ADAS Engineer Draws Parallels Between Self-Driving Systems and Modern AI Development
A professional with a background in Advanced Driver Assistance Systems (ADAS) and autonomous vehicles argues that AI is fundamentally an evidence-gathering process, not a mysterious technology. Drawing from years of scenario validation and edge-case analysis in self-driving systems, the engineer notes that sensors, algorithms, and probability-based decisions underpin both ADAS and large language model applications. The author highlights that the core questions in both fields are identical: when does the system fail, what data is it missing, and how does it behave when wrong. Having transitioned into broader AI engineering, the professional plans to share insights from applying safety-critical thinking to AI development.
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