Getting the right balance for ADAS architectures
Three different classes of ADAS system processing are needed—edge, zonal and central—with three different profiles.
Implementation basics for autonomous driving vehicles
The ideal architecture to make a viable and flawless L4 AD control loop must meet at least seven essential requirements.
Simulation testing for 3D object detection, collision prediction in AVs
Here is how to perform testing of the Point-GNN algorithm using LiDAR data in the simulated environment for autonomous vehicle…
C-V2X certification – necessary or just nice to have?
Wouldn't it be great if you could know in real time what is happening outside your line-of-sight?
Why FRAM memories are suitable for data logging in ADAS designs
The data-logging memory must understand what happens when vehicles have issues and provide the insights necessary to improve the ADAS…
Keysight emulator accelerates path to full vehicle autonomy
Keysight's Radar Scene Emulator emulates up to 512 radar objects and distances as close as 1.5m.
Renesas accelerates deep learning development for ADAS, automated driving applications
Renesas' R-Car SDK enables quicker and easier software development and validation for smart camera and automated driving applications.
Renesas automotive camera solution enables HD video using low-cost cables and connectors
Renesas solution enables cost-effective implementation of numerous safety features in virtually any vehicle.
NI accelerates AV development through monoDrive acquisition
The acquisition of monoDrive expands NI's reach in the ADAS and simulation markets.
The evolutionary path for DRAM and NAND flash technologies
The Korean memory chipmaker highlights the evolutionary path for DRAM and NAND flash technologies in a keynote address to IEEE…