Renesas’ R-Car Consortium Proactive Partner Program has selected StradVision as its member for the second consecutive year.
StradVision’s Artificial Intelligence-based camera perception software is a leading innovator in Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV),
StradVision, which has satisfied the consortium’s criteria of innovative, open and trusted, was verified as a high-performance company with a good track record in the automotive market, consequently enabling quick engagement with customers.
StradVision chief executive officer Junhwan Kim said, “Being selected as a member of the R-Car Consortium Proactive Partner Program is a fantastic opportunity to work even more closely with compatible vendors in the automotive industry.”
He further said that it is a very exciting prospect to be part of this platform to speed up the advancement of ADAS technology.
Engaging with ecosystem partners
R-Car Consortium is an open platform environment organized by Renesas, that enables customers to quickly identify and engage with ecosystem partners, whose solutions will help accelerate their innovation for the future mobility market, strengthening research and development for the connected car and ADAS.
StradVision and Renesas have been working closely since the announcement of their collaboration on developing a deep learning-based object recognition solution for smart cameras used in ADAS in September 2019.
Vice President of Automotive Digital Products Marketing Division at Renesas, Naoki Yoshida said, “We regard StradVision’s SVNet software as one of the best deep learning and camera based ADAS perception solutions for Front Facing Camera (FFC) and Surround View Monitoring (SVM).”
According to Yoshida, it was only natural to turn to StradVision to be the first deep learning-based algorithm to be run on Renesas’ R-Car V2H, R-Car H3, R-Car V3M, and R-Car V3H SoCs.
StradVision’s SVNet software allows for ADAS and autonomous vehicles to find objects like other pedestrians, animals, vehicles, lanes, free space, traffic signs, and lights, accurately, even in poor lighting or harsh weather conditions.
(With inputs from Automotive Lead Research Team)
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