Chinese Independent OEMs’ ADAS and Autonomous Driving Report, 2023
  • Oct.2023
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1. Wide adoption of NOA begins, and local brands grab market share.

According to ResearchInChina, from January to August 2023, joint venture brands accounted for 3.0% of installations of L2.5 and higher-level systems, mainly driven by Tesla; the proportion of independent brands was 3.7%, up 1.8 percentage points from 1.9% in 2022. From January to August 2023, independent brands took up 1.9% of installations of L2.9 systems, up 1 percentage point from that in 2022. Urban NOA is mainly available to Li Auto, NIO, Avatr, AITO and Xpeng.

自主ADAS 1_副本.png

As per the models and plans released by automakers, independent brands are working to deploy NOA, and they are beginning to reduce the price and improve the configuration, which means they intend to occupy the market ahead of others, by virtue of "cost performance”.

For example, the new AITO M7, launched in September 2023, is priced at RMB249,800-329,800 (the price of the old model is RMB319,800-379,800). The new M7, equipped with 27 sensors including a roof LiDAR, 3 radars, 11 high-definition cameras and 12 ultrasonic radars, supports Huawei ADS 2.0 and enables high-level intelligent driving on highways and in urban areas without HD maps. Up to now, AITO has realized the commercialization of NOA in six cities without using maps, which is expected to available to up to 45 cities in the fourth quarter.

In October 2023, IM LS6 (including four editions) was launched on market. Equipped with the IM AD intelligent driving system, it is priced at RMB229,900-291,900 (limited-time offer: RMB214,900-276,900). The IM AD system valued at RMB36,800 (NVIDIA OrinX, a LiDAR, 3 radars, 11 cameras and 12 ultrasonic radars) features highway NOA and urban NOA (some functions are realized via OTA updates).

According to IM's plan, the urban NOA on IM LS6 will be tested on public roads at the end of 2023, or will be launched before the 2024 Spring Festival, first available in Shanghai. In mid-2024, “non-map” urban NOA may be implemented; within 2024, the commuting mode will cover 100 key cities across China.

自主ADAS 2_副本.png

2. In view of the difficulty of all-scenario urban NOA, many OEMs start with commute NOA.

Commute NOA, also known as urban memory driving, tailors the "urban driving assistance" route according to users' mobility habits. Compared with urban NOA, commute NOA can be trained on a single vehicle. It can achieve the vision of 99% autonomous driving on fixed routes based on the user's driving routes and memorized trajectories. Li Auto revealed that simple routes can be trained in one week, and complicated routes take 2-3 weeks.

自主ADAS 3_副本.png

3. Independent conventional OEMs compete for talents to enhance the technical capabilities of their intelligent driving teams.

In the process of upgrading from L2 to L3 intelligent driving, the technical capabilities of the original intelligent driving teams of independent conventional OEMs can't keep up with the development trend of industrial technologies. Therefore they have poached technical experts from bellwethers, technology companies, and emerging carmakers to improve the technology level of their intelligent driving teams.   

For example, in August 2023, BYD invited Liao Jie, the former Intelligent Driving R&D Director of Horizon Robotics, to serve as the head of BYD’s intelligent driving team in Shanghai. In September 2023, Tao Ji, the former CEO of Autra.tech, a L4 truck company, joined Changan Automobile to take in charge of intelligent driving technology. Tao Ji used to work with Baidu as the general manager of the autonomous driving division of Intelligent Driving Group (IDG) and the general manager of intelligent transportation product research and development. He participated in the entire founding process of Baidu's autonomous driving project team from 0 to 1.

自主ADAS 4_副本.png

4. The upstream and downstream of the industry chain jointly promote solutions with optimal performance and cost.

HD maps have low city coverage, high collection cost and unstable update frequency, which are insoluble problems for the industry, so major OEMs have reached an industry consensus on “low-weight map” solutions.

In August 2023, IM Motors and Momenta released a solution based on Data Driven Landmark Detection (DDLD) technology without using HD maps. The DDLD model can replace HD maps, construct maps in real time during driving, integrate the road features recognized in multiple mappings to generate road topology, and predict road network information that is difficult to observe with conventional perception algorithms. The solution was first mounted on IM LS6, and used for NOA public beta in September 2023 without HD maps.

自主ADAS 5_副本.png

In addition, Tier 1 suppliers are actively introducing new solutions to reduce the cost of sensor solutions.

In April 2023, DJI released a thousand-yuan intelligent driving solution which uses 7V/9V vision-only configuration to achieve L2+ intelligent driving functions, including urban memory driving (32TOPS)/urban NOA (80TOPS) through "strong visual online real-time perception”, without relying on HD maps or LiDAR”. In September 2023. The 7V solution was launched on market with the Linxi Edition of Baojun Yunduo 460 Pro priced at RMB125,800. Thus high-level intelligent driving functions are popularized to mainstream RMB100,000 family cars. After several years of low-profile development, DJI's designated projects surged in 2023. It is estimated that more than 20 cars models will carry intelligent driving products from DJI by the end of 2024.

In October 2023, Haomo.AI released three "cost-effective" driving-parking integrated products - HP170 (5TOPS), HP370 (32TOPS) and HP570 (72TOPS or 100TOPS), which enable non-map highway NOH, city memory driving, and all-scenario non-map urban NOH, with the price of RMB3,000, RMB5,000 and RMB8,000 respectively.

自主ADAS 6_副本.png

In general, the "involution" in the NOA market has stimulated OEMs to quickly implement high-level driving assistance for greater competitive edges. However, the high-level intelligent driving technology is highly complex. In a short window period, OEMs with insufficient self-development capabilities will prefer large Tier 1 suppliers like Huawei and DJI, which have enough mass production experience, and advanced and mature technologies.

ADAS Rating
ADAS Function Definition
1 Status Quo of Chinese Independent Brands’ ADAS Market
1.1 ADAS Installations and Installation Rate:  By Function Level
1.2 Structure of Chinese Independent Brands by ADAS Level
1.3 ADAS Installations and Installation Rate of Chinese Independent Brands:  By Function
1.4 L2/L2+ ADAS Installations and Installation Rate:  Overall Situation
1.4.1 L2/L2+ ADAS Installations and Installation Rate:  By Brand
1.4.2 L2/L2+ ADAS Installation Rate:  By Brand
1.5 L2/L2.5/L2.9 ADAS Installations:  By Brand
1.6 L2/L2.5/L2.9 ADAS Installations:  By Model
1.7 L2/L2+/L2.5/L2.9 ADAS Installations:  By Price Range
1.8 L2/L2+/L2.5/L2.9 ADAS Installation Rate:  By Price Range
1.9 L2.5/L2.9 ADAS Installations:  By Price Range + Model (2022)
1.10 L2.5/L2.9 ADAS Installations:  By Price Range + Model (2023)
1.11 L2.5/L2.9 ADAS Installations and Installation Rate:  By Price Range 

2 ADAS and Autonomous Driving Layout of Chinese Independent Brands 
2.1 ADAS/AD Implementation Plan of Chinese Independent Brands
2.2 ADAS/AD Implementation Plan of Chinese Independent Emerging Brands
2.3 Comparison of L2/L2.5/L2.9 ADAS Solutions
2.4 Partner Camps of Chinese Independent OEMs (1)
2.4 Partner Camps of Chinese Independent OEMs (2)
2.5 Partner Camps of Chinese Independent Emerging Brands
2.6 Development Trends of Autonomous Driving Layout for Independent Brands
2.6.1 Trend 1
2.6.2 Trend 2
2.6.3 Trend 3
2.6.4 Trend 4
2.7 Upgrade of Intelligent Driving Technology for Independent Brands
2.7.1 Upgrade of Intelligent Driving Technology: EE Architecture
2.7.2 Upgrade of Intelligent Driving Technology (2)
2.7.3 Upgrade of Intelligent Driving Technology (3)
2.7.4 Upgrade of Intelligent Driving Technology (4)
2.8 Upgrade of Intelligent Driving System for Independent Brands
2.8.1 Upgrade of Intelligent Driving System (1)
2.8.2 Upgrade of Intelligent Driving System (2)
2.8.3 Upgrade of Intelligent Driving System (3)
2.9 Development Trends of Autonomous Driving for Independent Emerging Brands
2.9.1 Trend 1
2.9.1 Trend 1
2.9.2 Trend 2
2.9.3 Trend 3
2.9.4 Trend 4
2.9.5 Trend 5
2.9.6 Trend 6 

3 Research on ADAS/Autonomous Driving of Chinese Independent Brands 
3.1 Changan Automobile
3.1.1 Changan’s ADAS Strategic Planning
3.1.2 ADAS/AD Strategy:  "Beidou Tianshu" strategy (August 2018)
3.1.2 ADAS/AD Strategy:  123 Strategy (August 2021)- Business Model
3.1.2 ADAS/AD Strategy:  123 Strategy (August 2021) - Ark Architecture
3.1.2 ADAS/AD Strategy:  123 Strategy (August 2021) - SDA
3.1.2 ADAS Strategy: 123 Strategy - SDA - L3 
3.1.2 ADAS Strategy: 123 Strategy - SDA - L4
3.1.2 ADAS Strategy: 123 Strategy - SDA - L4 Layout
3.1.2 ADAS Strategy: 123 Strategy - SDA - L5
3.1.2 ADAS Strategy: 123 Strategy - SDA - L6
3.1.2 ADAS Strategy: 123 Strategy - SDA - L5/L6 Layout
3.1.2 ADAS Strategy: 123 Strategy - SDA Architecture - Zhuge Intelligence (Aug 2022)
3.1.2 ADAS Strategy: 123 Strategy - SDA Landing  
3.1.2 ADAS strategy: 123 Strategy - Electric Vehicle Platform
3.1.3 Development History of ADAS Functions 
3.1.4 ADAS Roadmap
3.1.5 Typical ADAS/AD Functions
3.1.6 Typical ADAS Models: L2.9 Deepal SL03 & Avatr 011
3.1.7 L4 Autonomous Vehicles
3.1.8 Changan’s Autonomous Driving Tests
3.1.9 Investment and Cooperation in ADAS 
3.1.10 Overseas Layout
3.2 Great Wall Motor
3.2.1 Overall ADAS Layout
3.2.2 ADAS/AD Strategy
3.2.3 Development History of ADAS
3.2.4 ADAS: HPilot
3.2.4 ADAS: HPilot 3.0
3.2.5 Typical ADAS Function
3.2.6 Typical Models with ADAS: L2.9 WEY Lanshan & Tank 500
3.2.7 ADAS Technology Layout: EEA (1)
3.2.7 ADAS Technology Layout: EEA (2)
3.2.8 ADAS Technology Layout of Haomo.AI
3.2.9 Autonomous Driving Chip Layout
3.2.10 Dynamic ADAS Layout - Intelligence Crowd Creativity Platform
3.2.10 Dynamic ADAS Layout
3.2.11 Overseas Layout
3.3 BYD
3.3.1 Development History of ADAS
3.3.2 Overall ADAS Layout 
3.3.3 ADAS Responsible Team
3.3.4 ADAS Roadmap
3.3.5 ADAS: DiPilot & DNP
3.3.5 ADAS: Eyes of the God
3.3.6 Typical ADAS-enabled Vehicle: Denza N7 
3.3.7 ADAS Hardware Layout
3.3.8 ADAS Software Layout
3.3.9 ADAS Algorithm Layout
3.3.9 ADAS Algorithm Layout: Planning & Decision Algorithm
3.3.10 Autonomous Driving Tests
3.3.11 ADAS Cooperative Ecosystem Layout
3.3.12 Overseas Layout
3.4 FAW
3.4.1 ADAS Path Planning 
3.4.2 ADAS Development Strategy
3.4.3 ADAS R&D Layout
3.4.4 ADAS Technology Layout
3.4.5 Typical ADAS/AD-Enabled Models: L2 - Hongqi HS7 and Besturn E01
3.4.5 Typical ADAS/AD-Enabled Models: L2.5 - Hongqi E-HS9
3.4.5 Typical ADAS/AD-Enabled Models: L4 - Robotaxi
3.4.5 Typical ADAS/AD-Enabled Models:  L4 - Hongqi Electric Minibus
3.4.6 ADAS/AD Road Tests: Demonstration Bases
3.4.6 ADAS Road Test: Public Road
3.4.7 Intelligent Driving Simulation Test
3.4.8 ADAS Investment and Cooperation
3.4.9 Overseas Layout
3.5 Geely
3.5.1 ADAS Strategic Planning 
3.5.2 ADAS/AD Strategic Plan: Smart Geely 2025 Strategy
3.5.3 ADAS Self-development Path 
3.5.4 ADAS Technology Layout
3.5.5 ADAS Roadmap: Autonomous Driving & Automated Parking
3.5.6 ADAS Technology Route
3.5.7 Typical ADAS/AD Technologies
3.5.8 Typical ADAS/AD-Enabled Models:L2 - Geely Xingyue L and Lynk & Co 03
3.5.8 Typical ADAS/AD-Enabled Models:L2.5 - Lynk & Co 01/05
3.5.8 Typical ADAS-Enabled Models: L2.9 - Zeekr 001 and Boyue L
3.5.9 Autonomous Driving Test
3.5.10 Commercial Vehicle Intelligent Driving Layout
3.5.11 ADAS Partners
3.5.12 ADAS Investment and Cooperation
3.5.13 Layout Dynamics in ADAS/AD
3.5.14 Overseas Layout
3.6 GAC
3.6.1 ADAS Roadmap 
3.6.2 ADAS/AD Strategy Plan: "1615" Strategy (Nov.2020)
3.6.3 ADAS Team 
3.6.4 ADAS/AD Technology Layout
3.6.5 Evaluation Roadmap of ADAS/AD Solution: ADiGO
3.6.6 ADAS/AD Systems: ADiGO
3.6.6 ADAS/AD Systems: ADiGO 3.0
3.6.6 ADAS/AD Systems: ADiGO 4.0
3.6.7 L4/L5 Autonomous Driving Layout 
3.6.8 L4/L5 Autonomous Driving Commercialization Progress
3.6.9 Autonomous Driving Tests
3.6.10 Investment and Cooperation in ADAS/AD
3.6.11 Overseas Layout
3.7 BAIC
3.7.1 Development Course of Autonomous Driving
3.7.2 ADAS/AD Strategy
3.7.3 ADAS/AD Technology Layout
3.7.4 ADAS/AD Roadmap
3.7.5 Typical ADAS/AD-Enabled Models:L2.5,ARCFOX αT
3.7.5 Typical ADAS/AD-Enabled Models: L2.9, ARCFOX αS New HI Edition
3.7.6 Autonomous Driving Tests
3.7.7 Autonomous Driving Cooperation Layout
3.7.8 Dynamic Deployments in ADAS/AD
3.8 SAIC
3.8.1 Development Course of Intelligent Driving Self-developed Team 
3.8.2 Autonomous Driving Planning
3.8.3 ADAS/AD Technology Layout
3.8.4 Development Course of Autonomous Driving
3.8.5 ADAS/AD Roadmap
3.8.6 Typical ADAS/AD-Enabled Models: L2, Rising MARVEL-R and MG ONE
3.8.6 Typical ADAS/AD-Enabled Models:L2.5, Roewe Whale and 3rd Gen Roewe RX5
3.8.6 Typical ADAS/AD-Enabled Models: L2.9, IM L7 and Rising R7
3.8.7 Advanced Intelligent Driving Solution  
3.8.8 Advanced Intelligent Driving Solution: PP-CEM
3.8.9 Rising Auto Advanced Intelligent Driving System: RISING PILOT
3.8.10 IM: Advanced Intelligent Driving System: IM AD
3.8.11 Technical Advantage of IM AD 
3.8.12 Data Performance of IM Intelligent Driving 
3.8.13 Cooperation between IM Momenta on Intelligent Driving
3.8.14 Autonomous Driving Road Tests
3.8.15 L4 Autonomous Driving Operation Platform: Xiangdao Robotaxi
3.8.16 Autonomous Driving Commercial Vehicle: UTOPILOT
3.8.17 ADAS/AD Partners
3.8.18 Dynamic Deployments in ADAS/AD
3.8.19 Overseas Layout 
3.9 Chery
3.9.1 Development Course of Autonomous Driving
3.9.2 Intelligence Strategy:  LION (Apr.2018)
3.9.2 Intelligence Strategy:  Yaoguang 2025 Strategy 
3.9.3 ADAS/AD Technology Self-developed Layout
3.9.4 ADAS Cooperation Layout
3.9.5 ADAS/AD Roadmap
3.9.6 Typical ADAS/AD-Enabled Models: L2.9, Tiggo  9 & EXEED Stellar 
3.9.7 Dynamic Deployments in ADAS/AD
3.9.8 Overseas Layout
3.10 Dongfeng
3.10.1 Brand Strategy
3.10.2 Strategy: 14th Five-year Plan 
3.10.3 ADAS Layout/ Technology Plan: Realizing L3+ City Navigation Assisted Driving Implementation Application by 2025
3.10.4 ADAS/AD Roadmap
3.10.5 Typical ADAS/AD-Enabled Models: L2.5, Aeolus Yixuan MAX and Voyah FREE
3.10.6 L4 Business Introduction: Self-developed "Two Systems and One Platform" Technology Products
3.10.6.1 L4 Business Introduction: Autonomous Driving Pilot Project
3.10.6.2 L4 Business Introduction: Autonomous Driving Pilot System R&D
3.10.6.3 Typical ADAS/AD-Enabled Models: L4,RoboTaxi
3.10.6.4 Typical ADAS/AD-Enabled Models: L4, Autonomous Minibus Sharing-VAN Iteration
3.10.6.5 Typical ADAS/AD-Enabled Models: L4, Autonomous Minibus Sharing-VAN 1.0 Plus
3.10.6.6 Typical ADAS/AD-Enabled Models: L4, Autonomous Minibus Sharing-VAN 2.0
3.10.6.7 L4 Business Introduction: Seamless Mobility Services
3.10.6.8 L4 Business Introduction: Feed L4 Technology back into L2 and L3 Autonomous Driving Mass Production Technology
3.10.6.9 L4 Business Introduction: Smart Logistics
3.10.7 Cooperation in AD Field 
3.10.7.1 Autonomous Driving Layout: Invested in Black Sesame Technologies to Build Driving-Parking Integrated Domain Control Platform
3.10.8 Autonomous Driving Partners
3.10.9 EEA Technology Layout: Overall Roadmap
3.10.9.1 EEA Technology Layout: SOA-based EEA 4.0 Platform
3.10.9.2 EEA Technology Layout: Network Topology of EEA 4.0
3.10.9.3 EEA solution: Hardware Architecture
3.10.9.4 EEA solution: Software Architecture
3.10.9.5 EEA solution: Software Hierarchical Decomposition
3.10.9.6 EEA solution: End – Cloud Cooperation
3.10.9.7 Centralized SOA EEA Case: Dongfeng Quantum Architecture Supports L3+ Autonomous Driving
3.10.10 Software Technology: AI Application
3.10.10.1 Software Technology: Fusion Perception and Positioning Technology of AI Application
3.10.10.2 Software Technology: Fusion Perception of AI Application
3.10.10.3 Software Technology: BSW Development Model and Development Process of AUTOSAR Application
3.10.10.4 Software Technology: AUTOSAR-based Controller Developed for AUTOSAR Applications

4. Research on ADAS/Autonomous Driving of Chinese Independent Emerging Brands 
4.1 NIO
4.1.1 ADAS/Autonomous Driving Team
4.1.2 ADAS/Autonomous Driving Roadmap
4.1.3 ADAS/Autonomous Driving Function Evolution
4.1.4 NIO Pilot Function of 1st Gen ADAS System
4.1.5 NIO NAD Function of 2nd Gen ADAS System
4.1.6 Comparision of NIO PILOT NAD Hardware Configuration 
4.1.7 NOP+ 
4.1.8 Core Functions and Iteration Path of NOP+beta 
4.1.9 NOP+ was formally Launched 
4.1.10 Comparison between NOP and NOP+
4.1.11 NOP Function Evolution
4.1.12 Development Path of Automated Parking 
4.1.13 Update of Latest  Parking System  
4.1.14 Automated Parking Function Evolution
4.1.15 Technology Layout 1  
4.1.16 Technology Layout 2  
4.1.17 Technology Layout 3  
4.1.18 Technology Layout 4  
4.1.19 ADAS/Autonomous Driving Cooperation Model and Dynamics 
4.1.20 Investment
4.1.21 ADAS/Autonomous Driving Related Suppliers 
4.1.22 Truck Autonomous Driving Layout 
4.2 XPeng
4.2.1 ADAS/Autonomous Driving Team
4.2.2 XPILOT Development Roadmap
4.2.3 NGP
4.2.4 XNGP 
4.2.5 NGP Function Evolution
4.2.6 Development Path of Automated Parking 
4.2.7 VPA
4.2.8 VPA-L
4.2.9 VPA Function Evolution
4.2.10 Technology Layout 1  
4.2.11 Technology Layout 2  
4.2.12 Technology Layout 3  
4.2.13 Technology Layout 4  
4.2.14 Technology Layout 5  
4.2.15 Autonomous Driving Hardware Configuration and Related Suppliers  
4.2.16 Autonomous Driving Dynamics
4.2.17 Cooperation 
4.3 LI Auto
4.3.1 ADAS/Autonomous Driving Team and Product Development Mode 
4.3.2 ADAS/Autonomous Driving Development Route 
4.3.3 AD System and Typical Models 
4.3.4 AD MAX System
4.3.5 AD Pro System
4.3.6 ADAS Typical Functions
4.3.7 ADAS System Software Iteration 
4.3.8 Development Roadmap of Automated Parking   
4.3.9 Intelligent Parking and Summon 
4.3.10 Function Evolution of Automated Parking   
4.3.11 ADAS System Hardware Iteration and Related Suppliers 
4.3.12 Technology Layout 1  
4.3.13 Technology Layout 2  
4.3.14 Technology Layout 3  
4.3.15 Technology Layout 4  
4.3.16 Technology Layout 5  
4.3.17 Technology Layout 6  
4.3.18 Recent Planning and Cooperation 

4.4 Neta  
4.4.1 Intelligent Driving Team 
4.4.2 Development Course of Intelligent Driving
4.4.3 Autonomous Driving Strategy
4.4.4 Haozhi Super-computing Platform
4.4.5 EEA
4.4.6 Development Course of Intelligent Driving System 
4.4.7 Scenarios Covered by Intelligent Driving System
4.4.8 NETA PILOT 3.0/4.0
4.4.9 Advanced Function Release Plan of NETA PILOT 3.0/4.0 
4.4.10 Parking Functions (1)
4.4.11 Parking Functions (2)
4.4.12 Intelligent Driving Full-Stack Self-development Solution 
4.4.13 Intelligent Driving Parts Suppliers 
4.4.14 Intelligent Driving Cooperation Dynamics 

4.5 Leapmotor
4.5.1 Development Course
4.5.2 Full-domain Self-development
4.5.3 Self-developed Achievement
4.5.4 R&D Team
4.5.5 Vehicle Platform
4.5.6 EEA
4.5.7 Clover EEA 
4.5.8Intelligent Driving Self-Development
4.5.9 Intelligent Driving System ——Leap Pilot
4.5.10 Technology Layout
4.5.11 Suppliers
 

Automotive AI Foundation Model Technology and Application Trends Report, 2023-2024

Since 2023 ever more vehicle models have begun to be connected with foundation models, and an increasing number of Tier1s have launched automotive foundation model solutions. Especially Tesla’s big pr...

Qualcomm 8295 Based Cockpit Domain Controller Dismantling Analysis Report

ResearchInChina dismantled 8295-based cockpit domain controller of an electric sedan launched in December 2023, and produced the report SA8295P Series Based Cockpit Domain Controller Analysis and Dism...

Global and China Automotive Comfort System (Seating system, Air Conditioning System) Research Report, 2024

Automotive comfort systems include seating system, air conditioning system, soundproof system and chassis suspension to improve comfort of drivers and passengers. This report highlights seating system...

Automotive Memory Chip and Storage Industry Report, 2024

The global automotive memory chip market was worth USD4.76 billion in 2023, and it is expected to reach USD10.25 billion in 2028 boosted by high-level autonomous driving. The automotive storage market...

Automotive Memory Chip and Storage Industry Report, 2024

The global automotive memory chip market was worth USD4.76 billion in 2023, and it is expected to reach USD10.25 billion in 2028 boosted by high-level autonomous driving. The automotive storage market...

Automotive AUTOSAR Platform Research Report, 2024

AUTOSAR Platform research: the pace of spawning the domestic basic software + full-stack chip solutions quickens. In the trend towards software-defined vehicles, AUTOSAR is evolving towards a more o...

China Passenger Car Electronic Control Suspension Industry Research Report, 2024

Research on Electronic Control Suspension: The assembly volume of Air Suspension increased by 113% year-on-year in 2023, and the magic carpet suspension of independent brands achieved a breakthrough ...

Global and China Hybrid Electric Vehicle (HEV) Research Report, 2023-2024

1. In 2025, the share of plug-in/extended-range hybrid electric passenger cars by sales in China is expected to rise to 40%. In 2023, China sold 2.754 million plug-in/extended-range hybrid electric p...

L3/L4 Autonomous Driving and Startups Research Report, 2024

The favorable policies for the autonomous driving industry will speed up the commercialization of L3/L4. In the second half of 2023, China introduced a range of policies concerning autonomous drivin...

Intelligent Vehicle Cockpit-driving Integration (Cockpit-driving-parking) Industry Report, 2024

At present, EEA is developing from the distributed type to domain centralization and cross-domain fusion. The trend for internal and external integration of domain controllers, especially the integrat...

Global and China Automotive Operating System (OS) Industry Report, 2023-2024

Chinese operating systems start to work hard In 2023, Chinese providers such as Huawei, Banma Zhixing, Xiaomi, and NIO made efforts in operating system market, launched different versions with competi...

Automotive RISC-V Chip Industry Research Report, 2024

Automotive RISC-V Research: Customized chips may become the future trend, and RISC-V will challenge ARM What is RISC-V?Reduced Instruction Set Computing - Five (RISC-V) is an open standard instructio...

Passenger Car CTP (Cell to Pack), CTC (Cell To Chassis) and CTB (Cell to Body) Integrated Battery Industry Report, 2024

Passenger Car CTP, CTC and CTB Integrated Battery Industry Report, 2024 released by ResearchInChina summarizes and studies the status quo of CTP (Cell to Pack), CTC (Cell To Chassis) and CTB (Cell to ...

Software-defined Vehicle Research Report, 2023-2024 - Industry Panorama and Strategy

1. How to build intelligent driving software-defined vehicle (SDV) architecture? The autonomous driving intelligent platform can be roughly divided into four parts from the bottom up: hardware platf...

Automotive DMS/OMS (Driver/Occupant Monitoring System) Research Report, 2023-2024

In-cabin Monitoring study: installation rate increases by 81.3% in first ten months of 2023, what are the driving factors? ResearchInChina released "Automotive DMS/OMS (Driver/Occupant Monitoring Sys...

Automotive Functional Safety and Safety Of The Intended Functionality (SOTIF) Research Report, 2024

As intelligent connected vehicles boom, the change in automotive EEA has been accelerated, and the risks caused by electronic and electrical failures have become ever higher. As a result, functional s...

Autonomous Driving Map Industry Report,2024

As the supervision of HD map qualifications tightens, issues such as map collection cost, update frequency, and coverage stand out. Amid the boom of urban NOA, the "lightweight map" intelligent drivin...

Automotive Vision Industry Research Report, 2023

From January to September 2023, 48.172 million cameras were installed in new cars in China, a like-on-like jump of 34.1%, including: 9.209 million front view cameras, up 33.0%; 3.875 million side vi...

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