IMC Newsdesk

Stradvision, Socionext to market Automotive AI SoC

  • September 8, 2020
  • William Payne

Automotive AI specialist StradVision is collaborating with SoC developer Socionext to bring StradVision’s SVNet deep learning-based camera perception software to market.

SVNet is currently used in mass production models of ADAS and autonomous driving vehicles that support safety function Levels 2 to 4, and is scheduled to be deployed in more than 9 million vehicles worldwide.

The software allows vehicles to detect and recognise objects on the roads. It is designed to perform well even in harsh weather conditions and prevent traffic accidents by processing collected road data with speed and accuracy.

The collaboration is designed to bring object recognition technology with software and hardware specialised for deep learning to the Advanced Driver Assistance Systems (ADAS) and autonomous driving markets.

“This collaboration allows us to optimise the integration of our perception software into SoC developed by Socionext, resulting in extremely stable software operation within vehicles. SVNet running on Socionext’s SoC will be a great solution for the ADAS and autonomous markets where robust performance and safety are essential,” said Junhwan Kim, CEO of StradVision.

“Socionext has a proven track record of supplying a large number of custom SoCs for various in-vehicle applications, offering cutting-edge design development technology including support for miniaturisation processes and quality control,” said Koichi Yamashita, Head of the Automotive Business Unit, Automotive & Industrial Business Group at Socionext.

“It is now possible to provide object detection with high recognition accuracy that is optimised for vehicle-mounted camera systems. Software like SVNet can detect vehicles, pedestrians, lanes, and free space. In the automotive market, where significant growth is expected in the future, we will actively utilise StradVision’s software with the aim of providing custom SoCs that are a source of customer differentiation and competitiveness.”