Embedded engineering
that elevates intelligent
systems

Self-driving cars rely on machine learning (ML) algorithms that require large volumes of accurately labeled data to function safely and reliably. Most ML models used in the automotive industry today are supervised, which means they must be trained on annotated datasets that define and categorize real-world driving scenarios.  Data annotation enables autonomous vehicles to understand...

The automotive industry is pivoting to a transformative phase resulting from a shift to electrification, tighter emission regulations, and the growing complexity of vehicle software. By 2030, the share of electric cars is set to surpass 40% in overall car sales. So, automotive OEMs and Tier 1 suppliers experience increased pressure to deliver optimized, robust...

Cars have long conveyed basic information to drivers, like a warning light for low fuel or a door-ajar alert. But Vehicle-to-Everything (V2X) communication takes this a step further by enabling real-time “conversations” among cars, pedestrians, bicycles, road signs, and traffic signals. The result? More energy-efficient driving Enhanced safety Better user experiences However, building these complex...