Sunny Lee, COO, explains progress in deep learning with StradVision. It paves the way to self-driving automobiles.
FREMONT, CA:StradVision, a provider of vision processing technology, attended the Autotech Council Meeting: Sensor innovation for Transportation and Mobility at the Sensors Expo and Conference in San Jose, where Sunny Lee, COO of StradVision, spoke on the progress made in camera technology for autonomous vehicles.
The discovery made by StradVision's lean SVNet software helps automotive chipsets to run at affordable cost levels. StradVision aims to provide deep learning based software that can be Automotive Safety Integrity Level B (ASIL B) compatible for better functioning and safety.
Lee looks forward to expanding business in China, Japan, and Germany as he mentioned in the meeting.
Further, with keen observation and plans to enhance the growth in India and the United States of America, StradVision provides a base that allows Advanced Driver Assistance Systems (ADAS) in autonomous vehicles ensuring the next stage of safety, accuracy, and convenience. Investors at StradVision include tech players like LG Electronics and Hyundai Motor Company.
StradVision’s deep learning-based software will soon be seen running in China. StradVision’s SVNet software enables a real-time response, discovers obstacles in blind spots, alerts mechanism and prevents accidents. StradVision’s SVNet also helps in sudden changes in lanes and maintains vehicle speed even in places with low light and poor weather conditions.
StradVision works with automotive Original Equipment Manufacturers (OEMs) and Tier 1 suppliers that help in functions like Automatic Emergency Braking through the front camera, Blind Spot Detection via rear camera, and its software will help create automated valet parking systems.
StradVision, with an expert team of core engineers, has worked on various hardware platforms at Intel and Ola works. StradVision focuses on building active technology that helps the drivers in safe transportation, and transitions towards fully autonomous vehicles.