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ADAS/自動運転車の関連産業の分析、2018年 (I):コンピューティングプラットフォームとシステムアーキテクチャ

ADAS and Autonomous Driving Industry Chain Report 2018 (I) - Computing Platform and System Architecture

発行 ResearchInChina 商品コード 666530
出版日 ページ情報 英文 152 Pages
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ADAS/自動運転車の関連産業の分析、2018年 (I):コンピューティングプラットフォームとシステムアーキテクチャ ADAS and Autonomous Driving Industry Chain Report 2018 (I) - Computing Platform and System Architecture
出版日: 2018年07月25日 ページ情報: 英文 152 Pages
概要

中国のADAS/自動運転車市場は、2017年には59億人民元、2021年には426億人民元と、毎年67%のAAGR (年間成長率) で成長する見通しです。

当レポートでは、世界全体と中国国内における、ADAS (先進運転支援システム)/自動運転関連のプラットフォームやシステムアーキテクチャの整備状況について分析し、全体的な市場構造や動向見通し、主要自動車メーカーの開発戦略、現在のソフトウェア/ハードウェア構造、各種の安全基準の整備状況、主要企業での開発・整備状況と先行事例、といった情報を取りまとめてお届けいたします。

第1章 ADASおよび自動運転の概略

  • ADAS (高度運転支援システム) の分類と定義
  • 自動運転車の主要技術の定義
    • 環境認識技術:センサー認識からデータ融合へ
    • ポジショニング技術
    • 経路計画技術
    • 自動駐車技術
  • 自動運転のグレード (米国SAE (自動車技術者協議会) 基準)
  • 自動運転のグレード (中国国内基準)
  • ADAS/自動運転の規制と規格
    • ウィーン条約 (道路交通に関する条約 (1968年)) の変更による、自動運転の実現
    • 自動運転試験に関する規制
    • EU:11項目の自動車安全システムの義務化 (2021年から)
  • 自動運転の典型的なフレームワーク
    • ステップ1:測位 (ポジショニング)
    • ステップ2:認識
    • ステップ3:交通量のシナリオ予測
    • ステップ4:意思決定
    • ステップ5:行動計画
    • ステップ6:実行

第2章 市場規模とその予測

  • 世界の自動運転車の販売台数の予測 (2015〜2050年)
  • 世界のADAS市場の年間平均成長率 (AAGR) の予測 (2017〜2025年)
  • Veoneer:アクティブセーフティの市場規模が300億米ドルに達する (2025年)
  • 中国のADAS/自動運転車の市場規模 (2016〜2021年)
  • 国内のADAS設置ずみ乗用車の累計台数 (2017年):ACC・FCW・LKSが最も急速に拡大

第3章 自動車メーカーのADAS/自動運転向け戦略

  • Geely
  • GM Intelligent Driving
  • 日産自動車・BMW・Xpeng:Mobileye Route
  • BMW:L3 CO-PILOTの大量生産計画 (2021年)
  • Bosch:Chang'an・FAW・NIO・SAICとの提携計画
  • Bosch:自動運転ソリューション
    • Boschのドメインコントローラー
    • TJP (トラフィックジャム・パイロット) ソリューション
    • センサーソリューション
    • HDマップ (高精度地図) ソリューション
  • AptivとGreat Wall Motorとの提携
  • デンソー:GACの吸収統合
  • Hyundai:レベル4無人走行車用センサーのレイアウト
  • Ford:ハイビームLiDARを中核センサーとして利用
  • BYTONとAuroraの事業協力

第4章 ADAS/自動運転車のソフトウェア・アーキテクチャ

  • ADAS/自動運転システムの中核要素
  • Autosarの概略
    • ロードマップ
    • 主要メンバー
    • 古典的バージョンと適応的バージョン
    • 古典的バージョンの構造
    • 適応的バージョンのソフトウェアの層別化:古典的/適応的バージョンの比較
    • 適応的バージョンのロードマップ
  • ROS:自動運転用OS
    • 一部の自動車メーカーが認証しているROS
    • ROS (ロボット・オペレーティング・システム) の概略
    • ROS2.0:近日中の実現化
    • ROSの転換
    • ROSのセキュリティ
  • QNX ADAS 2.0:最高のASIL Dレベルを獲得
    • QNX ADAS 2.0対応のスコープ

第5章 ADAS/自動運転のハードウェア・アーキテクチャ

  • 典型的な自動車用ネットワーク・アーキテクチャ
  • セントラル・ゲートウェイからドメインコントローラー構造 (NXP) へ
  • 未来の自動車向け電気・電子機器アーキテクチャ (Bosch)
  • なぜ、ドメインコントローラーを利用するのか
    • 現在/将来の自動車用電子機器のアーキテクチャ
    • ドメインコントローラーによるハードウェア・リソースの共有:OS・基本ソフトウェアとの共有の実現化
    • I/Oアーキテクチャとドメインコントローラー
    • ドメインコントローラーの土台:自動車用イーサネット、自動車用バスとの比較
  • 自動車用イーサネット
    • 自動車用イーサネットのプロトタイプ:EAVB
    • EAVBの次の段階:TSN
    • TSNネットワーク
    • TSNイーサネットスイッチ:未来の自動運転用コンピューティングシステムの中核
  • Waymoが利用しているコンピュータシステム・アーキテクチャ
  • NVIDIA PX2:アーキテクチャ
  • NXP S32G:ゲートウェイ
  • ルネサス エレクトロニクス:L4コンピューティング・プラットフォームの構造

第6章 ADAS/自動運転の安全認証基準

  • 政府の自動車規格に即した。チップの認証制度
  • AECの認証
  • ISO 26262:機能的安全性とASIL (安全性要求レベル)
  • ISO 26262のプロセス
  • 各種の安全レベルに応じた、別々の判断基準の必要性
  • 自動運転用ECUの典型的構造:モデルパートはBレベルまで、計画パートはDレベルまで到達

第7章 ADAS用プロセッサのベンダー

  • ADAS/自動運転用プロセッサの業界
  • ARM
  • NXP
  • ルネサス エレクトロニクス
  • Nvidia
  • Ambarella
  • Mobileye
  • TDA Series of Texas Instruments
  • Infineon
目次

ADAS and Autonomous Driving Industry Chain Report 2018 (I) - Computing Platform and System Architecture underscores the followings:

  • Introduction to ADAS and autonomous driving;
  • ADAS and autonomous driving market forecast;
  • ADAS and autonomous driving strategy of carmakers including Geely, GM, SAIC, Dongfeng, Great Wall, GAC, Chang'an, NIO, Xpeng and BYTON;
  • Software architecture of ADAS and autonomous driving, including AUTOSAR Classic and Adaptive, ROS 2.0 and QNX;
  • Hardware architecture of ADAS and autonomous driving, including automotive Ethernet, TSN, Ethernet switch and gateway, and domain controller;
  • Safety certification of ADAS and autonomous driving, including ISO26262 and AEC-Q100;
  • Study into processor firms, including NXP, Renesas, Texas Instruments, Mobileye, Nvidia, Ambarella, Infineon and ARM.

According to ResearchInChina, the Chinese ADAS and autonomous driving market was worth about RMB5.9 billion in 2017 and is expected to reach RMB42.6 billion in 2021 at an AAGR of 67% or so.

Automotive vision, MMW radar and ADAS are the market segments that develop first with the MMW radar market enjoying an impressive growth rate, closely followed by low-speed autonomous driving. While LiDAR, commercial-vehicle autonomous driving and passenger-car autonomous driving markets lag behind.

As the automobile enters an era of ADAS and autonomous driving, product iteration races up and lifecycle of products is shortened. The automotive market is far smaller than consumer electronics market but sees bigger difficulty in design and higher design and production costs than that in consumer electronics market. Thus automotive ADAS and autonomous driving processor is confronted with higher risks. Hence adequate financial and human resources are required to support the development of automotive ADAS and autonomous driving processors. Globally, only very a few enterprises like NXP and Renesas are capable of developing whole series of ADAS and autonomous driving processors.

With regard to safety certification, autonomous driving chips must attain ASIL B at least, a level only Renesas R-CAR H3 has reached for now. As GPU is a universal design and not car-dedicated design, it is hard to reach the certified safety level of ISO26262 from the point of design. The certification cycle of ASIL is up to two to four years.

Reliability, precision and functionality of stereo camera are well above that of mono camera, but as the stereo camera must use FPGA, it costs much. High costs restraint the application of the stereo camera only on luxury cars. However, with emergence of Renesas and NXP hardcore stereo processors, the stereo camera will be vastly used in ADAS and autonomous driving field, expanding from luxury models to mid-range ones.

With an explosive growth in data transmission, automotive Ethernet will become a standard configuration of the automobile, and Ethernet gateway or Ethernet switch is indispensable to autonomous driving.

Autosar will act as a standard configuration in ADAS and autonomous driving field.

CNN/DNN graphics machine leaning: GPU is most suitable when data is irrelevant to sequence. Nvidia GPU can be used in multiple fields except for automobile and finds shipments far higher than that of automotive ASIC, enjoying superiority in cost performance. TPU lifts speed and reduces power consumption (only 10% of that of GPU) at the expense of the precision of computation.

RNN/LSTM/reinforcement learning sequence-related machine learning: FPGA has distinct advantages, particularly in power consumption, consuming less than one-fifth of GPU under same performance. However, high-performance FPGA is incredibly costly. FPGA can also process graphics machine leaning and improve performance by reducing precision.

ASIC stands out by performance-to-power consumption ratio but has shortcomings of long development cycle, the highest development cost and the poorest flexibility. The unit price will be very high or firms will make losses if the shipments are small (at least annual shipments of 120 million units if 7-nanometer process is employed). Most ASICs for deep-learning graphics machine learning are similar to TPU.

Power consumption and cost performance are crucial in in-vehicle field. GPU is no doubt a winner in graphic machine learning. However, as algorithms are constantly improved, the ever low requirements on the precision of computation, and low power consumption will ensure a place of FPGA in graphics machine learning. FPGA has overwhelming advantages in sequence machine learning.

Autonomous driving can be divided into two types, one represented by Waymo, which has solved most of the problems concerning environmental perception and concentrates on behavior decision-making with computing architecture of CPU+FPGA (usually Intel Xeon 12-core and above CPU plus Altera or Xilinx's FPGA; the other represented by Mobileye which has not solved all problems involving environmental perception and concentrates on it with computing architecture of CPU+GPU/ASIC.

CPU+GPU will be the mainstream in the short run, but CPU+FPGA/ASIC may dominate in the long term, largely due to continuous decline in the precision of computation of graphics because of improvement in algorithms and performance of sensors (LiDAR in particular), which is conducive to FPGA, while it is hardly for the power consumption of GPU to fall. It is easier for FPGA to meet car-grade requirements.

In chip contract manufacturing field, TSMC has won all 7-nanometer chip orders, including A12 exclusively provided for Apple, marking for the first time TSMC overtook Intel to become the vendor with the most advanced semiconductor manufacturing process, a must in the production of digital logic chip whose computing capability is underlined in AI autonomous driving.

Table of Contents

1. Introduction to ADAS and Autonomous Driving

  • 1.1. Definition and Classification of ADAS
  • Main Functions of ADAS
  • 1.2. Definition and Key Technologies of Autonomous Vehicle
    • 1.2.1. Environmental Perception Technology: from Sensor Perception to Data Fusion
  • Environmental Perception Technology: Different Sensors Have Different Advantages
    • 1.2.2. Positioning Technology
    • 1.2.3. Path Planning Technology
    • 1.2.4. Automatic Parking Technology
  • 1.3. Grading of Autonomous Driving (SAE)
  • 1.4. Grading of Autonomous Driving (China)
  • 1.5. Regulations on and Standards for ADAS and Autonomous Driving
    • 1.5.1. Amendment to the 1968 Vienna Convention on Road Traffic Allows Autonomous Driving
    • 1.5.2. Regulations on Autonomous Driving Tests
    • 1.5.3. EU Lists 11 Automotive Safety Systems to Become Mandatory from 2021
  • 1.6. Typical Framework of Autonomous Driving
    • 1.6.1. First Step, Positioning
  • HD Map and V2X
    • 1.6.2. Step 2, Perception
  • 3D Bounding with Route Fusion
    • 1.6.3. Step 3: Traffic Scenario Forecast
  • Forecast Includes Scenario Understanding
    • 1.6.4. Step 4: Decision-making
  • Lane Overall Planning
  • Shorter Routes May Be Not Better.
  • Behavior Planning Is the Most Difficult
  • There Are Many Behavior Planning Algorithms, Mostly Immature
    • 1.6.5. Step 5: Action Planning
    • 1.6.6. Step 6: Execution

2. Market Size and Forecast

  • 2.1. Global Sales Volume of Autonomous Vehicles, 2015-2050E
  • 2.2. AAGR of Global ADAS Market, 2017-2025E
  • 2.3. Veoneer: Active Safety Market Is Expected to Reach USD30 Billion by 2025
  • 2.4. Chinese ADAS and Autonomous Driving System Market Size, 2016-2021E
  • 2.5. Concurrent Comparison of Domestic Passenger Car ADAS Cumulative Installations in 2017: ACC, FCW and LKS Saw the Fastest Growth Rate

3. Carmakers' ADAS and Autonomous Driving Strategies

  • 3.1. Geely
  • 3.2. GM Intelligent Driving
  • 3.3. Mobileye Route of Nissan, BMW and Xpeng
  • 3.4. BMW Plans to Mass-produce L3 CO-PILOT in 2021.
  • Intel's Driverless Cars Use 32-beam LiDAR
  • 3.5. Bosch Route of Chang'an, FAW, NIO and SAIC
  • 3.6. Bosch's Autonomous Driving Solutions
    • 3.6.1. Bosch's Domain Controllers
  • Comparison between Various Domain Controllers
    • 3.6.2. TJP Solutions
    • 3.6.3. Sensor Solutions
    • 3.6.4. HD Map Solutions
    • 3.6.5. Planning for Commercial Vehicle Autonomous Driving
  • 3.7. Aptiv Route of Great Wall
  • Aptiv's Road Model Relies on LiDAR
  • 3.8. Denso Route of GAC
  • 3.9. Layout of Hyundai L4 Driverless Car Sensors
  • 3.10. Ford Uses High-beam LiDAR as the Core Sensor
  • 3.11. BYTON Collaborates with Aurora

4. Software Architecture of ADAS and Autonomous Driving

  • 4.1. Core Elements of ADAS and Autonomous Driving System
  • 4.2. Introduction to Autosar
    • 4.2.1. Roadmap
    • 4.2.2. Main Members
    • 4.2.3. Classic Version and Adaptive Version
    • 4.2.4. Architecture of Classic Version
    • 4.2.5. Software Stratification of Adaptive Version; Comparison between Classic Version and Adaptive Version
    • 4.2.6. Roadmap of Adaptive Version
  • 4.3. ROS: an Autonomous Driving Operating System
    • 4.3.1. ROS Recognized by Some Carmakers
    • 4.3.2. Introduction to ROS
    • 4.3.3. ROS2.0 Is Close to Real Time
    • 4.3.3. Transformation of ROS
    • 4.3.4. Security of ROS
  • 4.4. QNX ADAS 2.0 Achieves the Highest ASIL D Level
    • 4.4.1. Scope Supported by QNX ADAS 2.0

5. Hardware Architecture of ADAS and Autonomous Driving

  • 5.1. Typical Automotive Network Architecture
  • 5.2. From the Central Gateway to the Domain Controller Structure (NXP)
  • 5.3. Future Automotive Electronic and Electrical Architecture (Bosch)
  • 5.4. Why Use A Domain Controller
    • 5.4.1. Current and Future Automotive Electronic Architecture
    • 5.4.2. Domain Controllers Share Hardware Resources, so that Operating System and Basic Software Realize Sharing
    • 5.4.3. I/O Architecture and Domain Controller
    • 5.4.4. Basis of Domain Controller: Automotive Ethernet, Automotive Bus Comparison
  • Automotive Bus Comparison
  • 5.5. Automotive Ethernet
    • 5.5.1. Prototype of Automotive Ethernet: EAVB
    • 5.5.2. The Next Step of EAVB: TSN
    • 5.5.3. TSN Network
    • 5.5.4. TSN Ethernet Switch Is the Core of the Future Autonomous Driving Computing System
  • 5.6. The Computing System Architecture Used by Waymo
  • 5.7. NVIDIA PX2: Architecture
  • 5.8. NXP S32G: Gateway
    • 5.8.1. Architecture of NXP Autonomous Driving Blue Box
    • 5.8.2. Gateway and Ethernet Switch
  • 5.9. Architecture of Renesas L4 Computing Platform
    • 5.9.1. Renesas' Vision of the Future Automotive Electronic Architecture

6. Safety Certification of ADAS and Autonomous Driving

  • 6.1. Chip Certification in Line with National Automotive Standards
  • 6.2. AEC Certification
  • 6.3. ISO26262, Functional Safety and ASIL
  • 6.4. ISO26262 Process
  • 6.5. Different Safety Levels Require Different Judgmental Independence
  • 6.6. Typical Structure of Autonomous Driving ECU; the Model Part Reaches the B Level; the Planning Part Reaches the D Level

7. ADAS Processor Vendors

  • 7.1. ADAS and Autonomous Driving Processor Industry
    • 7.1.1. FPGA/GPU/ASIC/CPU/TPU and Machine Learning
    • 7.1.2. Soft/Solid/Hard Core
    • 7.1.3. Solid Core Is the Mainstream
    • 7.1.4. Architecture of Typical L4 Computing System
  • 7.2. ARM
    • 7.2.1. Application Structure of ARM Autonomous Vehicles
    • 7.2.2. Autonomous Driving SoC Design Recommended by ARM
    • 7.2.3. ARM A Series
    • 7.2.4. ARM R Series and M Series
  • 7.3. NXP
    • 7.3.1. NXP Autonomous Driving CPU Roadmap
    • 7.3.2. Roadmap of NXP's ADAS and Autonomous Driving Vision Processing Chip
    • 7.3.3. Introduction to NXP S32V3
    • 7.3.4. NXP S32V3 Vision Processing System
    • 7.3.5. Framework Diagram of NXP ADAS Chassis Control MCU MPC5746R
    • 7.3.6. NXP Autonomous Driving Chassis Control MCU: S32D/S Series
  • 7.4. Renesas
    • 7.4.1. Renesas R-CAR H3
    • 7.4.2. Renesas R-CAR V3H
    • 7.4.3. Renesas RH850/P1H-C
  • MCU with the Highest Safety Level Designed for Chassis Control
    • 7.4.4. Renesas Cooperates with Dibotics to Develop LiDAR Applications
    • 7.4.5. Renesas Partners with USHR in HD Map
    • 7.4.6. Renesas Teams up with QNX and University of Waterloo in Operating System
    • 7.4.7. Renesas Collaborates with Leddartech on LiDAR
    • 7.4.8. Renesas' Cooperation in Autonomous Driving
  • 7.5. Nvidia
    • 7.5.1. Parameters of Nvidia DRIVE Series Products
    • 7.5.2. Circuit Schematic Diagram of PX2
    • 7.5.3. Nvidia DRIVE Xavier
    • 7.5.4. Nvidia DRIVE Pegasus
  • 7.6. Ambarella
    • 7.6.1. Technology Distribution and Roadmap
    • 7.6.2. Core Technology CVflov and Stereo-camera Data Processing Hard Core
    • 7.6.3. Ambarella CV2AQ
    • 7.6.4. Ambarella CV2AQ
  • 7.7. Mobileye
    • 7.7.1. Internal Framework Diagram of Mobileye Eyeq4/5
    • 7.7.2. Dual-EYEQ4 L3 Solutions (HiRain Technologies)
  • 7.8. TDA Series of Texas Instruments
    • 7.8.1. Introduction to TDA2 Series
    • 7.8.2. TDA4 and TIDL
    • 7.8.3. Single-chip MMW Radar Solutions
  • 7.9. Infineon
    • 7.9.1. MEMS LiDAR Solutions
    • 7.9.2. MMW Radar Transceivers
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