Xilinx Demonstrates Solutions for ADAS and Automated Driving at CAR-ELE Japan 2017

By Xilinx


TOKYO, Jan. 11, 2017 /PRNewswire/ -- Xilinx, Inc. (NASDAQ: XLNX) will demonstrate solutions for Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) at CAR-ELE Japan. Demonstrations will including machine learning, computer vision and sensor fusion on Xilinx's Zynq® SoC and Zynq UltraScale+™ MPSoC devices.  Visit the Xilinx Booth E35-38 at CAR-ELE Japan, 18–20 January 2017, at Tokyo Big Sight, Japan.  

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Xilinx and Ecosystem Demonstrations –Booth E35-38

Deep Learning Using Convolutional Neural Networks (CNN) on Zynq UltraScale+ MPSoC  
Demonstration showcases the use of the automotive industry's first 16nm Zynq UltraScale+ MPSoC as an embedded computing platform for pedestrian detection using deep learning.

Camera-based Driver Monitoring System – Presented by Fovio 

Real-time machine learning-based demonstration for head and eyelid tracking, combined with precision gaze direction measurement that allows reliable driver attention and driver state detection under a wide range of challenging real-world conditions.

Multi-Camera System using Ethernet Audio/Video Bridging – Presented by Regulus, NEC Communication Systems, Ltd. and Linear Technology 

Sensor fusion demonstration implementing Ethernet-AVB in an automotive camera system. Using various camera modules based on the Zynq-7000 All Programmable SoC for image signal processing, object detection and distortion correction.
Advanced Camera/Display-based E-Mirror System – Presented by Toyota Tsusho Electronics Corporation 
Demonstration of an e-mirror system based on the Zynq-7000 All Programmable SoC shows an innovative way to replace conventional rear-view mirrors in cars or other vehicles. The system displays how multiple camera inputs are combined with a set of displays

High-end Surround View System – Presented by Xylon 
Sensor fusion demonstration of an advanced surround view virtual flying camera system enabling drivers to smoothly transition perspective of a three-dimensional view of the vehicle.

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