車載用ADAS/自動運転プラットフォームDRIVE PX 及びコックピト・プラットフォームDRIVE CXのご紹介 シニア・ソリューションアーキテクト 馬路 徹, 2015年9月18日 Autonomous Driving System Architecture and DRIVE PX/CX Implementations Agenda DRIVE PX for Map Module, AI Module and Computer Vision DRIVE CX for HMI Module Summary 2 Autonomous Driving System Architecture Typical Architecture 白線距離 走行環境センシングおよび障害物認識 - 前方の障害物センシング（ミリ波レーダ、レーザレーダ、カメラ） レーンマーカセンシング エンジン・ブレーキ 速度制御モジュール 障害物 位置等 修正 - Adaptive Cruise Control 指示 - Pre-Crush System 地図モジュール 測位 GPS 車間 距離 - 固定道路地図 - ローカルダイナミックマップ - 目標走行軌跡生成 人工知能モジュール - 環境理解 - 判断 - 目標走行軌跡修正 交通情報等 ビッグデータ、道路・交通情報等（車外データ） 参考文献：「自動運転 システム構成と要素技術」、保坂明夫、青木啓二、津川定之 ハンドル 操舵制御モジュール （車線維持制御） 道路線形 道路地図 修正 指示 HMI モジュール -手動、自動切換え操作システム - 稼動状況表示 森北出版、2015年7月 Reference: “Automated Driving System and Technologies”, Akio Hosaka et al, Morikita Publishing Co., Ltd., July 2015 3 Autonomous Driving System Architecture Typical Architecture Driving Environment Sensing and Obstacle Recognition - Front Obstacles Sensing (Mili-wave Radar, Laser Radar, Camera) Lane Marker Sensing Obstacle Location Position Sensing GPS Lane Distance Car Distance Adjusting Acceleration MAP MODULE - Road Map Local Dynamic Map Target Path Generation AI MODULE - SPEED CONTROL MODULE - Direction Traffic Information STEERING CONTROL MODULE - Lane Keep Control HMI MODULE Big Data, Road, Traffic Information etc 参考文献：「自動運転 システム構成と要素技術」、保坂明夫、青木啓二、津川定之 Adaptive Cruise Control Pre-Crush System Steering Environment Recognition Decision Making Target Path Tuning Adjusting Road Structure Road Map Engine, Break - Auto/Manual Mode SW Operation - System Operation Status 森北出版、2015年7月 Reference: “Automated Driving System and Technologies”, Akio Hosaka et al, Morikita Publishing Co., Ltd., July 2015 4 Autonomous Driving System Architecture MAP MODULE implementation by DRIVE PX/CUDA Driving Environment Sensing and Obstacle Recognition - Front Obstacles Sensing (Mili-wave Radar, Laser Radar, Camera) Lane Marker Sensing Obstacle Location DRIVE PX / CUDA - Road Map Local Dynamic Map Target Path Generation AI MODULE - Engine, Break SPEED CONTROL MODULE - Direction Traffic Information STEERING CONTROL MODULE - Lane Keep Control HMI MODULE Big Data, Road, Traffic Information etc 参考文献：「自動運転 システム構成と要素技術」、保坂明夫、青木啓二、津川定之 Adaptive Cruise Control Pre-Crush System Steering Environment Recognition Decision Making Target Path Tuning Adjusting Road Structure Road Map Car Distance Adjusting Acceleration MAP MODULE Position Sensing GPS Lane Distance - Auto/Manual Mode SW Operation - System Operation Status 森北出版、2015年7月 Reference: “Automated Driving System and Technologies”, Akio Hosaka et al, Morikita Publishing Co., Ltd., July 2015 5 Autonomous Driving System Architecture + AI MODULE implementation by DRIVE PX/DL Driving Environment Sensing and Obstacle Recognition - Front Obstacles Sensing (Mili-wave Radar, Laser Radar, Camera) Lane Marker Sensing Obstacle Location DRIVE PX / DL DRIVE PX / CUDA MAP MODULE Position Sensing GPS - Road Map Local Dynamic Map Target Path Generation Car Distance Adjusting Acceleration Engine, Break SPEED CONTROL MODULE - Adaptive Cruise Control Pre-Crush System AI MODULE - Environment Recognition Decision Making Target Path Tuning Road Structure Road Map Lane Distance Steering Adjusting Direction Traffic Information - Lane Keep Control HMI MODULE Big Data, Road, Traffic Information etc 参考文献：「自動運転 システム構成と要素技術」、保坂明夫、青木啓二、津川定之 STEERING CONTROL MODULE - Auto/Manual Mode SW Operation - System Operation Status 森北出版、2015年7月 Reference: “Automated Driving System and Technologies”, Akio Hosaka et al, Morikita Publishing Co., Ltd., July 2015 6 Autonomous Driving System Architecture + HMI MODULE Implementation by DRIVE CX/HMI Driving Environment Sensing and Obstacle Recognition - Front Obstacles Sensing (Mili-wave Radar, Laser Radar, Camera) Lane Marker Sensing Obstacle Location DRIVE PX / DL DRIVE PX / CUDA MAP MODULE Position Sensing GPS - Road Map Local Dynamic Map Target Path Generation Car Distance Adjusting Acceleration Engine, Break SPEED CONTROL MODULE - Adaptive Cruise Control Pre-Crush System AI MODULE - Environment Recognition Decision Making Target Path Tuning Road Structure Road Map Lane Distance Steering Adjusting Direction Traffic Information - Lane Keep Control HMI MODULE Big Data, Road, Traffic Information etc 参考文献：「自動運転 システム構成と要素技術」、保坂明夫、青木啓二、津川定之 STEERING CONTROL MODULE 森北出版、2015年7月 Reference: “Automated Driving System and Technologies”, Akio Hosaka et al, Morikita Publishing Co., Ltd., July 2015 - Auto/Manual Mode SW Operation - System Operation Status DRIVE CX/HMI7 Autonomous Driving System Architecture + Computer Vision Processing by DRIVE PX/DL & CV -> Almost All Processings by Tegra DRIVE PX / DL & CV Computer Vision Lane Distance Deep Learning VisionWorks Obstacle Location DRIVE PX / DL DRIVE PX / CUDA MAP MODULE Position Sensing GPS - Road Map Local Dynamic Map Target Path Generation Car Distance SPEED CONTROL MODULE - Adaptive Cruise Control Pre-Crush System AI MODULE - Environment Recognition Decision Making Target Path Tuning Road Structure Road Map Adjusting Acceleration Engine, Break Steering Adjusting Direction Traffic Information - Lane Keep Control HMI MODULE Big Data, Road, Traffic Information etc 参考文献：「自動運転 システム構成と要素技術」、保坂明夫、青木啓二、津川定之 STEERING CONTROL MODULE 森北出版、2015年7月 Reference: “Automated Driving System and Technologies”, Akio Hosaka et al, Morikita Publishing Co., Ltd., July 2015 - Auto/Manual Mode SW Operation - System Operation Status DRIVE CX/HMI8 9 Audi zFAS Example as a Low-Speed Autonomous Driving: Obstacle Recognition, Target Path Generation by one Tegar K1 From GTC2015 10 Deep Learning Revolutionize Computer Vision Required Separate Algorithms/Apps - Pedestrian: HOG etc - Traffic Sign: Hough Transform + Character Recog. etc Only simple context recognition - Pedestrian Y/N Only (no additional info) - Speed Limit Signs Only One Deep Neural Net App can Detect various Objects - Pedestrian, Cars, Traffic Signs, lanes - Also with many attributes (Car: Police Car, Van, Sedan, Truck, Ambulance….) DEEP NEURAL NETWORK CONVENTIONAL (…) 11 TEGRA X1 CLASSIFICATION Performance IMAGES / SECOND AlexNet 100 90 80 70 60 50 40 30 20 10 0 Tegra K1 Tegra X1 12 13 Growing Performance of Automotive Tegra Products will allow further Integration in the Future Tegra X1 1200 Tegra X1 (FP16) Core i7 1000 GPU GPU CPU 800 CPU GFLOPS FP16/INT16 600 400 Tegra K1 200 Tegra 4 Tegra 2 Tegra 3 0 TIME Note: 4790K Core i7, CPU @ 4GHz,14 GPU @ 350 MHz DRIVE PX For Map Module, AI Module and Computer Vision 15 An advanced computing platform based on NVIDIA Tegra processors for autonomous driving cars FEATURES DRIVE PX The ability to capture and process multiple HD camera and sensor inputs A rich middleware for computer graphics, computer vision and deep learning A powerful and easy to develop platform for algorithm research and rapid prototyping NVIDIA CONFIDENTIAL — DRIVE PX DEVELOPMENT PLATFORM 16 Preliminary information — Subject to change DRIVE PX Camera & Display Interfaces 12 simultaneous LVDS camera inputs • All cameras synchronized within each Group (3 groups) 2 LVDS display ports Group A Group B Group C Display 17 & Confidential Proprietary All Information Subject to Change Other Interfaces to Aurix CAN*, LIN*, FlexRay* and Ethernet Ethernet (x1) 1x Power UART (x1) FlexRay (x2) LIN (x4) 48-pin Automotive Grade Vehicle Harness CAN 2.0 (x6) 18 Dual Tegra X1 VCM; each VCM consists of: Tegra X1 processor DRAM: 4GB NOR FLASH: 64MB Hardware Specs PROCESSORS eMMC: 64GB Inter-Tegra X1 VCM Communication SPI and USB 3.0 for direct inter-Tegra communication and through Ethernet Switch ASIL-D MCU Camera and IO controls through ASIL-D MCU. NVIDIA CONFIDENTIAL — DRIVE PX DEVELOPMENT PLATFORM 19 Preliminary information — Subject to change Sensors: Vision Sensors interface: 12x LVDS Cameras Hardware Specs PERIPHERALS Sensor Interfaces for Radar, LIDAR, Vehicle Dynamics etc.: CAN 2.0; LIN; Ethernet; Flexray Displays: LVDS interface (x2) Power Management of ECU: System power monitor/control — ASIL-D MCU NVIDIA CONFIDENTIAL — DRIVE PX DEVELOPMENT PLATFORM 20 Preliminary information — Subject to change OS: NVIDIA Vibrante Linux 4.0 64-bit Kernel Linux, Quickboot, AutoSAR RunTimeEnvironment Graphics: Open GL ES 3.1 Development Tools/Samples: Delivered through Jetpack 2.x DRIVE PX software specs Graphics debugger, PerfKit, DNN Classifier Sample, Vision Works 1.0 (beta) Computer Vision libraries and Samples ASIL MCU Support for CAN, Ethernet, Flexray and LIN; AutoSAR framework External Storage for Video Recording USB3.0 interface for camera output in RAW or H.265/H.264 encoded formats Camera: NVMedia and Driver support for LVDS camera Open Source Collaboration initiatives/Compliance: Yocto 1.8 Genivi7 Compliant NVIDIA CONFIDENTIAL — DRIVE PX DEVELOPMENT PLATFORM 21 Preliminary information — Subject to change WORLD CLASS SOFTWARE TOOLS Faster debug and analysis reduces development costs TEGRA GRAPHICS DEBUGGER PERFKIT ECLIPSE IDE Visualize GPU performance metrics Performance monitoring Standard Linux development environment Automated analysis of GPU bottlenecks Automated bottleneck analysis 22 Preliminary information — Subject to change DRIVE PX LINUX SOFTWARE STACK Performance Microprocessor A Performance Microprocessor B Safety MCU Applications Applications Applications Graphics/Compute Graphics/Compute CV/DL Libraries NVMedia Filesystem(s) Linux BSP/Drivers Imaging (Camera) Pipeline Graphics/Compute AUTOSAR BSW on Linux AUTOSAR on Safety MCU CUDA/EGL/ Open GL ES AUTOSAR BSW on Linux MCA L CV/DL Libraries Graphics/Compute CUDA/EGL/ Open GL ES Linux Imaging (Camera) Pipeline Safety MCU OS/3rd SW/HW Elektrobit NVIDIA Licensed SW Filesystem(s) Linux BSP/Drivers Linux Tegra™ X1 Hardware (ARM, GPU & SoC Peripherals) T1/OEM SW NVMedia Tegra™ X1 Hardware (ARM, GPU & SoC Peripherals) Drive PX Hardware 23 Preliminary information — Subject to change DRIVE CX For HMI MODULE 24 THE SOUL OF NVIDIA DRIVE™ CX DIGITAL COCKPIT CAR COMPUTER Natural Speech OTA updates Advanced Visuals Hypervisor – Cluster Cockpit NVIDIA CONFIDENTIAL 25 ADVANCED VISUALS – Digital CLUSTER TODAY DRIVE CX 26 DRIVE CX ADAS Also supported by DRIVE PX Best-in-Class Surround View 27 NVIDIA DRIVE Design NVIDIA’s HMI Platform version 8.0 Design Studio Professional artist environment Design Architect Integrated engineering environment 28 Fail-safe NATURAL LANGUAGE SPEECH ACCURACY VOCABULARY SPEED Today (no internet connection) Google (with Internet Connection) DRIVE CX (no internet connection) LOW HIGH HIGH 1M parameters 30M parameters 30M parameters SMALL LARGE LARGE 50k words 4M words 4M words FAST … or no response (lost internet connection) FAST … always SLOW 500+ ms latency 29 SUMMARY 1. Autonomous Driving System Architecture consists of Sensing Module, Map Module, AI Module and HMI Module. DRIVE PX and CX can implement all functions with CUDA, Deep Learning , Computer Vision and HMI Frameworks. 2. DRIVE PX consists of two powerful Tegra X1 processors with the total performance of 2.3TFLOPS. It comes with a rich middleware for GPU Computing, Deep Learning and Computer Vision. 3. DRIVE CX powerful Tegra X1 processor enables the fail-safe Natural Speech Recognition, advanced visual quality which offers a safe, versatile and highquality HMI. This is essential for the critical human-car interaction in the Autonomous Driving Cars. 4. Today, we might start with a few DRIVE PX and a DRIVE CX. However, the continuous performance and feature enhancement in the future will make it possible to implement the total system by a single DRIVE platform if required. 30 THANK YOU
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