ADAS
Sensor Fusion Intelligence That Sees What Cameras Miss
Multi-modal perception pipelines combining LiDAR, radar, camera, and V2X into a single ISO 26262 ASIL-D certified fusion stack — engineered for production vehicles.
Request a Demo99.97%
Object detection rate
< 50 ms
Fusion latency
< 10 cm
HD Map lane accuracy
ASIL-D
ISO 26262 safety level
See it in action
ADAS Sensor Fusion at Work
Multi-modal sensor fusion — LiDAR + Radar + Camera + V2X
HD Maps
The Ground Truth Layer Every ADAS System Needs
HD Maps are the foundational layer of every ADAS system. VoxelVeritas builds centimetre-accurate HD Maps that give vehicles a precise, pre-computed understanding of the road ahead — lane geometry, speed limits, traffic signs, road curvature, and elevation — before a single sensor fires.
Sample output
What an HD Map Looks Like
Lane-level road geometry — OpenDRIVE format
Vehicle sensor array — LiDAR + Radar + Camera integration
Capabilities
What the HD Map & Fusion Stack Delivers
Lane-Level Geometry
Sub-10 cm accurate lane boundaries, merge/split topology, and road curvature encoded in OpenDRIVE and NDS formats for direct ADAS consumption.
Static Object Layer
Traffic signs, signals, guardrails, kerbs, and road markings mapped and classified — giving ADAS systems a verified ground-truth reference independent of live sensor data.
Elevation & Slope Profiles
3D road surface models with gradient and banking data, enabling predictive braking and stability control on complex terrain.
Real-Time Map Delta Updates
Fleet telemetry continuously patches the HD Map layer — road works, new signs, and lane changes propagate to vehicles within minutes, not months.
Sensor Fusion Integration
HD Map priors feed directly into the LiDAR + radar + camera fusion stack, reducing false positives and improving object classification confidence by up to 40%.
ISO 26262 ASIL-D Certified
Every map layer is produced under a functional safety management system certified to ISO 26262 ASIL-D, meeting the most stringent automotive safety integrity requirements.