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市場調查報告書
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1613805

客車高速公路/城市NOA的中國市場(2024年)

China Passenger Car Highway & Urban NOA (Navigate on Autopilot) Research Report, 2024
出版日期: | 出版商: ResearchInChina | 英文 502 Pages | 商品交期: 最快1-2個工作天內

價格
簡介目錄

近年來，自動駕駛技術的發展路徑逐漸清晰，產業正加速從L2邁向L2.5/L2.9乃至L3。在此過程中，高速公路NOA的普及和城市NOA的快速發展已成為全行業的共識。 2023年下半年起，城市NOA的市場競爭將越來越激烈。各大主機廠正在加速城市NOA技術的部署，並逐步發布發展計畫。 2023年第三季起，乘用車NOA開發進入後期階段。許多汽車廠商正在積極部署和利用端到端的基礎模型和無地圖解決方案，推動國家智慧駕駛發展進入新階段。

目前，乘用車NOA面臨以下發展趨勢。

趨勢一：高速公路NOA、城市NOA滲透率持續上升

特斯拉自2019年首次在中國引進高速公路NOA以來，已於2022年快速落地，目前已涵蓋許多國內自主品牌和合資品牌。 2023年底，國產L2.5以上ADAS乘用車銷售量將達148.4萬輛，滲透率達7.1%，銷售量年增78.3%。

到2024年上半年末，L2.5級以上國產ADAS乘用車新銷量106萬輛，滲透率11%，其中高速公路NOA保有量32.8萬輛，普及率3.4%（不含同時具備高速公路NOA和城市NOA功能的車型）。城市NOA車型保有量73.2萬輛，普及率7.6%。

趨勢二：2023-2024年，城市NOA將進入軍備競賽

國內主要整車廠和解決方案提供商，以特斯拉、蔚來/小鵬汽車/理想汽車、華為系統等整車廠為首，都在向城市NOA邁進，其中一些將在2023年成為汽車。 2023-2024年，城市NOA將進入軍備競賽，多家主機工廠已成功量產L2.9。

趨勢三：2023年後聯合整車廠將加速高速公路NOA跟進

從OEM類型來看，2022年，高速公路NOA企業將集中於本土市場，不會有外資進入。 2023年起，合資整車廠開始加速高速公路NOA跟進。截至2024年上半年，聯合整車廠公路NOA車型在華銷量已達5.4萬輛，滲透率為0.6%。 2022年至2024年上半年，自主OEM公路NOA車款銷售量及採用率持續上升。截至2023年底，銷量達45.4萬輛，年增124.5%，滲透率達2.2%，較去年同期提升1.2個百分點。 2024年上半年銷售27.5萬輛，滲透率為2.8%。

2023年，合資品牌都市NOA銷售60.4萬輛，滲透率5.9%，較前一年提升1.8個百分點；自主品牌汽車銷售42.3萬輛，滲透率5.8%。提高1.8個百分點。 2023年至2024年上半年，自主品牌城市NOA模式的採用率將進一步加速。截至2024年上半年，自主品牌城市NOA銷售滲透率由2023年底的3.9%提升至8.3%。這一趨勢顯示眾多自主品牌在智慧駕駛領域的快速佈局以及國內消費者對L2.9 ADAS產品的接受度不斷提高。

趨勢四：L2.9、L2.5加速滲透中低價市場，科技平等時代悄悄來臨

從價格來看，城市NOA功能在20萬-30萬元價位車型中成長最快。 2024年上半年，城市NOA安裝量成長最快，尤其是 "20萬-25萬元" 細分市場，科技平等時代已悄悄到來。這一趨勢預示著，2024年，ADAS技術將加速向中低端市場滲透，逐漸普及，成為未來日常家庭用車的標準配備。

本報告研究分析了中國乘用車高速公路/城市NOA市場，提供銷售量和滲透率數據，以及國內外供應商NOA專案資訊。

目錄

第1章 中國的NOA支援小客車模式的銷售額與解決方案

  • NOA搭載車款銷售及滲透率
  • 適用於 NOA 相容型號的傳感器解決方案
  • 來自主要供應商的 NOA 解決方案
  • NOA主要供應商分析

第2章 客車NOA市場趨勢與考察

  • ADAS 更上一層樓
  • 高層NOA正在從高速公路NOA演變為城市NOA
  • NOA推動產業鏈技術升級

第3章 客車NOA解決方案和OEM的利用

  • Xpeng Motors
  • Li Auto
  • NIO
  • IM Motors
  • AITO
  • BYD
  • GAC
  • Geely
  • Changan Automobile
  • Leapmotor
  • Tesla

第4章 國內供應商的客車NOA計劃

  • Desay SV
  • Jingwei Hirain
  • Freetech
  • Huawei
  • Baidu Apollo
  • (舊DJI)
  • Haomo.AI
  • Momenta
  • Yihang.AI
  • Hongjing Drive
  • NavInfo
  • SenseTime
  • Horizon Robotics
  • Neusoft Reach
  • MAXIEYE
  • iMotion
  • Nullmax
  • ZongMu Technology
  • AutoBrain
  • QCraft
  • DeepRoute
  • Pony.ai

第5章 國外供應商的客車NOA計劃

  • Bosch
  • Continental
  • ZF
  • Aptiv
  • Mobileye
簡介目錄
Product Code: DTT003

NOA industry research: seven trends in the development of passenger car NOA

In recent years, the development path of autonomous driving technology has gradually become clear, and the industry is accelerating from L2 to L2.5/L2.9 and even L3. In this process, promoting popularization of Highway NOA and rapid development of Urban NOA has become a consensus of entire industry. Since 2023H2, the market competition for Urban NOA has become increasingly fierce. Major OEMs have accelerated the implementation of Urban NOA technology and gradually disclosed their development plans. Since 2023Q3, the development of passenger car NOA has entered the second half stage. Many automakers have actively deployed and used end-to-end foundation models and map-free solutions to promote the development of national intelligent driving into a new stage.

Currently, passenger car NOA faces the following evolution trends:

Trend 1: Highway NOA and Urban NOA penetration rates continue to increase

Since Tesla first introduced Highway NOA to China in 2019, it has been rapidly implemented in 2022 and has now covered many domestic independent and joint venture brands. By the end of 2023, sales volume of domestic ADAS passenger cars with L2.5 and above reached 1.484 million, with a penetration rate of 7.1%, of which sales increased by 78.3% year-on-year.

By the end of 2024H1, sales volume of domestic new passenger cars with L2.5 and above ADAS models was 1.06 million, with a penetration rate of 11%; among them, sales volume of Highway NOA models was 328,000, with a penetration rate of 3.4% (excluding models with both Highway NOA and Urban NOA functions). Sales volume of Urban NOA models was 732,000, with a penetration rate of 7.6%.

Trend 2: Urban NOA entering an arms race in 2023~2024

Led by OEMs such as Tesla, NIO/Xpeng Motors/Li Auto, and Huawei System, major domestic OEMs and solution providers have all moved towards Urban NOA, some of which have begun to installed in vehicles in 2023. From 2023 to 2024, Urban NOA enters an arms race, and many OEMs have successfully mass-produced L2.9.

Trend 3: Since 2023, joint venture OEMs have accelerated their follow-up of Highway NOA

From the perspective of OEM type, in 2022, Highway NOA enterprises concentrated in local market, and no foreign capital has entered. Since 2023, joint venture OEMs have begun to accelerate their follow-up of Highway NOA. As of 2024H1, sales of joint venture OEM Highway NOA models in China reached 54,000 units, with a penetration rate of 0.6%. From 2022 to 2024H1, the sales and penetration rate of independent OEM Highway NOA models continued to rise. By the end of 2023, its sales had reached 454,000 units, a year-on-year increase of 124.5%; the penetration rate reached 2.2%, an increase of 1.2 percentage points year-on-year. In 2024H1, its sales reached 275,000 units, with a penetration rate of 2.8%.

In 2023, sales volume of joint venture brand Urban NOA was 604,000 units, with a penetration rate of 5.9%, up 1.8 percentage points year-on-year; sales volume of independent brand models was 423,000 units, with a penetration rate of 3.9%, up 1.8 percentage points year-on-year. From 2023 to 2024H1, the penetration rate of independent brand Urban NOA models further accelerated. As of 2024H1, the sales penetration rate of independent brand Urban NOA increased from 3.9% at the end of 2023 to 8.3%. This trend shows the rapid layout of many independent brands in intelligent driving field and the increasing acceptance of domestic consumers for L2.9 ADAS products.

Trend 4: L2.9 and L2.5 are accelerating their penetration into the mid- and low-end markets, and the era of technological equality is quietly approaching.

From the perspective of price, Urban NOA functions have grown most significantly in models priced at 200,000-300,000 yuan. In 2024H1, especially in the "200,000-250,000 yuan" market segment, Urban NOA's installation volume grew fastest, and the era of technological equality has quietly arrived. This trend shows that in 2024, ADAS technology is accelerating its penetration into mid- and low-end markets, gradually becoming popular, and becoming a standard configuration for future daily family cars.

In the field of Highway NOA, L2.5 has been extended to models in two price ranges of "100,000-150,000 yuan" and "150,000-200,000 yuan". According to ResearchInChina data, penetration rate of domestic passenger car Highway NOA models priced at "100,000-150,000 yuan" and "150,000-200,000 yuan" increased from 0.02% and 0.29% at the end of 2023 to 0.08% and 1.39% in 2024H1, respectively. Taking the iCAR03 Family, a new energy electric brand under Chery Group, as an example, on June 12, 2024, iCAR 03 launched two new intelligent driving versions with an official price of 149,800 yuan and 159,800 yuan. iCAR 03 Intelligent Driving belongs to the L2.5 and has Highway NOA functions.

Trend 5: The first year of map-free Urban NOA has arrived, and many OEMs have launched national intelligent driving solutions without HD maps

At present, the debate around "map-free" technology route has also risen to an unprecedented height, and getting rid of HD maps has begun to become a key R&D direction of more and more Chinese companies. Among many domestic OEMs, OEMs that have adopted map-free NOA solutions include but are not limited to: Li Auto, Xpeng Motors, Huawei System, GAC, Great Wall Motor, Zeekr, etc., and Xiaomi adopts the low-weight map solution. Taking Huawei as an example, the NCA (Navigation Cruise Assist) system launched by Huawei in December last year does not require HD maps and covers all types of public roads across China. Based on ADS2.0 platform, the system integrates BEV and GOD networks to improve road analysis capabilities. In 2024, Huawei released Qiankun ADS3.0 system, which makes driving behavior more humanized through bionic neural networks and AI algorithms. In May 2024, Xpeng Motors announced that XNGP has achieved 100% map-free, and in cities and counties, core sections will be opened first to ensure the continuity of user experience. Even in areas without HD maps, through the combination of "navigation map + XNet perception capability + driving strategy", XNGP's functional performance is close to that in areas with HD maps.

Trend 6: End-to-end foundation models are being adopted in vehicles to help upgrade intelligent driving

In 2024, emerging OEMs announced that their self-developed foundation model would be applied to vehicles. Introduction of foundation model made the system more accurate and efficient in dealing with complex environments and dynamic changes. Through deep learning and real-time data processing, the foundation model can analyze road conditions in real time, make intelligent decisions, and provide drivers with a safer and more reliable driving experience. With the promotion of this solution, autonomous driving systems can be more widely used nationwide, improving driving flexibility and adaptability.

Li Auto believes that it is not possible to achieve autonomous driving above L4 by relying solely on One Model end-to-end. It proposed a new solution: "System 1 + System 2", namely, E2E (end-to-end foundation model) + VLM (visual language model). Currently, System 1 is in the "second generation: map-free, segmented end-to-end", which consists of two models, namely perception and planning. The biggest change is the removal of NPN, which does not rely on prior information. This generation of technology allows Li Auto to truly be able to drive across the country and can be driven with navigation only.

Trend 7: Vision-only perception route is regarded as one of new directions of technological development by more Chinese OEMs

In the evolution of Chinese intelligent driving technology, core technology architecture of the first half is highly dependent on LiDAR and HD map. This mode ensures the stability and safety of autonomous driving functions through complex sensor fusion technology and support of geographic information systems, especially in real-time updating and precise matching of HD maps.

As the second half of intelligent driving competition begins, the technical routes are showing a diversified development trend, which can be mainly divided into two categories:

The first technical route, represented by Huawei, adopts the strategy of "LiDAR multi-fusion perception + map-free solution/lightweight map solution + end-to-end foundation model". Under this framework, LiDAR provides high-precision perception data, combined with map-free or lightweight map solutions, reducing the system's dependence on HD maps. Meanwhile, the application of end-to-end foundation model further enhances system's autonomous learning and decision capabilities, making the autonomous driving system more flexible and reliable in complex environments.

The second technical route, represented by Tesla, Xpeng Motors, Jiyue, etc., mainly adopts the technical combination of "vision-only + map-free solution / HD map + end-to-end foundation model". In August 2024 , Xpeng Motors officially launched the AI Eagle Eye Vision Solution, which is a high-level intelligent driving solution with light radar (LiDAR has changed from mandatory to optional in L3). The first model equipped with AI Eagle Eye Vision Solution is the P7+, which will be officially launched in Q4.

Table of Contents

1 Sales and Solutions of NOA-enabled Passenger Car Models in China

  • 1.1 Sales and Penetration Rate of Models Equipped with NOA
    • 1.1.1 Sales and Penetration Rate of NOA-enabled Passenger Car Models in China, 2022-2024H1
    • 1.1.2 Sales and Penetration Rate of Models Equipped with Highway NOA (by OEM Type)
    • 1.1.3 Sales and Penetration Rate of Models Equipped with Highway NOA (by Price Range)
    • 1.1.4 Sales and Penetration Rate of Models Equipped with Highway NOA (by Auto Brand)
    • 1.1.5 Sales and Penetration Rate of Models Equipped with Highway NOA (by Energy Type)
    • 1.1.6 Sales and Penetration Rate of Models Equipped with Highway NOA (by Model)
    • 1.1.7 Sales and Penetration Rate of Models Equipped with Urban NOA (by OEM Type)
    • 1.1.8 Sales and Penetration Rate of Models Equipped with Urban NOA (by Price Range)
    • 1.1.9 Sales and Penetration Rate of Models Equipped with Urban NOA (by Auto Brand)
    • 1.1.10 Sales and Penetration Rate of Models Equipped with Urban NOA (by Energy Type)
    • 1.1.11 Sales and Penetration Rate of Models Equipped with Urban NOA (by Model)
  • 1.2 Sensor Solutions of NOA-enabled Models
    • 1.2.1 Overall Sensor Solutions of Models Equipped with Highway NOA, 2022-2024H1
    • 1.2.2 Overall Sensor Solutions of Models Equipped with Urban NOA, 2021-2023
    • 1.2.3 Sensor Solutions of Models Equipped with Urban NOA, Jan.-Jul.2024: by Auto Brand/Model
  • 1.3 NOA Solutions of Major Suppliers
    • 1.3.1 NOA Solutions of Major Chinese Suppliers (1)
    • 1.3.1 NOA Solutions of Major Chinese Suppliers (2)
    • 1.3.1 NOA Solutions of Major Chinese Suppliers (3)
    • 1.3.2 Comparison of Highway NOA Solutions between Major Chinese Suppliers
    • 1.3.3 Comparison of Urban NOA Solutions between Major Chinese Suppliers
    • 1.3.4 NOA Solutions of Major Foreign Suppliers and Their Layout in China
  • 1.4 Analysis of Major NOA Suppliers
    • 1.4.1 Market Share of Highway NOA ADAS Integrator (2023-2024H1)
    • 1.4.2 Market Share of Highway NOA ADAS Software/Algorithm Supplier (2023-2024H1)
    • 1.4.3 Market Share of Urban NOA ADAS Integrator (2023-2024H1)
    • 1.4.4 Market Share of Urban NOA ADAS Software/Algorithm and Domain Control Supplier (2023-2024H1)
    • 1.4.5 Major Suppliers of Highway Intelligent Driving and Urban Intelligent Driving SoC, 2024H1

2 Passenger Car NOA Market Trends and Discussions

  • 2.1 ADAS Moves towards Higher Level
    • 2.1.1 Penetration Rate of ADAS above L2+ Increases Rapidly
    • 2.1.2 OEMs Accelerate Implementation of NOA (1)
    • 2.1.2 OEMs Accelerate Implementation of NOA (2)
    • 2.1.3 Layered Promotion of NOA Is Current Mainstream Trend
    • 2.1.4 Extremely Cost-Effective Solutions Drive Down L2+ Costs
  • 2.2 High-level NOA Is Evolving from Highway NOA to Urban NOA
    • 2.2.1 Urban NOA Evolution Direction 1
    • 2.2.2 Urban NOA Evolution Direction 2
    • 2.2.2 "Map-free" Solution V.S. Urban NOA (1)
    • 2.2.2 "Map-free" Solution V.S. Urban NOA (2)
    • 2.2.2 "Map-free" Solution V.S. Urban NOA (3)
    • 2.2.3 "Map-free" Case 1: Huawei
    • 2.2.3 "Map-free" Case 2: Xpeng Motors
    • 2.2.4 Challenges of "Map-free"
    • 2.2.5 Choice of Map Providers under the Trend of Non-/Low-Weight HD Maps
    • 2.2.6 Urban NOA Evolution Direction 3
    • 2.2.7 End-to-end Foundation Model Case 1: Iteration of Li Auto System 1
    • 2.2.7 End-to-end Foundation Model Case 1: System 1 (End-To-End Model) + System 2 (VLM)
    • 2.2.7 End-to-end Foundation Model Case 1: Li Auto's Next-Generation Autonomous Driving Technology Architecture
    • 2.2.7 End-to-end Foundation Model Case 1: Li Auto Drive VLM Model: Architecture
    • 2.2.7 End-to-end Foundation Model Case 1: Li Auto Drive VLM Model: Rendering effect
    • 2.2.7 End-to-end Foundation Model Case 1: Li Auto Drive VLM Model: Processing of BEV and Text Features
    • 2.2.8 End-to-end Foundation Model Case 2: Zhuoyu Achieved Mass Production of Two-Stage End-To-End Technology on Medium Computing Platform
    • 2.2.9 Comparison and summary of end-to-end solutions
    • 2.2.10 Urban NOA Evolution Direction 4: Vision-only Perception Becomes One of New Development Directions in Second Half for Intelligent Driving
    • 2.2.11 Case of Using Vision-Only Perception Route to Realize Urban NOA: Jiyue Intelligent Driving Solution Evolution Route (1)
    • 2.2.11 Case of Using Vision-Only Perception Route to Realize Urban NOA: Jiyue Intelligent Driving Solution Evolution Route (2)
    • 2.2.12 Passenger Car NOA Evolution Direction 5: Memory driving (commuting NOA) is booming
    • 2.2.13 Booming Memory Driving (Commute NOA)
    • 2.2.13 Booming Memory Driving (Commute NOA)
    • 2.2.13 Booming Memory Driving (Commute NOA)
    • 2.2.14 Memory Driving (Commuting NOA) Case: Xpeng Motors
    • 2.2.15 Memory Driving (Commuting NOA) Case: Li Auto
    • 2.2.16 Memory Driving (Commuting NOA) Case: Haomo.ai
    • 2.2.17 Memory Driving (Commuting NOA) Case: DJI
    • 2.2.18 Urban NOA Business Model Has Not Yet Been Unified (1)
    • 2.2.18 Urban NOA Business Model Has Not Yet Been Unified (2)
  • 2.3 NOA Promotes the Technology Upgrade of the Industry Chain
    • 2.3.1 Development Trends of Key Industry Chain Technologies
    • 2.3.2 Cameras Are Upgraded to 8M
    • 2.3.3 4D Radar Trend Strengthens (1)
    • 2.3.3 4D Radar Trend Strengthens (2)
    • 2.3.4 LiDAR Accelerates Installation and Iteration (1)
    • 2.3.4 LiDAR Accelerates Installation and Iteration (2)
    • 2.3.5 Demand for High Computing Power Is Growing
    • 2.3.6 Driving-parking Integrated Domain Controller Helps Implement High-Level Solutions (1)
    • 2.3.6 Driving-parking Integrated Domain Controller Helps Implement High-Level Solutions (2)
    • 2.3.6 Driving-parking Integrated Domain Controller Helps Implement High-Level Solutions (3)
    • 2.3.7 Building "Supercomputing Center + Data Closed Loop" Has Become the Key to Technology Upgrade

3 Passenger Car NOA Solutions and Application of OEMs

  • 3.1 Xpeng Motors
    • 3.1.1 Development History of Autonomous Driving Team
    • 3.1.2 Overview of Autonomous Driving Evolution
    • 3.1.3 Intelligent Driving System First Half - Xpilot System Upgrade and Iteration
    • 3.1.4 Intelligent Driving System Second Half: XNGP (1)
    • 3.1.4 Intelligent Driving System Second Half: Typical Models of XNGP and Xpilot (2)
    • 3.1.4 Intelligent Driving System Second Half: Typical Models of XNGP and Xpilot (3)
    • 3.1.5 End-to-End Foundation Model (1): Architecture
    • 3.1.5 End-to-End Foundation Model (2): Intelligent Driving Model
    • 3.1.5 End-To-End Foundation Model (3): AI+XNGP
    • 3.1.5 End-to-End Foundation Model (4): Organizational Change
  • 3.2 Li Auto
    • 3.2.1 Autonomous Driving Platform Evolution Route
    • 3.2.2 Hardware Basis and Algorithm Model in the Intelligent Driving 3.0 Era
    • 3.2.3 Intelligent Driving Team and Product Development Model
    • 3.2.4 ADAS Iteration Route (1)
    • 3.2.4 ADAS Iteration Route (2)
    • 3.2.5 Iterative Process of NOA
  • 3.3 NIO
    • 3.3.1 Intelligent Driving Business Layout
    • 3.3.2 Full- Stack Autonomous Driving Technology
    • 3.3.3 Iterative Process of Intelligent Driving System
    • 3.3.4 Next-generation Intelligent Driving System
    • 3.3.5 NOP Iteration
    • 3.3.6 Highway NOP+
    • 3.3.7 Global NOP+
    • 3.3.7 Global NOP+: General Generalization Capabilities (1)
    • 3.3.7 Global NOP+: General Generalization Capabilities (2)
    • 3.3.7 Global NOP+: Swarm Intelligence System (1)
    • 3.3.7 Global NOP+: Swarm Intelligence System (2)
    • 3.3.8 Dynamics in Intelligent Driving System OTA
  • 3.4 IM Motors
    • 3.4.1 Intelligent Driving Business Layout
    • 3.4.2 Intelligent Driving System and Planning
    • 3.4.3 Capabilities of Next-generation IM AD Intelligent Driving System (1)
    • 3.4.3 Capabilities of Next-generation IM AD Intelligent Driving System (2)
    • 3.4.4 Development History and Planning of NOA
    • 3.4.5 Application Cases of NOA
  • 3.5 AITO
    • 3.5.1 Intelligent Driving Business Layout
    • 3.5.2 Intelligent Driving System Iteration
    • 3.5.3 ADS2.0 System
    • 3.5.4 ADS2.0 NCA Typical Models
    • 3.5.5 ADS3.0 NCA Typical Models
  • 3.6 BYD
    • 3.6.1 ADAS Team
    • 3.6.2 Intelligent Driving System Iteration
    • 3.6.3 "Eye of God" High-level Intelligent Driving System
    • 3.6.4 ADAS Development History
    • 3.6.5 ADAS System Typical Model 1: Denza N7 (1)
    • 3.6.5 ADAS System Typical Model 1: Denza N7 (2)
    • 3.6.5 ADAS System Typical Model 2: Yangwang U8
    • 3.6.6 High-level Intelligent Driving Technology Strategy Trend
  • 3.7 GAC
    • 3.7.1 Development History of Intelligent Driving Business
    • 3.7.2 Intelligent Driving Business Layout: R&D and Production
    • 3.7.2 Intelligent Driving Business Layout: Investment and Cooperation
    • 3.7.3 Intelligent Driving System Iteration
    • 3.7.4 ADiGO 4.0
    • 3.7.5 Typical Models Equipped with NOA
  • 3.8 Geely Automobile
    • 3.8.1 Intelligent Driving Business Layout
    • 3.8.2 ADAS Technology Layout: Xingrui Intelligent Computing Center
    • 3.8.3 Xingrui AI Foundation Model
    • 3.8.4 Application of Intelligent Driving Foundation Model Technology
    • 3.8.5 ADAS Development Roadmap: Autonomous Driving & Automated Parking
    • 3.8.6 Dirive safe 2.0
    • 3.8.7 Typical Model Case 1: Zeekr 001 Intelligent Driving Solution Evolution Route
    • 3.8.8 Typical Model Case 2: Comparison of Intelligent Driving Solutions for Typical Models of Other Brands under Geely
  • 3.9 Changan Automobile
    • 3.9.1 ADAS Strategic Planning
    • 3.9.2 ADAS Strategy: "Beidou Tianshu" Strategy
    • 3.9.3 ADAS Function Development History
    • 3.9.4 Intelligent Driving Technology Strategy
    • 3.9.5 SDA Architecture
    • 3.9.6 Fourth Generation Intelligent Driving Self-developed Platform Iteration
    • 3.9.7 Deepen Cooperation with Huawei, Avatr and Deepal Become the Key Fulcrum
    • 3.9.8 ADAS Typical Models: L2.9, Deepal SL03 & Avatr 12
  • 3.10 Leapmotor
    • 3.10.1 LEAP Platform Architecture (1)
    • 3.10.1 LEAP Platform Architecture (2)
    • 3.10.1 LEAP Platform Architecture (3)
    • 3.10.2 Self-developed Autonomous Driving Technology (1)
    • 3.10.2 Self-developed Autonomous Driving (2)
    • 3.10.3 Intelligent Driving Evolution Path (1)
    • 3.10.3 Intelligent Driving Evolution Path (2)
    • 3.10.4 Leapmotor Pilot
  • 3.11 Tesla
    • 3.11.1 ADAS (1)
    • 3.11.1 ADAS (2)
    • 3.11.2 Iteration of FSD System (1)
    • 3.11.2 Iteration of FSD System (2)
    • 3.11.2 Iteration of FSD System (3)
    • 3.11.3 Core Intelligent Driving Capabilities: Algorithms (1)
    • 3.11.3 Core Intelligent Driving Capabilities: Algorithms (2)
    • 3.11.3 Core Intelligent Driving Capabilities: Algorithms (3)
    • 3.11.3 Core Intelligent Driving Capabilities: Dojo Supercomputing Center (1)
    • 3.11.3 Core Intelligent Driving Capabilities: Dojo Supercomputing Center (2)
    • 3.11.3 Core Intelligent Driving Capabilities: Dojo Supercomputing Center (3)
    • 3.11.4 Dynamics in Intelligent Driving System OTA
    • 3.11.5 Layout in China

4 Domestic Suppliers Passenger Car NOA Program

  • 4.1 Desay SV
    • 4.1.1 Profile
    • 4.1.2 Overview of Operations, 2023
    • 4.1.3 Overview of R&D Investment, 2023
    • 4.1.4 Supply Chain Distribution and Core Customers
    • 4.1.5 Intelligent Driving Layout
    • 4.1.6 Intelligent Driving Sensor
    • 4.1.7 Radar Products and Technology Product Line
    • 4.1.8 Intelligent Driving Domain Controller (1)
    • 4.1.8 Intelligent Driving Domain Controller (2)
    • 4.1.9 Central Computing Platform
    • 4.1.10 Intelligent Driving Decision Layout
    • 4.1.11 Intelligent Driving Solutions
    • 4.1.12 Smart Solution
    • 4.1.13 Main Customers
  • 4.2 Jingwei Hirain
    • 4.2.1 Profile
    • 4.2.2 Overview of Operations, 2023
    • 4.2.3 Intelligent Driving Layout
    • 4.2.4 Driving-parking Integration Products
    • 4.2.5 Driving-parking Integrated Domain Controller: ADCU
    • 4.2.6 High Performance Computing Platform HPC
    • 4.2.7 Central Computing Platform and Zone Controller (1)
    • 4.2.7 Central Computing Platform and Zone Controller (2)
    • 4.2.8 Intelligent Driving Software & Algorithms
    • 4.2.9 ADAS Solution
    • 4.2.10 Partners
  • 4.3 Freetech
    • 4.3.1 Profile
    • 4.3.2 Core Intelligent Driving Capabilities (1)
    • 4.3.2 Core Intelligent Driving Capabilities (2)
    • 4.3.2 Core Intelligent Driving Capabilities (3)
    • 4.3.2 Core Intelligent Driving Capabilities (4)
    • 4.3.3 Intelligent Driving Solution Roadmap (1)
    • 4.3.3 Intelligent Driving Solution Roadmap (2)
    • 4.3.4 L2 Driving-parking Integrated Solutions
    • 4.3.5 L2+ Driving-parking Integrated Solutions
    • 4.3.6 L2.5 Driving-parking Integrated Solutions
    • 4.3.7 L2.9 Driving-parking Integrated Solutions
    • 4.3.8 L3/L3+ Driving-parking Integrated Solutions
    • 4.3.9 Intelligent Driving Partners
    • 4.3.10 Dynamics
  • 4.4 Huawei
    • 4.4.1 Profile
    • 4.4.2 Business of Intelligent Automotive Solution (IAS) Business Unit (BU) (1)
    • 4.4.2 Business of Intelligent Automotive Solution (IAS) Business Unit (BU) (2)
    • 4.4.3 ADS Full -Stack Solution
    • 4.4.4 ADS2.0
    • 4.4.5 Differences between ADS2.0 and ADS1.0: Sensors
    • 4.4.6 Comparison of ADS1.0 and 2.0 (1)
    • 4.4.6 Comparison of ADS1.0 and 2.0 (2)
    • 4.4.7 ADS 2.0 Algorithm
    • 4.4.8 ADS 2.0 Progress
    • 4.4.9 ADS2.0: Intelligent Parking Capability
    • 4.4.10 ADS2.0: Obstacle Recognition Capability
    • 4.4.11 Features of ADS 2.0 (1)
    • 4.4.11 Features of ADS 2.0 (2)
    • 4.4.11 Features of ADS 2.0 (3)
    • 4.4.11 Features of ADS 2.0 (4)
    • 4.4.12 Huawei ADS 3.0 (1)
    • 4.4.12 Huawei ADS 3.0 (2): End-to-end
    • 4.4.12 Huawei ADS 3.0 (3): ASD3.0 VS. ASD2.0
    • 4.4.13 ADS 3.0 Implementation Case (1): STELATO S9
    • 4.4.13 ADS 3.0 Implementation Case (2): Luxeed R7
  • 4.5 Baidu Apollo
    • 4.5.1 Profile
    • 4.5.2 Strategic Layout in Intelligent Driving
    • 4.5.3 Business Model (1)
    • 4.5.3 Business Model (2)
    • 4.5.4 Intelligent Driving Technology
    • 4.5.5 Intelligent Product Matrix Layout
    • 4.5.6 ACU Computing Platform
    • 4.5.7 Algorithm + Chip Layout (1)
    • 4.5.7 Algorithm + Chip Layout (2)
    • 4.5.7 Algorithm + Chip Layout (3)
    • 4.5.7 Algorithm + Chip Layout (4)
    • 4.5.7 Algorithm + Chip Layout (5)
    • 4.5.7 Algorithm + Chip Layout (6)
    • 4.5.7 Algorithm + Chip Layout (7)
    • 4.5.7 Algorithm + Chip Layout (8)
    • 4.5.8 Intelligent Driving Solutions
    • 4.5.9 City Driving Max (1)
    • 4.5.9 City Driving Max (2)
    • 4.5.10 Apollo Self-Driving - ASD
    • 4.5.11 Intelligent Driving Hardware Configuration Solution - Perception
    • 4.5.1 2 L4 Commercial Implementation Progress (1)
    • 4.5.1 2 L4 Commercial Implementation Progress (2)
    • 4.5.1 3 Intelligent Driving Business Partners
  • 4.6 (formerly DJI)
    • 4.6.1 Profile
    • 4.6.2 R&D and Production
    • 4.6.3 Development History of Intelligent Driving Business
    • 4.6.4 Full-Scenario Intelligent Driving Solution
    • 4.6.5 Iteration of OSMO Intelligent Driving Technology
    • 4.6.6 OSMO Intelligent Driving System 2.0
    • 4.6.6 OSMO Intelligent Driving System 2.0: Core Capabilities (1)
    • 4.6.6 OSMO Intelligent Driving System 2.0: Core Capabilities (2)
    • 4.6.6 OSMO Intelligent Driving System 2.0: Core Capabilities (3)
    • 4.6.6 OSMO Intelligent Driving System 2.0: Core Capabilities (4)
    • 4.6.6 OSMO Intelligent Driving System 2.0: Main Functions
    • 4.6.7 High-level Intelligent Driving System Deployment Strategy
    • 4.6.8 Application Cases of L2 Solutions
    • 4.6.9 Application Cases of L2+ Solutions (1)
    • 4.6.9 Application Cases of L2+ Solutions (2)
    • 4.6.10 Partners and Dynamics
  • 4.7 Haomo.AI
    • 4.7.1 Profile
    • 4.7.2 Business Model
    • 4.7.3 Main Business
    • 4.7.4 Iterative Roadmap of HPilot System
    • 4.7.5 First-generation HPilot System (1)
    • 4.7.5 First-generation HPilot System (2)
    • 4.7.5 First-generation HPilot System: HP350 Solution
    • 4.7.5 First-generation HPilot System: HP550 Solution
    • 4.7.6 Second Generation HPilot System
    • 4.7.7 Models with Intelligent Driving
    • 4.7.8 Customers and Partners
  • 4.8 Momenta
    • 4.8.1 Profile
    • 4.8.2 Autonomous Driving Strategy
    • 4.8.3 L2+ Autonomous Driving Solution: Mpilot (1)
    • 4.8.3 L2+ Autonomous Driving Solution: Mpilot (2)
    • 4.8.4 Launches High-level Intelligent Driving Solutions Based on NVIDIA Chips
    • 4.8.5 Map-Free Intelligent Driving Solution
    • 4.8.5 Map-Free Intelligent Driving Solution
    • 4.8.6 Dynamics
  • 4.9 Yihang.AI
    • 4.9.1 Profile
    • 4.9.2 Business Model
    • 4.9.3 Autonomous Driving Solutions
    • 4.9.3 L2.5 Solution 1: Lite Edition (Single SoC)
    • 4.9.3 L2.5 Solution 2: Lite Edition (Single SoC)
    • 4.9.4 L2.5 Solution 1: Ultimate Edition (Dual SoC)
    • 4.9.4 L2.5 Solution 2: Ultimate Edition (Dual SoC)
    • 4.9.4 L2.5 Solution 3: Ultimate Edition (Dual SoC)
    • 4.9.5 All-scenario FSD Solution: Dual Orin-X/Dual J5
    • 4.9.5 All-scenario FSD Solution: Dual Orin-X/Dual J5
    • 4.9.6 Partners and Dynamics
  • 4.10 Hongjing Drive
    • 4.10.1 Profile
    • 4.10.2 Company Development History and Popular Mass-produced Models
    • 4.10.3 Business Layout
    • 4.10.4 Business Model
    • 4.10.5 Product Roadmap
    • 4.10.6 Camera All-in-One Solution: IPM1.0-J2&J3 All-in-One
    • 4.10.7 Domain Controller: HDC 1.0 - Single J3 Domain Controller
    • 4.10.8 Software Algorithm Platform (1)
    • 4.10.8 Software Algorithm Platform (2)
    • 4.10.9 Intelligent Driving Solutions
    • 4.10.10 Lightweight Driving-Parking Integrated Solutions
    • 4.10.11 High-level Intelligent Driving System Solutions
    • 4.10.12 Main Partners
  • 4.11 NavInfo
    • 4.11.1 Profile
    • 4.11.2 Operations
    • 4.11.3 Intelligent Driving Solution (1)
    • 4.11.3 Intelligent Driving Solution (2)
    • 4.11.4 Driving-Parking Integrated Solution (L2.5) (1)
    • 4.11.4 Driving-Parking Integrated Solution (L2.5) (2)
    • 4.11.5 Partners and Dynamics
  • 4.12 SenseTime
    • 4.12.1 Profile
    • 4.12.2 SenseAuto Intelligent Driving Solution
    • 4.12.3 SenseAuto L2 Intelligent Driving Solution
    • 4.12.4 SenseAuto L2.5 Intelligent Driving Solution
    • 4.12.5 SenseAuto L2.9 Intelligent Driving Solution
    • 4.12.6 SenseAuto Intelligent Driving Capabilities (1)
    • 4.12.6 SenseAuto Intelligent Driving Capabilities (2)
    • 4.12.7 Automotive Partners
  • 4.13 Horizon Robotics
    • 4.13.1 Key Nodes of Intelligent Driving Layout
    • 4.13.2 Computing Platform - Matrix(R) 2
    • 4.13.2 Computing Platform - Matrix(R) 5
    • 4.13.3 Autonomous Driving Products and Solutions
    • 4.13.4 L2+ Solution: Horizon Matrix Mono
    • 4.13.5 L2.5 Solution: Horizon Matrix Pilot 3 (1)
    • 4.13.5 L2.5 Solution: Horizon Matrix Pilot 3 (2)
    • 4.13.6 Horizon Matrix(R) SuperDrive (1)
    • 4.13.6 Horizon Matrix(R) SuperDrive (2)
    • 4.13.7 All-Scenario Intelligent Driving Solution: SuperDrive2.0
  • 4.14 Neusoft Reach
    • 4.14.1 Profile
    • 4.14.2 Autonomous Driving Product Matrix
    • 4.14.3 Intelligent Driving Solutions
    • 4.14.4 L2++ Intelligent Driving Solution
    • 4.14.5 Central Computing Platform (1)
    • 4.14.5 Central Computing Platform (2)
    • 4.14.6 Core Intelligent Driving Capabilities (1)
    • 4.14.6 Core Intelligent Driving Capabilities (2)
    • 4.14.6 Core Intelligent Driving Capabilities (3)
  • 4.15 MAXIEYE
    • 4.15.1 Profile
    • 4.15.2 Intelligent Driving Business and Planning
    • 4.15.3 Intelligent Driving Solutions
    • 4.15.4 L2 Intelligent Driving System (1)
    • 4.15.4 L2 Intelligent Driving System (2)
    • 4.15.5 L2.5 Intelligent Driving System (1)
    • 4.15.5 L2.5 Intelligent Driving System (2)
    • 4.15.5 L2.5 Intelligent Driving System (3)
    • 4.15.6 Core Capabilities of High-level Intelligent Driving System
    • 4.15.7 Autonomous Driving Cooperation Updates
  • 4.16 iMotion
    • 4.16.1 Profile
    • 4.16.2 Overview of Operations, 2023
    • 4.16.3 Business Model (1)
    • 4.16.3 Business Model (2)
    • 4.16.4 Product Strategic Roadmap Planning
    • 4.16.5 Camera Products
    • 4.16.6 Domain Controller Products (1)
    • 4.16.6 Domain Controller Products (2)
    • 4.16.7 Intelligent Driving Solutions (1)
    • 4.16.7 Intelligent Driving Solutions (2)
    • 4.16.8 Main Partners
  • 4.17 Nullmax
    • 4.17.1 Profile
    • 4.17.2 Intelligent Driving Product Development Roadmap
    • 4.17.3 Main Products
    • 4.17.4 MaxDrive Intelligent Driving Solution
    • 4.17.5 Core Intelligent Driving Capabilities (1)
    • 4.17.5 Core Intelligent Driving Capabilities (2)
    • 4.17.5 Core Intelligent Driving Capabilities (3)
    • 4.17.6 Partners and Dynamics
  • 4.18 ZongMu Technology
    • 4.18.1 Profile
    • 4.18.2 Overview of Operations, 2023
    • 4.18.3 Strategic Layout
    • 4.18.4 Intelligent Driving Layout
    • 4.18.5 Intelligent Driving Solutions
    • 4.18.6 Solution 1: Amphiman 3000 (1)
    • 4.18.6 Solution 1: Amphiman 3000 (2)
    • 4.18.6 Solution 1: Amphiman 3000 (3)
    • 4.18.7 Solution 2: Amphiman 500 0
    • 4.18.8 Solution 3: Amphiman 8000
    • 4.18.9 Solution 4: Cockpit-Driving-Parking Integration
    • 4.18.10 Solution 5: Drop'nGo
    • 4.18.11 Partners and Dynamics
  • 4.19 AutoBrain
    • 4.19.1 Profile
    • 4.19.2 Intelligent Driving Products and Solutions
    • 4.19.3 L2.5 Intelligent Driving Solution
    • 4.19.4 Core Intelligent Driving Capabilities (1)
    • 4.19.4 Core Intelligent Driving Capabilities (2)
    • 4.19.5 Partners and Dynamics
  • 4.20 QCraft
    • 4.20.1 Profile
    • 4.20.2 "Dual Engine Strategy"
    • 4.20.3 Intelligent Driving Product Matrix
    • 4.20.4 Robobus Layout
    • 4.20.5 Mid- ad High-level Intelligent Driving Products and Solutions - Based on ZE3(R) 6 (1)
    • 4.20.5 Mid- ad High-level Intelligent Driving Products and Solutions - Based on ZE3(R) 6 (2)
    • 4.20.6 Mid- ad High-level Intelligent Driving Products and Solutions - Based on ZE3(R) 5 (1)
    • 4.20.6 Mid- ad High-level Intelligent Driving Products and Solutions - Based on ZHENG(R) 5 (2)
    • 4.20.7 Core Intelligent Driving Technology (1)
    • 4.20.7 Core Intelligent Driving Technology (2)
    • 4.20.8 Partners
  • 4.21 DeepRoute
    • 4.21.1 Profile
    • 4.21.2 Business Layout
    • 4.21.3 High-level Intelligent Driving Solutions
    • 4.21.4 L2++ High-level Intelligent Driving Solution (1)
    • 4.21.4 L2++ High-level Intelligent Driving Solution (2)
    • 4.21.5 Core High-level Intelligent Driving Technology
    • 4.21.6 Partners and Dynamics
  • 4.22 Pony.ai
    • 4.22.1 Profile
    • 4.22.2 Development History
    • 4.22.3 Business Model
    • 4.22.4 Intelligent Driving Solutions for Passenger Cars: Pony Shitu
    • 4.22.5 Intelligent Driving Solutions for Passenger Cars: Fangzai
    • 4.22.6 Intelligent Driving Solutions for Passenger Cars: Cangqiong
    • 4.22.7 L2.5 Intelligent Driving Solution (1)
    • 4.22.7 L2.5 Intelligent Driving Solution (2)
    • 4.22.8 L4 Commercial Implementation Progress

5 Foreign Suppliers' Passenger Car NOA Program

  • 5.1 Bosch
    • 5.1.1 Profile
    • 5.1.2 Overview of Operations, 2023
    • 5.1.3 Bosch China Strategic Layout (1)
    • 5.1.3 Bosch China Strategic Layout (2)
    • 5.1.3 Bosch China Strategic Layout (3)
    • 5.1.3 Bosch China Strategic Layout (4)
    • 5.1.3 Bosch China Strategic Layout (5)
    • 5.1.3 Bosch China Strategic Layout (6)
    • 5.1.4 Autonomous Driving Product Matrix
    • 5.1.5 Intelligent Driving Solutions and Planning
    • 5.1.6 High-level Intelligent Driving System (L2++) Software and Hardware
  • 5.2 Continental
    • 5.2.1 Profile
    • 5.2.2 Overview of Operations, 2023
    • 5.2.3 Layout in China (1)
    • 5.2.4 Full- Stack Intelligent Driving Solution Map
    • 5.2.5 Intelligent Driving Solutions and Planning
    • 5.2.6 Intelligent Driving Business Layout in China
    • 5.2.7 Layout in China
  • 5.3 ZF
    • 5.3.1 Profile
    • 5.3.2 Overview of Operations, 2023
    • 5.3.3 Autonomous Driving Strategy (1)
    • 5.3.3 Autonomous Driving Strategy (2): Launching the Chinese Version of "ProAI"
    • 5.3.3 Autonomous Driving Strategy (3): Automated Parking
    • 5.3.4 ZF L2++ Intelligent Driving Solution
  • 5.4 Aptiv
    • 5.4.1 Profile
    • 5.4.2 Overview of Operations, 2023
    • 5.4.3 Autonomous Driving Layout
    • 5.4.4 Overall Strategic Layout in China (1)
    • 5.4.5 Product Layout in China (1)
    • 5.4.5 Product Layout in China (2)
    • 5.4.6 Decision Products - Domain Controller/Multi- Domain Computing Platform
    • 5.4.7 Solution - Driving-Parking Integration (1)
    • 5.4.7 Solution - Driving-Parking Integration (2)
  • 5.5 Mobileye
    • 5.5.1 Profile
    • 5.5.2 Main Products and Services
    • 5.5.3 Intelligent Driving Product Route
    • 5.5.4 Intelligent Driving Solutions
    • 5.5.5 Level 2+ Autonomous Driving Solution: SuperVision
    • 5.5.6 L3/L4 Autonomous Driving Solution: Chauffeur Solution
    • 5.5.7 Core Intelligent Driving Technology
    • 5.5.7 Core Intelligent Driving Technology: EyeQ Chip
    • 5.5.7 Core Intelligent Driving Technology: REM (Road Network Information Management - Visual Crowdsourcing HD Map Drawing)
    • 5.5.7 Core Intelligent Driving Technology: True Redundancy (TR)
    • 5.5.7 Core Intelligent Driving Technology: Vision and Radar Algorithms (1)
    • 5.5.7 Core Intelligent Driving Technology: Vision and Radar Algorithms (2)
    • 5.5.7 Core Intelligent Driving Technology: RSS (Responsibility Sensitive Safety Model)
    • 5.5.7 Core Intelligent Driving Technology: EyeQ Kit
    • 5.5.8 Customers and partners