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Sensor Fusion & L4

Fleet Telemetry & Real-Time Sensor Processing at the Edge

How commercial autonomous fleets process terabytes of raw camera, LiDAR, and radar telemetry on-vehicle before streaming high-value disengagement events to cloud training clusters.

Self-driving electric vehicle equipped with roof-mounted LiDAR dome sensor suite

The Data Ingestion Bottleneck

An autonomous vehicle equipped with 10 high-definition cameras, 4 LiDAR units, and 6 radar suites generates over 2 Terabytes of raw data per operational hour. Uploading complete raw logs over cellular 5G is economically infeasible and bandwidth-prohibitive.

Edge Telemetry Filtering Strategy

  • Shadow Mode Execution: Running new neural models in background execution alongside active drivers to compute delta predictions.
  • Trigger-Based Event Logging: Hard braking, unexpected disengagements, or low-confidence perception triggers immediate 10-second buffer retention.
  • Compression & Vectorization: Converting raw point clouds into lightweight vector feature maps before transmission.

Automated Data Shadow Pipelines

By extracting active corner-case scenarios directly at the edge, engineering teams curate targeted training datasets to fine-tune perception models in continuous integration CI/CD loops.