DriveCore™ was designed as a complete technology platform, consisting of the hardware, in-vehicle middleware and PC-based software toolset needed to develop machine learning algorithms for autonomous driving applications of Level 3 and above. Building on
"To meet the computing demands of Level 3-plus solutions, autonomous systems will need highly scalable levels of processing power and the ability to perform sensor fusion across multiple radar, camera and Lidar sensors," explained
DriveCoreTM consists of three primary components, all of which can be experienced at
- Compute is a modular, scalable computing hardware platform that can easily be adapted to all levels of automated driving. It is designed to deliver from 500 gigaflops to 20 teraflops of processing power (with existing Systems on Chip) in a scalable manner, independent of the type of central processing unit (CPU) used. It will support
NVIDIA, Freescale, Qualcommand, later, other processor types seamlessly – protecting an automaker's investment in this technology.
- Runtime is in-vehicle middleware that provides a secure framework to enable applications and algorithms to communicate in a real-time, high-performance environment. It enables sensor fusion in a sensor-independent manner, so sensors can be upgraded as new capabilities become available, such as radar going from 2-D to 3-D.
- Studio is a PC-based development environment that enables automakers to create and support an ecosystem of algorithm developers, unlocking innovation potential through an open framework for sensor-based artificial intelligence algorithm development. Studio allows easy integration of third-party algorithms and access to real-life sensor data – complemented by a simulation, validation and benchmarking environment for algorithms ranging from object detection to camera-based lane detection.
Consistent with the goal of creating an open collaboration model for automakers,
- DeepScale uses deep learning to create an integrated model of the environment in real-time, from any combination of sensors. Then, DeepScale's deep neural networks (DNNs) add the perception capabilities needed for automated driving.
- STEER provides a fully automated parking solution, in which the car drops off passengers at a designated point, drives itself to the nearest automated parking zone, waits for a "summon signal," then drives to the designated pick-up point.
- StradVision deploys machine learning algorithms to build advanced object detection and recognition software.
- Automotive Artificial Intelligence provides a graphical simulation environment, offering intelligent traffic and traffic scenarios, which runs in conjunction with DriveCore™ Studio.
"Successful implementation of autonomous driving technologies will require collaboration from multiple companies offering specific expertise in different aspects of the solution," said
Visteon Media Contact: Jim Fisher, 734-417-6184 - mobile, firstname.lastname@example.org