GPUS FOR AUTONOMOUS DRIVING ELIF ALBUZ VISION SOFTWARE

WHY AUTONOMOUS? Safety

Efficient utilization of resources (roads, time, fuel)

Mobility

TRENDS & REGULATIONS

SELF-DRIVING NEEDS.. Sensor and image data processing

Interpretation of visual sensor data

COMPUTER VISION

Power efficiency Safety critical processing

HIGH COMPUTATIONAL COMPLEXITY Computer Vision

Detect pedestrians, road plane, traffic signs, lanes,..

Sensor fusion Path planning and control

HIGHLY PARALLEL +

HIGH COMPUTATIONAL COMPLEXITY =

GPU

NVIDIA Automotive

6.2 M Cars on the road

More coming…

20+ Brands 100+ Models

UNIFY GPU & TEGRA ARCHITECTURE

MOBILE ARCHITECTURE

Maxwell

Kepler

Tesla

Fermi

Tegra 3

Tegra 4

GPU ARCHITECTURE

TEGRA K1

TEGRA K1 192-Core Super Chip 326 GFLOPs CUDA

ONE ARCHITECTURE 1X

~200 Cores 10X

~2000 Cores 100X

~20,000 Cores

CUDA

ONE ARCHITECTURE MULTIPLE WAYS TO AUTONOMOUS DRIVING

1X ~200 Cores

10X ~2000 Cores

100X ~20,000 Cores

Massive Vision Learning 3D Map processing

High-end Vision functionality Development and prototyping

Embedded Vision Deployment

CUDA

TEGRA K1 DEVELOPMENT PLATFORMS

JETSON TK1 PRO gigE, USB3.0, HDMI, CANBUS

running Vibrante Linux

AUTOMOTIVE GRADE

JETSON TK1 gigE, USB3.0, HDMI

running Linux4Tegra

$192

COMPUTER VISION ON CUDA

Feature Detection / Tracking ~30 GFLOPS @ 30 Hz

Object Recognition / Tracking

~180 GFLOPS @ 30 Hz

3D Scene Interpretation ~280 GFLOPS @ 30 Hz

VISUAL SUPERCOMPUTING WITH TEGRA K1

COMPUTER VISION ON GPUS

NPP OpenCV VisionWorks NVIDIA Performance

Primitives for basic image & vision processing

Hundreds of functions,

accelerating OpenCV and other libraries

Open Source Computer Vision Libraries

> 900 functions, widely adopted for prototyping

NVIDIA Computer Vision Library

High performance functions and algorithms

SOFTWARE STACK

Application Code

Sample Pipelines

Tegra/Kepler dGPU

CUDA

VisionWorks Framework

OpenVX

VisionWorks APIs

…

Classifier Corner Detection

Feature Tracking

Hough Detection

SAMPLE PIPELINES Application Code

Demo & Sample Pipelines

Feature Tracker

Hough Detection

Feature Tracker Hough

Circle/Line Object Tracker Optical Flow

Denoising

PRIMITIVES

Absolute Difference Accumulate Image Accumulate Squared Accumulate Weighted Add Affine Warp And BoxFilter Canny Edge Detector Channel Combine Channel Extract Color Convert Convert Depth Convolution Dilation Filter

Erosion Filter Fast Corners Gaussian Filter Gaussian Image Pyr. Harris Corners Histogram Histogram Equalization Integral Image Magnitude Mean Std Deviation Median Filter Min Max Locations Multiply Not Optical Flow (LK)

Or Perspective Warp Phase Node Remap Scale Image Sobel 3x3 Subtract Table Lookup Threshold Xor

Bilateral Filter Convert To Gray Corner FAST Corner Harris Fast NLM Denoising Find Homography HOG (Hist. of Oriented Gradients) Hough Circles Hough Lines IME (Iterative Motion Estimation)

Integral Histogram Median Flow Optical Flow Farneback RANSAC Scharr3x3 Soft Cascade Detector Stereo Block Matching Object Tracker Algorithm SLAM Algorithm

Additional NVIDIA Primitives All OpenVX Primitives

THE ROAD TO THE SELF DRIVING CAR DEPENDS ON VISUAL SUPERCOMPUTING

QUESTIONS?