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Created to address the needs of NVIDIA’s automotive partners for an open development platform, DRIVE PX 2 provides unprecedented amounts of processing power for deep learning, equivalent to that of 150 MacBook Pros.
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Deep Learning in Self-Driving Cars
Self-driving cars use a broad spectrum of sensors to understand their surroundings. DRIVE PX 2 can process the inputs of 12 video cameras, plus lidar, radar and ultrasonic sensors. It fuses them to accurately detect objects, identify them, determine where the car is relative to the world around it, and then calculate its optimal path for safe travel.
This complex work is facilitated by NVIDIA DriveWorks™, a suite of software tools, libraries and modules that accelerates development and testing of autonomous vehicles. DriveWorks enables sensor calibration, acquisition of surround data, synchronization, recording and then processing streams of sensor data through a complex pipeline of algorithms running on all of the DRIVE PX 2’s specialized and general-purpose processors. Software modules are included for every aspect of the autonomous driving pipeline, from object detection, classification and segmentation to map localization and path planning.
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NVIDIA delivers an end-to-end solution — consisting of NVIDIA DIGITS™ and DRIVE PX 2 — for both training a deep neural network, as well as deploying the output of that network in a car.
DIGITS is a tool for developing, training and visualizing deep neural networks that can run on any NVIDIA GPU-based system — from PCs and supercomputers to Amazon Web Services and the recently announced Facebook Big Sur Open Rack-compatible hardware. The trained neural net model runs on NVIDIA DRIVE PX 2 within the car.
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Since NVIDIA delivered the first-generation DRIVE PX last summer, more than 50 automakers, tier 1 suppliers, developers and research institutions have adopted NVIDIA’s AI platform for autonomous driving development. They are praising its performance, capabilities and ease of development.
“Using NVIDIA’s DIGITS deep learning platform, in less than four hours we achieved over 96 percent accuracy using Ruhr University Bochum’s traffic sign database. While others invested years of development to achieve similar levels of perception with classical computer vision algorithms, we have been able to do it at the speed of light.”
— Matthias Rudolph, director of Architecture Driver Assistance Systems at Audi
“BMW is exploring the use of deep learning for a wide range of automotive use cases, from autonomous driving to quality inspection in manufacturing. The ability to rapidly train deep neural networks on vast amounts of data is critical. Using an NVIDIA GPU cluster equipped with NVIDIA DIGITS, we are achieving excellent results.”
— Uwe Higgen, head of BMW Group Technology Office USA
“Due to deep learning, we brought the vehicle’s environment perception a significant step closer to human performance and exceed the performance of classic computer vision.”
— Ralf G. Herrtwich, director of Vehicle Automation at Daimler
“Deep learning on NVIDIA DIGITS has allowed for a 30X enhancement in training pedestrian detection algorithms, which are being further tested and developed as we move them onto NVIDIA DRIVE PX.”
— Dragos Maciuca, technical director of Ford Research and Innovation Center
The DRIVE PX 2 development engine will be generally available in the fourth quarter of 2016. Availability to early access development partners will be in the second quarter.