A commercial vehicle’s compute and memory are fixed the day it leaves the factory
But expectations for the AI running on that hardware keep growing.
That’s the tension O'Brian Hunter takes on in his latest blog post. His argument: stop treating AI models as always-on features and manage them as shared vehicle infrastructure. Separate what’s deployed and available from what’s running at any given time, and let vehicle context decide when each model deserves resources.
Moving intelligence to the vehicle also changes how teams collect data. Instead of uploading everything and searching the cloud for data relevant to a specific event or issue, the vehicle can detect an anomaly locally and collect data selectively. Fastlane™ Edge Smart Triggers puts that approach into practice. A lightweight edge model monitors vehicle behavior, and when it detects an abnormal condition, it triggers a Fastlane™ Collector policy that captures signals around the event. Data collection happens only when a policy is triggered.
To learn more, read the full post: https://lnkd.in/eWqCKcXp
#EdgeAI #CommercialVehicles #FastlanePlatform
Why this matters for talent
Sonatus is making the case that vehicle compute and memory get fixed at the factory while AI expectations keep rising, which is the core pitch for its software defined vehicle platform. That framing supports demand for embedded and vehicle software engineers comfortable working within fixed hardware limits. Sonatus has 20 open roles on the board.
Read the original LinkedIn post. Curated by Larry Sherwood Jr. from company announcements; analysis above is my own.