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ADAS及AD系統用的感測器資料融合策略:北美(NA)、EU市場預測:∼2025年

NA and EU Sensor Data Fusion Strategies for ADAS and AD Systems, Forecast to 2025

出版商 Frost & Sullivan 商品編碼 920287
出版日期 內容資訊 英文 66 Pages
商品交期: 最快1-2個工作天內
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ADAS及AD系統用的感測器資料融合策略:北美(NA)、EU市場預測:∼2025年 NA and EU Sensor Data Fusion Strategies for ADAS and AD Systems, Forecast to 2025
出版日期: 2019年12月17日內容資訊: 英文 66 Pages
簡介

由於自動化的發展及採用駕駛支援系統的情形增加,產生了完整且正確探測、認識汽車週遭環境的必要性。由於先進的感測器資料融合,OEM及Tier1供應商在為汽車周邊環境提供高度冗餘與信賴的同時,也獲得了以低成本提供數個ADAS應用的機會。現在汽車中的連網、自動駕駛、汽車共用及服務(CASE)的進化,使得OEM需要從頭重新設計產品開發。OEM透過開發新的車輛平台或改變既有平台設計,變更內部配線與通訊協定,並支援電動動力總成、連線功能及既有自動駕駛應用程式。

本報告聚焦於北美(NA)及EU地區的ADAS、AD系統用感測器資料融合策略,提供今後預測,並針對主要感測器資料融合策略及其對E/E(電力/電子)電腦系統結構設計要素的影響,以及為了達成L3以上的自律等級的AD軟體調集、影響市場佈局的主要動向、使用案例等進行了分析。

執行摘要

調查範圍、目的

感測器套組的進化

  • 技術的進化:雷達模組
  • 技術的進化:LiDAR模組
  • 感測器條件隨著自動化層級而增加
  • 各感測器的表現能力
  • 在AD的感測器融合需求

感測器資料融合策略

  • 策略1:分散型系統結構
  • 策略2:集中型系統結構
  • 策略3:混合系統結構
  • 感測器資料融合表現能力
  • 感測器資料融合的功用:SAE自動化層級別

感測器資料融合市場的動向與分析

  • ADAS感測器資料融合的市場滲透
  • 基於車輛部門的最佳感測器資料融合策略

業界整體的最佳實踐:感測器融合供應商的方法

  • 使用案例:AEye與Cartica
  • 使用案例:Continental的LiDAR與相機的融合
  • 使用案例:Valeo及Mobileye

業界整體的最佳實踐:感測器融合OEM方法

  • Tesla Autopilot
  • Audi AI

感測器資料融合對主要車輛的影響

  • 感測器資料融合策略對車輛技術的影響
  • 用於自動駕駛的汽車E/E(電力/電子)電腦系統結構的拓樸學

E/E電腦系統結構的進化

  • 既有E/E電腦系統結構的課題
  • E/E電腦系統結構的進化
  • 汽車E/E電腦系統結構策略

業界整體的最佳實踐:E/E電腦系統結構OEM方法

  • GM的數位載體平台
  • Volkswagen:數位轉換

業界整體的最佳實踐:E/E電腦系統結構供應商的方法

  • Aptiv:Smart Vehicle Architecture(SVA)

AD採購軟體策略

  • 實現AD的主要車載軟體
  • ADAS/AD採購軟體的商業模式
  • AD軟體平台
  • 價值鏈:AD軟體及科技

成長機會及C2A (建議行動)

  • 成長機會:來自OEM / TSP的投資、合作
  • 為了成功與成長的策略性必要行動

主要結論

附錄

目錄
Product Code: MEE3-18

Strategies Influencing Key Design Elements of E/E Architecture and Sourcing AD Software to Achieve L3 and Above Autonomy

The evolution in autonomy and increase in the adoption of driver assistance systems have generated a need to sense and perceive the surrounding environment of a vehicle entirely and accurately. The fusion of forward-looking sensor data has given OEMs and Tier-I suppliers opportunities to offer multiple ADAS applications at low costs, while providing high redundancy and reliability in perceiving vehicle surroundings.

The use of multiple sensors and sensor data fusion and an increase in the number of ADAS applications have elevated the amount of in-vehicle data exchange to a few gigabytes, which is expected to rise further, as the level of vehicle autonomy goes up. This will generate the need to increase the speed of data transfers within the vehicle communication network and the use of high-powered control and processing units, which will, in turn, increase the complexity of the vehicle E/E architecture.

The evolution of Connected Autonomous Shared and Electric (CASE) in today's vehicles has urged OEMs to redesign product development ground up. OEMs are developing new vehicle platforms or changing the design of the existing ones, thereby altering the internal wiring and communication protocols to accommodate electric powertrains, connectivity features, and autonomous applications, including the embedded and decision-making software in existing and future vehicles.

The study focuses on the sensor data fusion strategies for ADAS and AD systems in the NA and EU regions, with forecasts running up until 2025. Frost & Sullivan has highlighted the key strategies of sensor data fusion and its influence on the key design elements of E/E architecture and sourcing AD software to achieve L3 and above autonomy. The study discusses the major trends observed in the market and explains the impact scenarios, along with use cases.

Key Issues Addressed:

  • What is the need for sensor data fusion?
  • What are the different types of sensor data fusion strategies in the market?
  • What is the optimum sensor data fusion strategy for various vehicle segments?
  • What are the key OEM strategies influenced by sensor data fusion to accelerate the development of autonomous driving?
  • What does the autonomous vehicle value chain look like?
  • How are the key sensors in autonomous vehicles evolving and what are their capabilities?
  • How are the sensor requirements changing with the level of autonomy?
  • How is the vehicle E/E architecture evolving and what are the key topologies?
  • What are the business models adopted by the autonomous software developers?

Table of Contents

Executive Summary

  • Key Questions this Study will Answer
  • Sensor Data Fusion Strategies
  • Influence of Sensor Data Fusion Strategies on Vehicle Technologies
  • Vehicle E/E Architecture Strategies
  • AD Sourcing Software Strategies
  • Highlights

Research Scope, Aims, and Objective

  • Research Scope
  • Research Aims and Objectives

Sensor Suite Evolution

  • Technology Evolution-Radar Module
  • Technology Evolution-LiDAR Module
  • Technology Evolution-Forward Camera Module
  • Rise in Sensor Requirements with Levels of Autonomy
  • Individual Sensor Performance Capabilities
  • Need for Sensor Fusion in AD

Sensor Data Fusion Strategies

  • Strategy 1-Distributed Architecture
  • Strategy 2-Centralized Architecture
  • Strategy 3-Hybrid Architecture
  • Sensor Data Fusion Performance Capabilities
  • Role of Sensor Data Fusion by SAE Level of Automation

Sensor Data Fusion Market Trends and Analysis-2019

  • ADAS Sensor Data Fusion Market Penetration
  • Optimum Sensor Data Fusion Strategy Based on Vehicle Segment

Industry-wide Best Practices-Sensor Fusion Suppliers Approach

  • Use Case-AEye and Cartica
  • Use Case-Continental LiDAR Camera Fusion
  • Use Case-Valeo and Mobileye

Industry-wide Best Practices-Sensor Fusion OEM Approach

  • Tesla Autopilot
  • Audi AI

Influence of Sensor Data Fusion on Key Vehicle Technologies

  • Influence of Sensor Data Fusion Strategies on Vehicle Technologies
  • Vehicle E/E Architecture Topologies for Automated Driving

E/E Architecture Evolution

  • Challenges with Existing E/E Architecture
  • Evolution of E/E Architecture
  • Vehicle E/E Architecture Strategies

Industry-wide Best Practices-E/E Architecture OEM Approach

  • GM Digital Vehicle Platform
  • Volkswagen-The Digital Transformation

Industry-wide Best Practices-E/E Architecture Supplier Approach

  • Aptiv-Smart Vehicle Architecture (SVA™)

AD Sourcing Software Strategies

  • Key In-vehicle Software Enabling AD
  • ADAS/AD Sourcing Software Business Models
  • AD Software Platforms
  • Value Chain-AD Software and Technology

Growth Opportunities and Companies to Action

  • Growth Opportunity-Investments and Partnerships From OEMs/TSPs
  • Strategic Imperatives for Success and Growth

Key Conclusions

  • Key Conclusions
  • Legal Disclaimer

Appendix

  • Abbreviations and Acronyms
  • List of Exhibits
  • List of Exhibits (continued)
  • List of Exhibits (continued)
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