Sensor data platform
Hardware-timestamped ingest, synchronisation and versioning of LiDAR, radar, ultrasonic and camera streams, with per-vehicle calibration records.
Fusion pipeline and on-vehicle runtime
A modular platform for sensor-data ingest, calibration, multimodal fusion, model training, optimisation and on-vehicle inference.
Development vehicle, sensor suite
Multi-sensor perception pipeline
The platform provides defined interfaces for data ingest, calibration, fusion, model training, optimisation and on-device inference.
- 01
Sensor Data Plane
Ingest and synchronise LiDAR, radar, ultrasonic and camera streams with per-vehicle calibration.
- Hardware-timestamped capture
- Per-vehicle calibration records
- Versioned datasets
- 02
Calibration & Fusion
Align every modality to one coordinate frame and time base, then fuse into a shared representation.
- Intrinsic and extrinsic calibration
- Temporal alignment and drift checks
- Unified spatial representation
- 03
Model Training
Train perception and planning models on the fused representation, adapted to each sensor set.
- Multimodal perception models
- Scenario-based evaluation
- Hardware-aware training
- 04
On-Device Runtime
Compile, quantise and run the model on the automotive SoC inside a fixed budget.
- Quantisation and distillation
- Deterministic scheduling
- Latency and power budgeting
The pipeline runs on your infrastructure or ours. Training happens in a data centre; inference happens on the vehicle.
From sensor ingest to model evaluation
The platform provides an integrated workflow from sensor-data management through model deployment and evaluation.
Calibration & fusion
Intrinsic and extrinsic calibration, temporal alignment and drift monitoring, producing one spatial representation the model can consume.
Multimodal models
Perception and planning models trained on the fused representation and adapted to your sensor configuration.
Model optimisation
Quantisation, distillation, pruning and hardware-aware tuning for the automotive SoC in the vehicle.
On-device runtime
Deterministic inference, real-time scheduling and safety-bounded planning and control.
Evaluation & tooling
Scenario simulation, regression suites and dataset versioning that keep results comparable across model revisions.
Stage-by-stage operating requirements
Operational requirements are measured on the production stack and defined for sensor input, calibration, fusion, training, export and on-device inference.
- Sensors
- LiDAR, mmWave radar, ultrasonic and camera on one hardware-timestamped, calibrated time base.
- Calibration
- Per-vehicle intrinsic and extrinsic calibration, with drift monitoring and recorded calibration versions.
- Fusion
- A shared spatial representation that stays valid when a sensor is added, replaced or removed.
- Training
- Multi-node GPU training with checkpointing, elastic scheduling and failure recovery.
- Model export
- Quantised formats for automotive SoCs, produced through a hardware-aware tuning step.
- On-device inference
- Deterministic scheduling with a bounded latency and power budget from perception through planning.
Multi-sensor calibration and fusion
LiDAR, radar, ultrasonic and camera data are calibrated per vehicle and aligned to a common coordinate frame and time base before model training.
Fusion is performed in a shared spatio-temporal representation, with sensor configuration changes managed through calibration and dataset versioning.
- Per-vehicle calibration with drift monitoring
- Hardware-timestamped synchronisation across four modalities
- Radar and ultrasonic fused with camera and LiDAR
Deployment architecture
Training and data processing run in public cloud, private cloud, on-premise or edge environments; quantised inference models run on the vehicle.
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Public cloud
Elastic training and data processing on managed infrastructure, for programmes that start quickly.
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Private cloud or on-premise
Inside your network, under your data-governance and access-control regime.
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Edge
Regional processing close to where data is collected, keeping transfers bounded.
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In-vehicle
The quantised model and runtime on automotive compute, inside the latency and power budget.
Architecture and integration review
The review covers sensor configuration, calibration requirements, target automotive compute, deployment constraints and initial scope.