JOIN gps_fixes     g ON l.vehicle_id = g.vehicle_id AND l.ts >= g.ts
WHERE l.vehicle_id = 'vehicle-042'
  AND l.ts BETWEEN '2026-03-15 14:00:00' AND '2026-03-15 14:30:00'

Reference architecture

Vehicle / Robot Edge          Cloud Cluster              Training
─────────────────────    ──────────────────────    ─────────────────
LiDAR      ──→  │                            │
Camera     ──→  │ ZeptoDB Edge               │
Radar      ──→  │   ↓ WAL + Parquet          │──→  S3 Parquet HDB
IMU        ──→  │   ↓ Sync to cloud          │──→  ZeptoDB Cluster
GPS / CAN  ──→  │                            │──→  Arrow Flight
                 ──────────────────────────          ↓
                                                PyTorch / JAX Training

Why this matters commercially

Autonomy stacks tend to grow separate systems for online fusion, log replay, training data, vector search, and agent traces. Each comes with its own ingest path, schema drift, and on-call burden. ZeptoDB keeps the evidence and the memory together, reducing the distance between a real-world incident and a root-cause answer.

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