このスライドは、2018 年 4 月 24 日 (火) にベルサール高田馬場にて開催の NVIDIA Deep Learning Seminar 2018における、エヌビディア 技術顧問　馬路 徹、シニアソリューションアーキテクト 室河 徹による「GTC 2018 で発表された自動運転最新情報」の資料です。

1. 馬路 徹
技術顧問、GPUエバンジェリスト
エヌビディア
GTC 2018 で発表された
自動運転最新情報

2. 目次
GTC2018 での NVIDIA 自動運転関係・技術発表及び関連アナウンスメント （補
足スライド入り）
1. S8666: Deploying Autonomous Vehicles with NVIDIA DRIVE
2. S8531: Deep Learning Infrastructure for Autonomous Vehicles
3. S8294: NVIDIA DRIVE Safety: NVIDIA's Strategies for Enabling Safety in Automotive
Platforms
4. S8324: Synthetic Data Generation for an All-in-One Driver Monitoring System
5. ANNOUNCEMENT: NVIDIA and ARM Partner to Bring Deep Learning to Billions of IoT
Devices
P2

3. Shri Sundaram
DEPLOYING AUTONOMOUS
VEHICLES WITH NVIDIA DRIVE
S8666

4. ANNOUNCING “PEGASUS”
ROBOTAXI DRIVE PX
レベル5 完全自動運転
▪ Xavier (Volta GPU integrated) x 2
▪ Next generation discrete-GPU x 2
▪ 320 TOPS CUDA TensorCore
▪ ASIL D Certification
▪ Combined Memory Bandwidth: >1TBytes/sec
▪ Automotive I/Os
▪ 16x GMSL High-speed Camera Inputs
▪ Multiple 10Gbit Ethernet
▪ CAN, Flexray
▪ Late Q1 Early Access Partners
▪ Supercomputing Data Center in your Trunk
補足スライド

5. DRIVE DEVELOPMENT PLATFORM
Development Platform for DRIVE Xavier & DRIVE Pegasus
– building on the capabilities of DRIVE PX 2
Auto-grade ASIL-D Safety MCU
Upto 16x CAN
2x Flexray
Ethernet:
4x 10Gbps
7x 1Gbps or 100Mbps
5x 100Mbps
2x Xavier SOC:
CV & DL Accelerator
CUDA Processing
137GB/s LPDDR4x
2x discrete GPU:
Next Gen CUDA GPU
Tensor Core
Support for IST
384GB/s GDDR6
Raw Sensor Input
16x GMSL
91Gbps
XAVIER
Next
Generation
GPU
Next
Generation
GPU
DeSer
DeSer
DeSer
XAVIER
DeSer
MCU
PCIE
Switch
NVLINK
NVLINK
ENET
2x Xavier Developer I/O
HDMI
4 x USB
UART (via USB)
JTAG
Only with
Pegasus
DRIVE PX2 | DRIVE Xavier | DRIVE Pegasus | One Architecture

6. DRIVE SOFTWARE
NVIDIA Deliverables
DRIVE Development Platform
Sensors
Sensors
& Maps
NVMEDIA DRIVE OS, CUDA
CUDA accelerated libraries
(notably TensorRT)
DriveWorks Algorithm Modules
Autonomous Driving Applications
DriveWorks
Tools DNNs
Included Not included
DRIVE OS (Linux or QNX)
NVMEDIA
SAL
Sensor Abstraction
Layer
CUDA libraries
(CuDNN…)
DriveWorks Algorithm Modules
TensorRT NvMedia
(VPI)
Open GL
ES
VPI: Vision Primitives / Programmable Interface
(OpenVX, VisionWorks supported by CUDA)

7. NvMedia
(Camera,
ISP,
Encoder
TensorRT
NvMedia
(VPI)
CUDA
OpenGL
Xavier CPU
Video Encoder
0.9 - 1.4 Gpix/s
Video Decoder
– Gpix/s
Deep Learning
Accelerator (DLA)
5 FP16 / 10 INT8 TOPs DL
ISP
1.5 Gpix/s
VIC
2 Gpix/s
PVA
0.5 - 1.3 TOPS
Stereo & OF
(SOFE)
6 TOPS
X 16
Camera
• Raw data for DNN
training
• Detection,
classification,
segmentation
• Computer vision,
autonomous vehicle
algorithms
• Archiving, computer
data for DNN,
automotive simulation
• Visualization, in-
vehicle display
GPU Compute
1.3FP32 TFLOPS CUDA
20INT8 TOPs DL
GPU Graphics
1.3 TFLOPS FP32
Human
Interface
Compressed
Storage
Math
Computation
DNN
Inference
Raw
Data Storage
Computer
Vision
• Autonomous vehicle
algorithms
Lidar
Radar
...
XAVIER SOC ENGINES
& DRIVE OS
Xavier HW Engines DRIVE OS
SW APIs
Inputs
Data
Destination
Purpose
ISP: Image Signal Processor
VIC: Video Imaging Controller
PVA: Programmable Vision Accelerator
OF: Optical Flow
SOFE: Stereo Optical Flow Engine

8. DRIVEWORKS
Modules (with C APIs):
 Sensor, Vehicle I/O abstraction
 Automotive image processing modules
• Stereo/Rectification, Color Correction…
 Automotive Computer Vision modules
• Point Cloud Processing, SFM(Structure
from Motion), 2D Tracker…
Tools
 Recording, Replaying, Visualization.
Abstracting the vehicle

9. DRIVE DEVZONE
 SW Installer & Dev. Tools
 Latest SW
 Documentation
 Links to Support
 Updates on Ecosystem
https://developer.nvidia.com/DRIVE

10. REVISITING THE FLOW…
to put an Autonomous Vehicle on the road?
Curated
Annotated
Training Data
Data Acquired
From Sensors
Trained
Deep Neural
Network
Autonomous Vehicle
Applications
Autonomous Vehicle
Application Development
Test/Drive
Simulation
& Re-Simulation
HD Map
Neural
Network
Training
1
2
3
Data Acquisition to train DNN and create HD Map
Autonomous Vehicle Application Development
Testing In-Vehicle or With Simulation
1
2
3
DRIVE PX
DRIVE PX
(HIL)
DRIVE PX
DGX (SIL)

11. DRIVE PX 2
2 PARKER SoC + 2 PASCAL GPU
| 20 TOPS DL | 120 SPECINT | 80W
DRIVE PX XAVIER
20 TOPS DL | 160 SPECINT | 20W
（20 TOPS DL/20Wは高電力効率使用の場合)
DRIVE PX XAVIER
開発時：DRIVE PX2
量産時：DRIVE PX XAVIER
補足スライド

12. NVIDIA DRIVE
ROADMAP
ONE ARCHITECTURE
Auto-Grade
Super Energy-Efficient
ASIL-D Functional Safety
DRIVE PX
Parker
DRIVE PX 2 DRIVE Xavier
OrinDRIVE Pegasus
Performance
Timeline
20182016

13. 2018 2019
DRIVE AI COMPUTER
Development to Production | One Architecture
DRIVE™ Xavier
30 TOPS | 1x Xavier
DRIVE™ Pegasus
320 TOPS | 2x Xavier + 2x Next Gen GPU
DRIVE PX 2
24 TOPS | 2x Parker + 2x Pascal dGPU
DRIVE™ Development Platform - Xavier
60 TOPS | 2x Xavier
DRIVE™ Development Platform - Pegasus
320 TOPS | 2x Xavier + 2x Next Gen GPU
DEVELOPMENT PRODUCTION

14. SIMULATION —
THE PATH TO BILLIONS OF MILES
World drives trillions of miles each year.
U.S. has 770 accidents per billion miles.
A fleet of 20 test cars cover 1 million miles
per year.

15. ANNOUNCING
NVIDIA DRIVE SIM
AND CONSTELLATION
AV VALIDATION SYSTEM
▪ Virtual Reality AV Simulator
▪ Same Architecture as DRIVE Computer
▪ Simulate Rare and Difficult Conditions
▪ Recreate Scenarios
▪ Run Regression Tests
▪ Drive Billions of Virtual Miles
10,000 Constellations Drive 3B Miles per Year
DRIVE SIM
Generating
Virtual Reality
3D Image
Stimulus
Constallation
Running
Autonomous Driving
Application on real
HW
Interfaces
HW Platform
3D Virtual Images
Car Responses
Example: 4 Camera based Autonomous Driving Simulation
HIL (Hardware In the Loop) Simulation

16. 16
S8531

17. 自動運転用ディープラーニングの課題
▪ 機能安全は最重要要件。その他に再現性、推論性能他も重要。
▪ 学習に膨大なデータが必要。Raw Data、10以上のセンサー入力、地域依存性
▪ 全ての周囲環境を把握・判断するための何種類ものディープ・ニューラルネット
▪ 長大な学習に要する時間
▪ 全End-to-end workflowの最大限の時間短縮が必要 【データ収集、ラベリング、学習、再
学習、検証、全車に展開】
膨大なデータ、長大な学習時間

18. 自動運転用ディープラーニングの課題
▪ データ収集車： 100台
▪ 収集データスケール：2000時間 データ／車／年
▪ 各車に5台の2M画素カメラ、レーダ他センサー実装：1 Tバイト / 車 / 時間
▪ 1年間に総計200 Pバイト（Peta:1015）の膨大なデータを収集
▪ ただし収集データの1/1000 のみを深層学習に使用（キュレート、ラベル付け済みデータ）
▪ ResNet50クラスの画像認識ニューラルネットを上記データで学習：
▪ 1台のPascal GPU, 12.1 年
▪ 1基のDGX1 AIスーパコンピュータ (Pascal GPU x 8)：1.5年
▪ 今日、上記の1/10のデータ、8基のDGX-1(Pascal GPUx8 x8)では1週間で学習完了 (最新のVolta
GPU使用DGX-1はｘ12の性能）
スケールの想定

19. NVIDIA DRIVE SAFETY
GTC 2018
S8294

20. NVIDIA DRIVE FUNCTIONAL SAFETY ARCHITECTURE
HD Map
DRIVE XAVIER DDPX DGX (DDPX Emulation)
AutoSIM
(Photo Realistic 3D-CG by DGX
for Corner Cases etc)
DRIVE OS
▪ Diverse Engines
▪ Dual Execution
▪ ECC/Parity
▪ Diagnosis, BIST
Hypervisor
CUDA, TensorRT
ISO 26262
DRIVE AV
System Operates Safely Even when Faults Detected
Holistic System — Process & Methods, Processor Design, Software, Algorithms, System Design, Validation
ISO 26262 ASIL-D Safety Level | Partnership with BlackBerry QNX and TTTech | New AutoSIM Virtual Reality 3D Simulator
Same GPU
Architecture
Re-SIM
(Captured Real Data)
Traffic Environment Input(Stimulus)
and Testing/VerificationHIL SIL
HIL: Hardware (DDPX) In the Loop
SIL: Software(DGX) In the LoopISO26262
ASIL-D

21. DRIVE SOFTWARE
NVIDIA Deliverables
DRIVE Development Platform
Sensors
Sensors
& Maps
NVMEDIA DRIVE OS, CUDA
CUDA accelerated libraries
(notably TensorRT)
DriveWorks Algorithm Modules
Autonomous Driving Applications
DriveWorks
Tools DNNs
Included Not included
DRIVE OS (Linux or QNX)
NVMEDIA
SAL
Sensor Abstraction
Layer
CUDA libraries
(CuDNN…)
DriveWorks Algorithm Modules
TensorRT NvMedia
(VPI)
Open GL
ES
VPI: Vision Primitives / Programmable Interface
(OpenVX, VisionWorks supported by CUDA)
補足スライド

22.  Critical Components are ASIL-D
QNX RTOS, Classic AUTOSAR, & Hypervisor
 To be Safe, must be Secure
Secure boot, Security Services, Firewall, & OTA
 To be Safe, must be Real-time
QNX RTOS for mission application
Hypervisor for Quality of Service (QoS)
DRIVE OS
Safe, Secure, & Real-time

23. NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE.
WHY QNX
FOR
SAFETY?
 Safety OS Key Selection Criteria:
ISO 26262 ASIL D Certified RTOS
ISO 26262 qualified tool chain (up to TCL 3)
POSIX PSE52 standards certification
- Requirement for CUDA, cuDNN support
Common Unix heritage with Linux
- Rich dependent library support
TCL3: Tool Confidence Level
POSIX PSE52: support application portability at source code level

24. SOFTWARE SAFETY FRAMEWORK

25. NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE.
SOFTWARE SAFETY FRAMEWORK
 Three Level Safety Supervision (3LSS) architecture
 Coherent with Xavier SOC safety architecture and DRIVE platform
• Support for Xavier SOC HW Safety Manager
• Integrated into DRIVE OS to be available for DRIVE platform
• Anchored by ASIL D capable Safety-MCU on DRIVE platform
 Provides standard mechanism for handling of potentially safety critical errors and
performing diagnostics
 Supporting freedom from interference in execution, memory and information
exchange domains
Overview

26. NVIDIA SAFETY FRAMEWORK
THREE LEVEL SAFETY SUPERVISION (3LSS)
L1SS
One Partition on Hypervisor
CCPLEX(CPU Complex) = Carmel CPU x 8
L2SS
Safety OS & AutoSAR
SCE(Safety Control Engine) = R5 CPU x 2 in Lock-step)
L3SS
Safety OS & AutoSAR
External ASIL-D MCU
SHM (Safety Hardware Manager)
Safety Supervision Serial Channel on SPI I/F Error Signaling Pin
Heartbeat Monitoring on SPI I/F
Safety PMIC (Power Management IC)
XAVIER
▪ Diverse Engines
▪ Dual Execution
▪ ECC/Parity
▪ Diagnosis
▪ BIST (Built In Self
Test)

27. SOFTWARE SAFETY FRAMEWORK
 Flexible and extendable to handle the increasing safety requirements
 Configurable and portable to new DRIVE versions
 Off the shelf safety solution, optimizing/reducing effort on application side.
 Enables easy deployment of safety applications on DRIVE platform
• Provides safety services such as flow monitoring
 Supports the fault tolerant foundation of our Drive platform
Outcome

28. Sagar Bhokre
SYNTHETIC DATA GENERATION
FOR AN ALL-IN-ONE DMS
S8324

29. NVIDIA DRIVE IX SDK
IX (Intelligent eXperience) Toolkit
Sense Inside & Outside the Vehicle | Deep Learning Powered | Early Access Q4
Your Car is an AI
Customer Application
DRIVE OS
DRIVE AV
Object, Path, Wait Perception
DRIVE IX
Gaze, Head Pose, Gestures,
Recognize Face,
Voice Recognition & Lip Reading
Exterior Driver
Recognition
Automatic
Personalization
Inattentive Driver
Alert
Cyclist
Alert
Distracted Driver
Alert
Driver/Passenger
Recognition
Multiple In-Car Sensors
補足スライド

30. NEED FOR SYNTHETIC DATA
Example synthetic image

31. NEED FOR SYNTHETIC DATA
 No devices available – e.g. face landmarks at extreme angles
 Manual labelling – limited by human precision and error
 Manpower and time limits – recording in different environments
 Sensor interference with scene – glasses for eye tracking, optical markers
 Needs high resolution devices – head poses, gaze
 Lacks associativity and completeness – multiple recordings for multiple parameters
Where does real world data fall short?

32. NEED FOR SYNTHETIC DATA
 More flexible - environment parameters, camera distance, background
 Error free - Free from manual labelling errors / Human errors
 Accurate - Synthetic data readings are highly accurate. No sensor noise
 High resolution as well as low resolution images can be generated
 Allows labelling of occluded areas
 3D as well as 2D labels can be accurately generated
 Fast and economical
Where does synthetic data offer advantages?

33. AGENDA
 Data Generation Pipeline
 Parameter Settings
 Post Processing
 Deployment

34. DATA GENERATION PIPELINE
Steps involved in synthetic data generation:
 3D Head Scan with High Resolution
 Retopology
 Defining Mesh Deformation
 Annotation

35. DATA GENERATION PIPELINE
High Resolution 3D Head Scan
• Capture accurate 3D face details using depth sensor
or multiple synchronized cameras using triangulation
• High density mesh (~0.1 mm resolution)
• Why can’t we use such high resolution scan?
• Needs lots of computation power to transform each
mesh vertex
• More manual efforts for defining deformation and
key shapes
http://ten24.info/10-x-high-resolution-head-scans-avaliable-to-download/
[2]

36. DATA GENERATION PIPELINE
Retopology
Reduce mesh vertices count to save on
computation cost
• Reduced vertex count (~1000 x reduction)
• Add displacement map to preserve details
• Easier to define shape keys manually on reduced mesh

37. DATA GENERATION PIPELINE
Mesh Deformation
• Define shape keys and set vertices manually
to define a face feature (e.g. eyes looking
down, smile, eyebrow raised)
• Intermediate values are interpolated
automatically
• Allows programmatic control of parameters
[1]

38. DATA GENERATION PIPELINE
Landmark annotation
• Manually mark points of interest
• Allows tracking and automatic labelling of face landmarks
• 2D and 3D point coordinates are available as the environment is synthetic
• Occluded points could be accepted/rejected programmatically
[1]

39. DATA GENERATION PIPELINE
Example annotated image
Following features are saved along with image in a file
• Face bounding box
• Face landmarks
• Head pose
• Gaze
• Eye lid, pupil(eyeball), iris markers
• Face ID

40. PARAMETER SETTING

41. PARAMETER SETTING
Head Pose control
• Position camera to cover head
• Camera oriented towards center of both eyes
• Head orientations can be set to captured values
to mimic human head motions

42. PARAMETER SETTING
Gaze control
• Position subject with respect to camera
• Set target gaze location
• Rotate eyes to look at target location
• Render cases which honor anatomic constraints
• Unobstructed ( Pupil is partly exposed > 35% )
• Within anatomically allowed pitch/yaw range
• Visible to the camera
Constraints

43. PARAMETER SETTING
Eye Openness
[1]

44. POST PROCESSING

45. POST PROCESSING
Domain adaption is used to operate on synthetic data to closely resemble real
data. Following techniques can be used to adopt to real world domain:
 Gaussian filtering/Blurring – This effect imitates focal blur of the camera
 Noise addition – Imitates sensor noise, environment noise/dust
 Brightness/contrast correction – To simulate different lighting conditions
 Scaling – To make up for face distance, bounding box tightness
 Mirroring – Most of the use cases are agnostic to mirroring; others can use
transformed parameters

46. DEPLOYMENT

47. DEPLOYMENT
40
8
0.8
0
5
10
15
20
25
30
35
40
45
CPU GPU GCF
Time(sec)
Device Type
Time per frame
1
5
50
0
10
20
30
40
50
60
CPU GPU GCF
Gainfactor
Device Type
Performance gain
Comparing performance on different devices
GCF: GPU Compute Farm (e.g. DGX)

48. DEPLOYMENT
Execution SEC/FRAME 150K FRAMES COMMENTS
CPU 40 70 days Consumes entire CPU
GPU 8 14 days 30-60% GPU(~5 sec/frame)
GCF (10 GPUs) 0.8 1.4 days
Training a DNN requires images in the order of ~150K and higher
Speed up of 5x with a GPU and (10x) times using GCF (GPU Compute Farm)

49. 目次
GTC2018でのNVIDIA自動運転関係・技術発表及び関連アナウンスメント （補
足スライド入り）
1. S8666: Deploying Autonomous Vehicles with NVIDIA DRIVE
2. S8531: Deep Learning Infrastructure for Autonomous Vehicles
3. S8294: NVIDIA DRIVE Safety: NVIDIA's Strategies for Enabling Safety in Automotive
Platforms
4. S8324: Synthetic Data Generation for an All-in-One Driver Monitoring System
5. ANNOUNCEMENT: NVIDIA and ARM Partner to Bring Deep Learning to Billions of IoT
Devices

50. NVIDIA AND ARM PARTNER TO BRING DEEP LEARNING
TO BILLIONS OF IOT DEVICES
NVIDIA Deep Learning Accelerator IP to be Integrated into ARM Project Trillium
Platform, Easing Building of Deep Learning IoT Chips
GPU Technology Conference — NVIDIA and Arm today announced that they are partnering to bring deep
learning inferencing to the billions of mobile, consumer electronics and Internet of Things devices that
will enter the global marketplace.
Under this partnership, NVIDIA and Arm will integrate the open-source NVIDIA Deep Learning
Accelerator (NVDLA) architecture into Arm’s Project Trillium platform for machine learning. The
collaboration will make it simple for IoT chip companies to integrate AI into their designs and help put
intelligent, affordable products into the hands of billions of consumers worldwide.
Tuesday, March 27, 2018

51. ANNOUNCING DRIVE XAVIER SAMPLING IN Q1
Most Complex SOC Ever Made | 9 Billion Transistors, 350mm2, 12nFFN | ~8,000 Engineering Years
Diversity of Engines Accelerate Entire AV Pipeline | Designed for ASIL-D AV
Volta GPU
FP32 / FP16 / INT8 Multi
Precision
512 CUDA Cores
1.3 CUDA TFLOPS
20 Tensor Core TOPS
ISP
1.5 GPIX/s
Native Full-range HDR
Tile-based Processing
PVA
1.6 TOPS
Stereo Disparity
Optical Flow
Image Processing
Video Processor
1.2 GPIX/s Encode
1.8 GPIX/s Decode
16 CSI
109 Gbps
1Gbps E & 10Gbps Eithernet
256-Bit LPDDR4
137 GB/s
DLA
5 TFLOPS FP16
10 TOPS INT8
Carmel ARM64 CPU
8 Cores
10-wide Superscalar
2700 SpecInt2000
Functional Safety
Features
Dual Execution Mode
Parity & ECC
World’s First
Autonomous
Machine
Processor

52. NVDLA (NVIDIA DEEP LEARNING ACCELERATOR)
XAVIER SOCにも内蔵
更なる電力効率向上
Command Interface
Tensor Execution Micro-controller
Memory Interface
Input DMA
(Activations and
Weights)
Unified
512KB
Input
Buffer
Activations
and
Weights
Sparse Weight
Decompre-
ssion
Native
Winograd
Input
Transform
MAC
Array
2048 Int8
or
1024 Int16
or
1024 FP16
Output
Accumu-
lators
Output Post
processor
(Activation
Function,
Pooling etc.)
Output
DMA
係数が疎になる性質を利用して
メモリバンド幅削減
極力乗算を減らす最新アルゴリズムで
チップサイズ、消費電力低減
期待される数々の最新技術例
他機能は理にかなった
合理的なアクセラレーション
Reference NVDLA: http://nvdla.org

53. なぜ AI 推論で演算精度・乗算回数を低減する必要があるか？
Artem Vasilyev, “CNN Optimization for Embedded Systems and FFT”
CS231n: CNN for Visual Recognition Course, Stanford, 2017
INT16では乗算は加算の
13倍の電力を消費
FPFF16では乗算は加算の
2.25倍の電力を消費
FP32では乗算は加算の
4.7倍の電力を消費
可能な限り演算精度を
下げる必要があることは
言うまでもない。
乗算回数低減はcuDNNライブラリや
DLA (Deep Learning
Accelerator) アーキテクチャで実施

54. CONFIGURABILITY

55. XAVIER サンプリング：Q4 2017
▪ 7 Billion Transistors
TSMC Custom 12nm FFN Process
▪ 8 Core Custom ARM64 CPU
▪ 512 Core Volta GPU 20TOPS DL
▪ Computer Vision Accelerator CVA
• including NVDLA (Deep Learning Accelerator)
• 2,048 8-bit MAC x 2, 5TOPS x 2 = 10 TOPS DL
• 1,024 FP16 or INT16 MAC x 2 too
• Automatically Optimized, Compiled by TensorRT3
Max 30TOPS DL
@ 30Watt
Reference: Microprocessor Report “Xavier Simplifies Self-Driving Cars” June 19, 2017

56. NEW NVIDIA TENSORRT 3
DRIVE PX 2
JETSON TX2
NVIDIA DLA
DLA: Deep Learning Accelerator
TESLA P4
TensorRT
TESLA V100
推論のプログラマブルな最適化環境
ニューラルネットの最適化
及びコンパイル
データセンター
自動運転
スーパーコンピュータ
組み込みIoT
SoC内蔵IoT
各ターゲット・プラットフォーム向け最適化全てのフレームワークをサポート
学習済の
ニューラルネット
最適化された
実行コード

58. 室河 徹
ソリューション アーキテクト (オートモーティブ)
エヌビディア
GTC 2018 で発表された
自動運転最新情報

59. SAN JOSE CONFERENCE CENTER

64. SJCC ENTRANCE

70. EXHIBITION HALL

90. AUTOMOTIVE SESSIONS

122. SIMULATION

138. AUTOMOTIVE SENSORS

154. 自動運転全般、アルゴリズム関連
• S81014 - Advancing State-of-the-Art of Autonomous Vehicles and Robotics
Research using AWS GPU Instances (Presented by Amazon Web Services)
• TRIによるAWSの活用事例、開発環境の紹介
• S8140 - Deep Learning for Automated Systems: From the Warehouse to the Road
• Clemson Univ. 自動運転ソフトウェア開発フロー
• S8862 - Autonomous Algorithms
• Panelセッション。NNAISENSE社によるAUDI自動駐車実験の紹介や、fka社による経路生成におけ
るDNNの活用など

155. マップ、ローカライゼーション関連
• S8834 - In-Vehicle Change Detection, Closing the Loop in the Car
• HERE – セルフヒーリングマップ、フリート用センサーの紹介
• S8861 - Crowd-sourcing, Map updates, and Predictions as Complementary
Solutions for Mapping
• Panel (explorer.ai, VoxelMaps, DeepMap) – クラウドソーシングによるマップ、
Voxelベースの地図、オープンプロジェクト等
• S8618 - GPU Accelerated LIDAR Based Localization for Automated Driving
Applications
• FORD – GPUによるローカライゼーションの高速化

156. • S8758 - The Future of the In-Car Experience
• Affectiva社による感情検出AIの紹介、学習データセットの事例や転移学習の成果について
• S8970 - Creating AI-Based Digital Companion for Mercedes-Benz Vehicles
• Mercedes Benzでの車載AIの実装
インキャビン関連

157. www.nvidia.com/dli