A globally known AI compute leader and an autonomous driving developer
L4-grade autonomous driving annotation supporting perception, prediction, and DMS.
This is a Lifewood Data Technology case study in Autonomous driving — an engagement delivered for a globally known AI compute leader and an autonomous driving developer across Global · Asia-Pacific. L4-grade autonomous driving annotation supporting perception, prediction, and DMS.
What was the challenge?
L4 autonomy programs require multi-modal sensor data annotated to safety-critical accuracy: LiDAR point clouds, multi-camera detection, radar fusion, and driver-monitoring system data. Throughput, accuracy, and audit trail are non-negotiable.
How did Lifewood approach it?
Lifewood operates as an autonomous-driving data partner for both clients, supplying driver-monitoring system data and supporting autonomous-driving AI model training. Programs span object detection, scene segmentation, 3D point cloud annotation, behavior prediction, and sensor fusion — delivered through dedicated AV centers in Malaysia and Indonesia.
What was the outcome?
Active partnership supporting both clients’ autonomous driving programs, with annotation accuracy benchmarked at 99.9% for L4-level scenarios.
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Questions about this programme
Annotation accuracy on this programme is benchmarked at 99.9% for L4-level scenarios. The threshold is that high because the error budget is physical rather than statistical: a mislabelled pedestrian in training data is not a percentage point, it is a failure mode. Lifewood runs these programmes under a dual-layer human-in-the-loop process with a full audit trail per asset.
Four, and they have to agree with each other. LiDAR point clouds give 3D structure, multi-camera detection gives semantics, radar adds velocity and works in weather that defeats cameras, and driver-monitoring data covers the cabin. This programme spans object detection, scene segmentation, 3D point-cloud annotation, behaviour prediction and sensor fusion — fusion being the part where single-modality vendors usually stop.
Through dedicated AV centers in Malaysia and Indonesia, inside Lifewood's 40+ delivery center footprint spanning 50+ languages and held to a 95%+ accuracy SLA. Dedicated rather than shared, because AV annotation tooling, training and security review differ enough from general labelling that mixing them degrades both.
By treating them as one requirement rather than a trade-off. Throughput comes from trained, dedicated teams rather than from relaxing review — 414,120 training hours were delivered across the workforce in 2025, averaging 60 hours per person — and every asset keeps a timestamped approval record so a programme can be audited long after delivery.
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