A technical perspective on AI-powered ADAS and autonomous driving systems — from sensor fusion to ISO 26262-aligned process.
Published Apr 12, 2026
utonomous driving is one of the hardest engineering challenges of our era. It combines safety-critical real-time systems, computer vision at the edge, sensor fusion across heterogeneous inputs, and regulatory compliance that rivals aerospace.
From ADAS to full autonomy
Modern ADAS stacks process data from 12+ sensors — LIDAR, radar, cameras, IMU, GPS, V2X — and must reach actuation decisions in under 10ms. The path from driver-assist features to full autonomy requires not just better models, but a fundamentally different approach to software architecture.
The hardest part isn't the technology. It's the operational culture required to ship it.
The certification problem
ISO 26262 ASIL-D is the highest safety integrity level in automotive software. Achieving it requires deterministic behavior, formal verification, and comprehensive test coverage — not characteristics traditionally associated with machine learning. Our approach combines classical control theory with ML as a bounded decision layer, where every output is verifiable.
What we've learned
After deploying across OEM fleets, three lessons stand out: safety cases must be designed from day one, sensor fusion benefits far more from algorithmic rigor than from bigger models, and the hardest part isn't the technology — it's the operational culture required to ship it.
Filed under
Whitepaper · Automotive
