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自律走行配送産業（2024年）

Autonomous Delivery Industry Research Report, 2024

出版日
ページ情報
英文 205 Pages
納期
即日から翌営業日
価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=143.57円
自律走行配送産業（2024年）
出版日: 2024年06月22日
発行: ResearchInChina
ページ情報: 英文 205 Pages
納期: 即日から翌営業日
GIIご利用のメリット
  • 全表示
  • 概要
  • 目次
概要

1. 自律配送車が通常運行に入り、江蘇省は全国の最先端を走っています。

これまでのところ、自律配送は速達便、スーパーマーケット、自動小売などのシーンに応用されています。速達企業では自律配送の正常な応用が実現されています。地域別では、2023年初め～2024年5月に、速達企業における自律配送車の通常運行は主に華東、華北、華西北に集中しました。具体的には華東地区でもっとも自律走行車が多く、主に江蘇省、安徽省、浙江省に広がっています。省別の順位は、江蘇省>安徽省>陝西省>浙江省>山西省>四川省>河北省/北京市となっています。

江蘇省は通常運行する自律配送車の数がもっとも多く、蘇州市は江蘇省、さらには全国でもっとも速達企業の自律配送車が多い都市です。Standing Committee of the 14th Jiangsu Provincial People's Congressは2023年11月、全国に先駆けてInternet of Vehiclesとインテリジェントコネクテッドビークルの開発を促進する法律を制定し、Decision on Promoting the Development of Internet of Vehicles and Intelligent Connected Vehiclesを承認しました。2024年4月までに、蘇州市湘城区では41台の自律配送車が通常運行し、毎日約1万4,000個の小包を配送しています。湘城区はSF Express、Yunda Express、STO Expressなどの速達業者と深い協力関係を結びました。

2.「屋外自律走行車+屋内ロボット」は自律的に目的地に直接配送することができます。

2023年6月、「屋外自律走行車+屋内ロボット」統合AI配送ソリューションがChina Postal Express & Logisticsの本社で正式に発表されました。この新しいソリューションは、屋外自律走行車とさまざまな屋内ロボットを組み合わせ、さまざまなシナリオで目的地に商品を直接配送することができます。例えば、屋外自律走行車が屋内ロボットに接触し、郵便室のスタッフがロボットを操作して商品を配送先に届け、応用者がWeChatアプリでコーヒーを注文するとロボットが配送し、応用者がEMSのWeChatアプリで注文するとロボットが商品を受け取ります。

「屋外自律走行車+屋内ロボット」の配送ソリューションは主に、屋外自律走行車のスーパーシャーシとスーパーブレインに依存し、さまざまな屋内ロボットと接触して集中的な共有、幅広い協力、リアルタイムのスケジューリングを行います。一方、屋内ロボットは完全な知覚・認知機能コンポーネントと成熟した測位・ナビゲーション機能コンポーネントを備えており、1つのキーでエレベーターを呼び出したり、電子ドアを開けたり、改札を通過したり、IoTにアクセスしたりすることができます。

2024年4月、EMSのWeChatアプリがロボットの戸別配送サービスを開始しました。同アプリの配送ページには、「ロボット戸別配送」オプションが追加されています。大きな公園や大学などでは、配送業者が自律走行車を操作して階下へ荷物を運び、屋内ロボットが自動的に目的地まで「中継」します。

3. 自律配送技術は急速にイテレーションされ、BEV+Transformerの知覚技術が車両に上陸しました。

NEOLIXのアルゴリズム構造は、第1段階の軽い知覚と重い地図、第2段階の重い知覚と軽い地図、第3段階の基礎モデルの4次元知覚に基づくリアルタイム地図生成の3段階を経ています。

自律運転ソフトウェアの技術レベルでは、Neolixの自律走行車X3 Plusは、自律運転の基本技術アーキテクチャ論理を厳格に踏襲し、マルチモーダルBEV空間4D時系列融合知覚技術を採用し、Orion-Xを搭載して融合前のBEV知覚を実現し、Transformer知覚基礎モデルを活用して連続時系列のマルチセンサーデータをリアルタイムで処理し、死角ゼロ、高精度、高堅牢度の環境知覚を実現します。

当レポートでは、中国の自律配送産業について調査分析し、自律配送車の現状、自律配送ロボットの応用シナリオとソリューションプロバイダー、今後の研究開発動向の予測などの情報を提供しています。

目次

第1章 自律配送産業の概要

  • 自律配送産業の定義と分類
  • 国外の自律配送管理政策
  • 中国の自律配送車の路上テストと運用エリアのサマリー（１）
  • 中国の自律配送車の路上テストと運用エリアのサマリー（2）
  • 中国の自律配送車の路上テストと運用エリアのサマリー（3）
  • 自律配送車の路上試験と商業用実証管理措置の政策例：北京（2023年2月）
  • 中国で自律配送車が正式に走行を開始した地域のサマリー
  • 中国の自律配送車の開発における問題点
  • 自律配送車の規模拡大と商業化促進に関する提案
  • 自律配送の産業チェーン
  • 自律配送車ソリューションプロバイダーのサマリー
  • 自律配送車アプリケーションプロバイダーと連携のサマリー（１）
  • 自律配送車アプリケーションプロバイダーと連携のサマリー（２）
  • 自律配送車アプリケーション/ソリューションプロバイダーのサマリー
  • 自律配送ロボットソリューションプロバイダーのサマリー（1）
  • 自律配送ロボットソリューションプロバイダーのサマリー（2）
  • 自律配送ロボットソリューションプロバイダーのサマリー（3）

第2章 自律配送車の概要と応用事例

  • 自律配送車の概要
  • 7の産業チェーン
  • DADAの応用ソリューション
  • Meituanの応用とソリューション
  • Cainiaoの応用とソリューション
  • JD.comの応用とソリューション
  • SFの応用とソリューション
  • China Postの応用とソリューション
  • YTOの応用ソリューション
  • ZTOの応用ソリューション

第3章 自律配送車ソリューションプロバイダー

  • Neolix
  • Go Further AI
  • ZELOS
  • Haomo.AI
  • White Rhino

第4章 自律配送ロボットの応用シナリオとソリューション

  • 自律配送ロボットのタイプ
  • ホテルにおける自律配送ロボットの応用シナリオとソリューション
  • オフィスビルにおける自律配送ロボットの応用シナリオとソリューション
  • ショッピングモールにおける自律配送ロボットの応用シナリオとソリューション

第5章 自律配送ロボットソリューションプロバイダー

  • OrionStar
  • Keenon
  • PUDU
  • LimX Dynamics

第6章 自律配送の開発動向

目次
Product Code: CX003

Autonomous Delivery Research: Foundation Models Promote the Normal Application of Autonomous Delivery in Multiple Scenarios

Autonomous Delivery Industry Research Report, 2024 released by ResearchInChina combs and studies the status quo of autonomous delivery vehicles in the autonomous delivery industry, the application scenarios of autonomous delivery robots and solution providers, as well as predicts the future development trends of autonomous delivery.

1. Autonomous delivery vehicles have entered normal operation, and Jiangsu Province is at the forefront of the country.

So far, autonomous delivery has been applied to express delivery, supermarkets, autonomous retail and other scenarios. The normal application of autonomous delivery has been realized in express delivery enterprises. By region, the normal operation of autonomous delivery vehicles in express delivery enterprises was mainly concentrated in East China, North China and Northwest China during the period from early 2023 to May 2024. Specifically: East China saw the most autonomous delivery vehicles, which mainly spread in Jiangsu, Anhui and Zhejiang. By province, the ranking is Jiangsu > Anhui > Shaanxi > Zhejiang > Shanxi > Sichuan > Hebei/Beijing.

Jiangsu Province has the largest number of autonomous delivery vehicles in normal operation, and Suzhou is the city with the most autonomous delivery vehicles in express delivery enterprises in Jiangsu and even the whole country. In November 2023, Jiangsu took the lead in the country to enact legislation to promote the development of Internet of Vehicles and intelligent connected vehicles: the Standing Committee of the 14th Jiangsu Provincial People's Congress approved the "Decision on Promoting the Development of Internet of Vehicles and Intelligent Connected Vehicles", which made guiding and authoritative provisions on the passage and management of autonomous driving equipment such as autonomous delivery vehicles on roads and came into effect on January 1, 2024. By April 2024, there had been 41 autonomous delivery vehicles in normal operation in Xiangcheng District of Suzhou, delivering about 14,000 parcels every day. Xiangcheng District reached in-depth cooperation with express delivery companies such as SF Express, Yunda Express and STO Express.

The following indicates the normal operation of autonomous delivery vehicles in express delivery enterprises by province:

  • 2. "Outdoor autonomous vehicles + indoor robots" can accomplish autonomous direct delivery to destinations.

In June 2023, an "outdoor autonomous vehicles + indoor robots" integrated AI delivery solution was officially launched in the headquarters of China Postal Express & Logistics. The new solution can combine outdoor autonomous vehicles with different indoor robots to directly deliver goods to destinations in a variety of scenarios. For example, outdoor autonomous vehicles contact indoor robots, mailroom staff operate robots to deliver goods to destinations, users can order coffee via WeChat applets which is then delivered by robots, and robots pick up items after users place orders on the WeChat applet of EMS.

The "outdoor autonomous vehicles + indoor robots" delivery solution mainly relies on the super chassis and super brains of outdoor autonomous vehicles to contact different indoor robots for intensive sharing, wide cooperation and real-time scheduling, while indoor robots have complete perception and cognitive functional components as well as mature positioning and navigation functional components to summon elevators with one key, open electronic doors, pass turnstiles and access Internet of Things.

In April 2024, the WeChat applet of EMS launched the robot door-to-door delivery service. On the delivery page of the applet, there is an additional "robot door-to-door" delivery option. In large parks, universities and other places, couriers operate autonomous vehicles to transport parcels downstairs, and them indoor robots automatically "relay" them to destinations.

3. Autonomous delivery technology has been rapidly iterated, and BEV+Transformer perception technology has landed on vehicles.

The algorithm structure of NEOLIX has gone through three stages, from first stage of light perception and heavy maps, to the second stage of heavy perception and light maps, and to the third stage of real-time map generation based on 4D perception of foundation models.

On the technical level of autonomous driving software, X3 Plus, Neolix's autonomous vehicle, strictly follows the underlying technical architecture logic of autonomous driving, adopts the multi-modal BEV space 4D time sequence fusion perception technology, carries Orion-X to realize pre-fusion BEV perception, and leverages the Transformer perception foundation model to process multi-sensor data of continuous time series in real time and realize zero-blind-spot, high-precision and high-robustness environmental perception.

In BEV space, time sequence fusion is carried out to form 4D space. At the same time, complex traffic flow reinforcement learning (TFRL) allows autonomous vehicles to interact with other road participants and predict their future behavior, so as to conduct better planning and control. Neolix's autonomous driving system has the capability of autonomous learning, so that it can perceive more complex obstacles, handle more complicated roads, and make autonomous delivery easy.

In other words, all the features of 2 lidars and 11 panoramic cameras are converted into the BEV space for fusion, and more comprehensive perception is fulfilled based on the fused features. This system can make full use of the advantages of each sensor, and overcome the corresponding shortcomings, so as to perform perfect perception within a range of 40m front and rear and 30m left and right, and ensure that autonomous vehicles can accurately detect objects and lane lines and recognize traffic lights, thus guaranteeing the driving safety of autonomous vehicles.

Table of Contents

1 Overview of Autonomous Delivery Industry

  • 1.1 Definition and Classification of Autonomous Delivery Industry
  • 1.2 Overseas Autonomous Delivery Management Policies
  • 1.3 Summary of Road Tests and Operation Areas of Autonomous Delivery Vehicles in China (1)
  • 1.3 Summary of Road Tests and Operation Areas of Autonomous Delivery Vehicles in China (2)
  • 1.3 Summary of Road Tests and Operation Areas of Autonomous Delivery Vehicles in China (3)
  • 1.4 Policy Example of Road Tests and Commercial Demonstration Management Measures for Autonomous Delivery Vehicles: Beijing (February 2023)
  • 1.5 Summary of Regions Where Autonomous Delivery Vehicles Officially Hit the Road in China
  • 1.6 Problems in the Development of Autonomous Delivery Vehicles in China
  • 1.7 Suggestions on Promoting the Scale and Commercialization of Autonomous Delivery Vehicles
  • 1.8 Autonomous Delivery Industry Chain
  • 1.9 Summary of Autonomous Delivery Vehicle Solution Providers
  • 1.10 Summary of Autonomous Delivery Vehicle Application Providers and Cooperation (1)
  • 1.10 Summary of Autonomous Delivery Vehicle Application Providers and Cooperation (2)
  • 1.11 Summary of Autonomous Delivery Vehicle Application Providers and Solution Providers
  • 1.12 Summary of Autonomous Delivery Robot Solution Providers (1)
  • 1.12 Summary of Autonomous Delivery Robot Solution Providers (2)
  • 1.12 Summary of Autonomous Delivery Robot Solution Providers (3)

2 Overview and Application Cases of Autonomous Delivery Vehicles

  • 2.1 Overview of Autonomous Delivery Vehicles
    • 2.1.1 Product Types
    • 2.1.2 Application Scenarios
    • 2.1.3 Composition
    • 2.1.4 Core Technology
    • 2.1.5 Core Parts
    • 2.1.6 Main Cost
  • 2.1. 7 Industry Chain
    • 2.1.8 Latest Financing
  • 2.2 Application Solutions of DADA
    • 2.2.1 Open Autonomous Delivery Platform
    • 2.2.2 Delivery Capacity Modes
    • 2.2.3 Cooperation with Autonomous Delivery Vehicle Companies
    • 2.2.4 Cases of Cooperation with Autonomous Delivery Vehicle Companies
    • 2.2.5 Application Cases
  • 2.3 Application and Solutions of Meituan
    • 2.3.1 Autonomous Delivery Vehicle Solutions
    • 2.3.2 Core Technology of Autonomous Delivery Vehicles
    • 2.3.3 The First Open Road Test of Automatic Delivery Vehicles
    • 2.3.4 Application Scenario Cases
  • 2.4 Application and Solutions of Cainiao
    • 2.4.1 Autonomous Delivery Vehicle Solutions
    • 2.4.2 Summary of Universities Where Autonomous Delivery Vehicles Are Used
    • 2.4.3 Application Scenario Cases
  • 2.5 Application and Solutions of JD.com
    • 2.5.1 Autonomous Delivery Vehicle Solutions (1)
    • 2.5.2 Autonomous Delivery Vehicle Solutions (2)
    • 2.5.3 Application Scenario Cases (1)
    • 2.5.4 Application Scenario Cases (2)
    • 2.5.5 Application Scenario Cases (3)
    • 2.5.6 Application Scenario Cases (4)
  • 2.6 Application and Solutions of SF
    • 2.6.1 Autonomous Delivery Vehicle Solutions (1)
    • 2.6.2 Autonomous Delivery Vehicle Solutions (2)
    • 2.6.3 Autonomous Delivery Vehicle Solutions (3)
    • 2.6.4 Summary of the First Batch of Autonomous Delivery Vehicles on the Road in Cities
    • 2.6.5 Application Scenario Cases (1)
    • 2.6.6 Application Scenario Cases (2)
    • 2.6.7 Cases of Cooperation with Autonomous Delivery Vehicle Companies
  • 2.7 Application and Solutions of China Post
    • 2.7.1 Summary of Solutions in Cooperation with Autonomous Delivery Vehicle Companies
    • 2.7.2 Latest Autonomous Delivery Vehicle Solutions (1)
    • 2.7.2 Latest Autonomous Delivery Vehicle Solutions (2)
    • 2.7.3 Usage of Autonomous Delivery Vehicles in Cities and Outlets
    • 2.7.4 Application Cases (1)
    • 2.7.5 Application Cases (2)
  • 2.8 Application Solutions of YTO
    • 2.8.1 Solutions in Cooperation with Autonomous Delivery Vehicle Companies (1)
    • 2.8.2 Solutions in Cooperation with Autonomous Delivery Vehicle Companies (2)
    • 2.8.3 Solutions in Cooperation with Autonomous Delivery Vehicle Companies (3)
    • 2.8.4 Solutions in Cooperation with Autonomous Delivery Vehicle Companies (4)
    • 2.8.5 Summary of the First Batch of Autonomous Delivery Vehicles on the Road in Cities
    • 2.8.6 Application Cases
  • 2.9 Application Solutions of ZTO
    • 2.9.1 Solutions in Cooperation with Autonomous Delivery Vehicle Companies (1)
    • 2.9.2 Solutions in Cooperation with Autonomous Delivery Vehicle Companies (2)
    • 2.9.3 Solutions in Cooperation with Autonomous Delivery Vehicle Companies (3)
    • 2.9.4 Summary of the First Batch of Autonomous Delivery Vehicles on the Road in Cities
    • 2.9.5 Application Cases of Autonomous Delivery Vehicles
    • 2.9.6 Autonomous Vehicle Operation and Supervision Platform

3 Autonomous Delivery Vehicle Solution Providers

  • 3.1 Neolix
    • 3.1.1 Summary of Autonomous Delivery Vehicle Solutions
    • 3.1.2 Latest Autonomous Delivery Vehicle Solutions (1)
    • 3.1.2 Latest Autonomous Delivery Vehicle Solutions (2)
    • 3.1.3 Core Technology of Autonomous Delivery Vehicles (1)
    • 3.1.3 Core Technology of Autonomous Delivery Vehicles (2)
    • 3.1.4 Summary of Road Rights of Autonomous Delivery Vehicles in Key Cities in China
    • 3.1.5 Overseas Landing of Autonomous Delivery Vehicles and Cooperation
    • 3.1.6 Application Scenarios of Autonomous Delivery Vehicles and Cooperation
    • 3.1.7 Cases of Cooperation in Autonomous Delivery Vehicle Technology (1)
    • 3.1.8 Cases of Cooperation in Autonomous Delivery Vehicle Technology (2) and Financing Layout
  • 3.2 Go Further AI
    • 3.2.1 Autonomous Delivery Vehicle Solutions (1)
    • 3.2.2 Autonomous Delivery Vehicle Solutions (2)
    • 3.2.3 Autonomous Delivery Vehicle Solutions (3)
    • 3.2.4 Core Technology of Autonomous Delivery Vehicles (1)
    • 3.2.4 Core Technology of Autonomous Delivery Vehicles (2)
    • 3.2.5 Summary of Application Solutions of Autonomous Delivery Vehicles
    • 3.2.6 Summary of Regions Where Autonomous Delivery Vehicles Are Used and Services
  • 3.3 ZELOS
    • 3.3.1 Autonomous Delivery Vehicle Solutions
    • 3.3.2 Technical Framework and Operation Platform of Autonomous Delivery Vehicles
    • 3.3.3 Core Technology of Autonomous Delivery Vehicles (1)
    • 3.3.3 Core Technology of Autonomous Delivery Vehicles (2)
    • 3.3.4 Application Solutions of Autonomous Delivery Vehicles
  • 3.4 Haomo.AI
    • 3.4.1 Autonomous Delivery Vehicle Solutions (1)
    • 3.4.2 Autonomous Delivery Vehicle Solutions (2)
    • 3.4.3 Cooperation in Autonomous Delivery Vehicle Technology
    • 3.4.4 Manufacturing Bases of Autonomous Delivery Vehicles
    • 3.4.5 Application Solutions of Autonomous Delivery Vehicles
  • 3.5 White Rhino
    • 3.5.1 Autonomous Delivery Vehicle Solutions and Application Solutions (1)
    • 3.5.2 Autonomous Delivery Vehicle Solutions and Application Solutions (2)
    • 3.5.3 Core Technology of Autonomous Delivery Vehicles

4 Application Scenarios and Solutions of Autonomous Delivery Robots

  • 4.1 Types of Autonomous Delivery Robots
  • 4.2 Application Scenarios and Solutions of Autonomous Delivery Robots in Hotels
    • 4.2.1 Application Cases of Autonomous Delivery Robots in Hotels (1)
    • 4.2.2 Application Cases of Autonomous Delivery Robots in Hotels (2)
    • 4.2.3 Application Cases of Autonomous Delivery Robots in Hotels (3)
    • 4.2.4 Application Cases of Autonomous Delivery Robots in Hotels (4)
  • 4.3 Application Scenarios and Solutions of Autonomous Delivery Robots in Office Buildings
    • 4.3.1 Application Cases of Autonomous Delivery Robots in Office Buildings (1)
    • 4.3.2 Application Cases of Autonomous Delivery Robots in Office Buildings (2)
    • 4.3.3 Solutions of Autonomous Delivery Robots in Office Buildings
  • 4.4 Application Scenarios and Solutions of Autonomous Delivery Robots in Shopping Malls
    • 4.4.1 Application Cases of Autonomous Delivery Robots in Shopping Malls (1)
    • 4.4.2 Application Cases of Autonomous Delivery Robots in Shopping Malls (2)
    • 4.4.3 Solutions of Autonomous Delivery Robots in Shopping Malls

5 Autonomous Delivery Robot Solution Providers

  • 5.1 OrionStar
    • 5.1.1 Autonomous Delivery Robot Solutions (1)
    • 5.1.1 Autonomous Delivery Robot Solutions (1)
    • 5.1.2 Autonomous Delivery Robot Solutions (2)
    • 5.1.2 Autonomous Delivery Robot Solutions (2)
  • 5.2 Keenon
    • 5.2.1 Autonomous Delivery Robot Solutions
    • 5.2.2 Application Scenarios and Cooperation Cases of Autonomous Delivery Robots
  • 5.3 PUDU
    • 5.3.1 Autonomous Delivery Robot Solutions (1)
    • 5.3.1 Autonomous Delivery Robot Solutions (2)
    • 5.3.2 Application Scenarios and Cooperation Cases of Autonomous Delivery Robots
    • 5.3.3 Autonomous Quadruped Delivery Robot Solutions (1)
    • 5.3.3 Autonomous Quadruped Delivery Robot Solutions (2)
  • 5.4 LimX Dynamics
    • 5.4.1 Autonomous Quadruped Delivery Robot Solutions (1)
    • 5.4.1 Autonomous Quadruped Delivery Robot Solutions (2)
    • 5.4.2 Autonomous Bipedal Delivery Robot Solutions
    • 5.4.3 Core Technology of Autonomous Delivery Robots and Latest Financing

6 Development Trends of Autonomous Delivery

  • 6.1 Trend 1
  • 6.2 Trend 2
  • 6.3 Trend 3
  • 6.4 Trend 4
  • 6.5 Trend 5
  • 6.6 Trend 6
  • 6.7 Trend 7
  • 6.8 Trend 8
  • 6.9 Trend 9