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HDマップを使用したモビリティ情勢のナビゲート

Using HD Maps to Navigate the Mobility Landscape

出版日: | 発行: Guidehouse Insights (formerly Navigant Research) | ページ情報: 英文 16 Pages; 3 Tables, Charts & Figures | 納期: 即納可能 即納可能とは

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HDマップを使用したモビリティ情勢のナビゲート
出版日: 2019年03月11日
発行: Guidehouse Insights (formerly Navigant Research)
ページ情報: 英文 16 Pages; 3 Tables, Charts & Figures
納期: 即納可能 即納可能とは
  • 全表示
  • 概要
  • 図表
  • 目次
概要

当レポートでは、HDマッピングの利用例について調査し、これらの地図を作製する方法の議論、およびデータの収集・利用ならびに潜在的なビジネスアレンジメントを可能にするための提言を提供しており、HDマップの様々な機能とこれらツールの利用例などをまとめています。

マシンリーダブルフォームで世界をマッピング

  • HDマップは将来のモビリティ実現のために必要

ビットとバイトで世界を捉える

  • 長年にわたり、特殊車両が地図データを収集
  • 衛星画像からデータを自動的に抽出
  • リアルタイム地図アップデートの主要ソースとして混雑を利用
  • 付加製造 (AM) によりHDマップを作製
  • 地上検証データを決定
  • ユーザーへのデータのアグリゲーションとディストリビューション
  • 移動中のHDマップの読み込み

データの所有権とそれによる受益者の決定

  • 新規車両におけるユニバーサルコネクティビティの保証
  • 消費者は共有されるデータおよび受け取るメリットを把握
  • データプライバシーとセキュリティの確立
  • マッピングの新たなビジネスモデルとしてピアリングを利用
図表

List of Charts and Figures

  • TomTom RoadDNA HD Map with Simplified Visualization
  • WaveSense Ground-Penetrating Radar
  • Multi-Layered HD Map Model
目次
Product Code: SI-HNM-19

The automotive industry is facing the greatest transformation in its 130-plus year history. The fundamental business model of designing, manufacturing, and selling vehicles, parts, and services to consumers is expected to be supplanted by a shift to providing mobility-derived services in the next few decades. A key building block of this shift is location awareness and the ability for a vehicle to navigate between any desired locations. Whether in old-fashioned paper form or a modern digital version, basic street-level maps have always helped travelers navigate. However, these are now insufficient for the upcoming mobility paradigm.

As the automotive industry accelerates toward electrification, automation, and connectivity, embedded navigation with high definition (HD) maps becomes increasingly important. In recent years, several manufacturers have already begun using topographical information as an input to powertrain control and others are using road contour information to manage speed in partially automated driving systems. Highly automated vehicles will require detailed HD maps to enable precise localization. This new age of maps is targeted more at the electronic control systems running in the vehicle than at drivers.

This Navigant Research report assesses the use cases for HD mapping, discusses the means for building these maps, and provides recommendations for enabling the collection and use of data and potential business arrangements. The study describes various functionalities of HD maps as well as use cases for these tools. Guidance is provided for OEMs, traditional suppliers of navigation data, and new entrants into the field.

Key Questions Addressed

  • Why are machine-readable HD maps needed for new mobility applications?
  • How is the data for HD maps collected?
  • Can HD maps generate new revenue?
  • Is crowdsourcing map data viable?
  • Who owns and secures the data?

Who Needs This Report

  • Vehicle manufacturers
  • Automotive industry suppliers
  • Mapping and navigation providers
  • Commercial fleet managers
  • Mobility service providers
  • City managers
  • Government agencies
  • Investor community

Table of Contents

Spark

Context

Recommendations

Mapping the World in Machine-Readable Form

  • HD Maps Are Needed to Enable Future Mobility
    • Automated Driving Requires Embedded HD Maps
      • Creating a Longer-Range Sensor through Maps
      • Determining Absolute Position with Simultaneous Localization and Mapping
      • Creating a Route for Navigation
    • HD Maps Can Help Improve Propulsion Efficiency
    • Location Enables New Revenue-Generating Services
    • Maps Are Necessary for Dispatch and Logistics of Automated Vehicles

Capturing the World in Bits and Bytes

  • Fleets of Specialized Vehicles Have Been Collecting Map Data for Years
  • Automatic Extraction of Data from Satellite Imagery
  • Using the Crowd as a Key Source of Real-Time Map Updates
    • Understanding Areas of Interest Using Big Data and Machine Learning
  • Creating HD Maps by Additive Manufacturing
  • Determining the Ground Truth of Data
  • Aggregation and Distribution of Data to Users
  • Reading HD Maps on the Go
    • Using Smartphone Projection for Onboard Navigation

Determine Ownership of Data and Who Profits from It

  • Ensure Universal Connectivity in New Vehicles
  • Ensure Consumers Understand the Data Being Shared and the Benefits Received
  • Establish Data Privacy and Security
  • Use Peering as the New Business Model for Mapping
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