ADAS and Autonomous Driving Tier 1 Suppliers Research Report, 2026

Description

Intelligent Driving Industry Leap: From Widespread L2.9 Adoption toward Physical AI and New Full-Domain Mobility Ecosystem

As vehicle intelligence advances, the installations and penetration rate of ADAS (L1-L2.9) in passenger cars in China have sustained stable growth. ADAS was installed in 9.842 million passenger cars in China in 2022, compared with 6.751 million units in 2021, representing a compound annual growth rate of 20.7%. From 2024 to 2025, the installations increased from 14.559 million cars to 16.231 million cars in 2025, marking a year-on-year growth of 11.5%. In H1 2026, the structure of intelligent driving underwent further reshaping. The penetration rate of L2 and higher-level assisted driving exceeded 72.0%, of which the combined penetration rate of NOA (L2.5+L2.9) reached 37.4% and the aggregate penetration rate of basic assisted driving (L2-L2+) surpassed 34.6%.

Insight 1: New vehicle product definitions have made a leap to L2.9 in advance. Overall structure will rapidly tilt toward L2.9 following model replacements of existing stock vehicles over the next 1-2 years.

According to penetration rates by level of newly launched models across years, L0/L1 low-level intelligent driving solutions have become nearly marginalized in new vehicle model selection. Automakers no longer separately develop discrete low-level solutions in new vehicle project planning. The share of L0 has long hovered close to zero, and the penetration rate of L1 in new vehicles stood below 5% in H1 2026. Basic ADAS covering L2-L2+ and L2.5 solutions remain mainstream configurations for mid-range mass-market models. 2025 saw intensive launches of new L2.5 products, with numerous mid-range models equipped with L2.5 hardware pre-embedded solutions. In H1 2026, the share of new L2.5 products declined moderately as automakers shifted product resources toward L2.9. At the level of new vehicle product definition, the leap to L2.9 has already been completed ahead of schedule. It is projected that over the next 1-2 years, as existing vehicle models undergo iteration and replacement, the overall market structure will keep shifting rapidly toward L2.9.

Insight 2: NOA growth logic undergoes shift: Growth drivers shift from high-end vehicles to entry-level market segments. Highway NOA completed market validation in vehicles in the price range of RMB0-100,000 in 2025, and urban NOA achieved deployment in the same price range in H1 2026.

The core growth logic for the NOA industry has experienced a fundamental shift. The incremental growth pattern once dominated by high-end vehicles has come to an end. The entry-level market segment for vehicles priced below RMB100,000 has emerged as the new core growth engine for intelligent driving. In terms of deployment timeline, highway NOA achieved adaptation and deployment in vehicle models in the price range of RMB0-100,000 in 2025, completing commercial validation in the entry-level market, becoming the third-largest segment by installations of highway NOA, with a penetration rate rising to 9.8% and installations in 162,000 vehicles. Following the penetration of highway NOA into the RMB0-100,000 segment in 2025, urban NOA was deployed in this same price range in H1 2026. Supported by lightweight algorithms for urban NOA and low-cost domain controller solutions with declining hardware costs, urban NOA is gradually becoming a standard capability for entry-level and mainstream vehicle models.

Future competitive focus will shift from "whether high-level intelligent driving can be realized" to "whether low-cost mass-production and deployment in lower price ranges can be achieved". The adoption of NOA in vehicles priced below RMB100,000 compels Tier1 suppliers and chip suppliers to cut BOM costs. Solution providers with capabilities of low-cost mass production and end-to-end lightweight implementation will take larger market shares in lower-tier segments.

Insight 3: 2025-2026 marks a critical turning point for mass-production shift to L2.9 chips, with new-generation chips commencing deployment in vehicles collectively.

Prior to and in 2024, chip supply for urban NOA was highly concentrated, dominated by NVIDIA Orin-X, Tesla's self-developed FSD, and Hisilicon Ascend 1951. 2025-2026 opens a core window period for shift between old and new hardware platforms. New-generation chips including Horizon J6M/J6P, Qualcomm SA8650/8775/8797, Hisilicon Ascend 1951 Lite, NIO Shenji NX9031, and Xpeng Turing achieve volume deployment in vehicles. Installed in a total of 2.3308 million vehicles from 2024 through H1 2026, NVIDIA Orin-X remains the core foundation for high-level intelligent driving, yet its product lifecycle has entered the second half. Next-generation platforms such as NVIDIA Thor-U and Orin-Y will address replacement demands, and the industry overall accelerates migration toward new-generation chips.

From the perspective of supplier landscape, domestic intelligent driving chips have formally entered a cycle of large-scale commercial delivery. With installations in a total of 1.126 million vehicles, Hisilicon Ascend 1951 ranks first among domestic urban NOA chip suppliers and third across the whole industry. Horizon J6M follows a differentiated approach, leveraging its 128 TOPS medium-compute solution to support urban NOA deployment. It empowers third-party intelligent driving suppliers to deliver cost-effective urban NOA solutions, and facilitates intelligent driving penetration into lower-tier markets. QCraft is the first third-party intelligent driving algorithm providers in China to realize mass production of urban NOA based on a single Horizon J6M chip. The chip self-development strategies adopted by leading domestic automakers have yielded tangible results: NIO Shenji and Xpeng Turing have both achieved large-scale installation in vehicles. Through independent chip development, automakers bind their vehicle intelligent driving systems and build differentiated product barriers; XHEART BMC X7 completed mass-production validation from Zero to One, further enriching the local chip supply echelons.

In terms of computing power, the 100-260 TOPS medium computing power range constitutes the absolute mainstream for L2.9. From January to May 2026, this computing power range saw installations in 507,700 vehicles, mostly adopting single-SoC architectures. Chips including Horizon J6M (128 TOPS), Orin-Y (200 TOPS), Orin-X (254 TOPS), and Qualcomm SA8650 represent core mass-market offerings. Domain controllers for mid-range vehicles are predominantly configured with computing power within this range. This confirms that the industry no longer blindly pursues ultra-high computing power. Medium-compute, cost-effective single-chip solutions are better suited for volume production and deployment of urban NOA in lower-tier markets.

The domestic passenger car intelligent driving industry has completed phased transformations including large-scale popularization, technological penetration into lower-tier markets, and hardware iteration. L2.9 has become a mainstream standard vehicle feature, lower-tier market segments represent core growth drivers, and domestic chips have achieved large-scale substitution. A competitive landscape in the stock market has taken shape within the industry.

Nevertheless, the intelligent driving industry does not stop at the existing L2.9 mass-production system. Based on research covering strategic plans, product layouts, technical routes, and ecosystem cooperation trends of 24 ADAS Tier1 suppliers, ResearchInChina observes that ADAS Tier1 suppliers are entering a new development cycle spanning from 2025 to 2027. This cycle features technical advancement, scenario extension (L3/L4), segment expansion (commercial vehicles, two-wheelers, embodied artificial intelligence (EAI)), and global overseas layout.

Trend 1: ADAS Tier1 suppliers accelerate deployment of L3 autonomous driving. 2026 serves as a solution verification window, and mass-production and deployment of intelligent driving products will arrive in 2027.

Several core domestic ADAS Tier1 suppliers have completed strategic layouts of L3 high-level intelligent driving. Seven of these suppliers secured L3 vehicle project designations from 2025 to 2026 and have launched full-scale project development. Five enterprises including Jingwei Hirain, Huawei, Momenta, Horizon Robotics, and Joyson Electronics designate 2027 as the core milestone for mass-production, launch and large-scale commercial application of L3 products.

From an industry rhythm perspective, 2026 is a window period of intensive solution verifications the L3 industry. Core industry tasks focus on qualification application, MIIT access review, real-vehicle acceptance testing, and road pilot implementation. 2027 will mark the first year for large-scale commercial adoption of L3 intelligent driving in China. Leading suppliers maintain highly aligned mass-production timelines, delivering markedly improved industry certainty.

In terms of deployment progress, the first echelon is represented by Huawei. Its supported vehicle models have obtained MIIT L3 access permit and entered road pilot trials across multiple cities, making Huawei the solution supplier with the fastest deployment progress in China. The second echelon include Jingwei Hirain, Momenta, Horizon Robotics, and DeepRoute.ai. These suppliers have secured OEM designations and are in development or real-vehicle testing phases without yet obtaining access permits. They will submit access applications primarily during 2026-2027.

Regarding L3 intelligent driving product layouts, traditional Tier1 suppliers such as Jingwei Hirain primarily deliver domain controller and sensor hardware for L3 without developing full-stack algorithms, relying on external algorithm partners. Native intelligent driving solution suppliers including Huawei, Momenta, Horizon Robotics, DeepRoute.ai, and Afari Technology deliver hardware-software integrated offerings with self-developed algorithms. Their L3 and 2+/L4 systems share the same technical foundation and reuse a data closed loop.

Consumer-oriented commercial deployment of L3 intelligent driving needs to pass through three successive phases. Phase 1 covers local L3 road testing limited to internal technical validation, requiring safety officials on-board and prohibiting commercial sales to end users. Phase 2 covers road-traffic pilot operations. Enterprises must obtain conditional product access permits from MIIT and dedicated L3 license plates issued by local governments. This phase opens only for business operating entities and excludes retail sales to individual consumers. Phase 3 represents full national access. Formal MIIT L3 product access permits are required before sales to private individuals. No such permits have been issued to date. Gradual liberalization will follow enforcement of the mandatory national standard GB 44721-2026 in 2027. Possession of access qualifications does not authorize unrestricted nationwide operation. L3 functions may only activate within designated road segments and speed limits defined by pilot cities.

Up to now, Deepal SL03 and Arcfox aS are the only two domestic vehicle models that have obtained MIIT L3 conditionally autonomous driving product access (Announcement Batch 401, December 15, 2025). The vehicles are undergoing "product access + on-road use pilot". Neither model is retailed to ordinary private consumers. They are operated by mobility service affiliates of automakers, with strict operational limits on designated road sections, ODD ranges, and vehicle speeds.

Trend 2: Following large-scale deployment of urban NOA, intelligent driving suppliers target Physical AI as the next key breakthrough area. L4 autonomous driving represents the optimal entrance to Physical AI and is also the first half of Physical AI industrialization.

Physical AI refers to enabling AI to step out of the screen and be embedded in physical entities (such as cars, robots, and drones) to perceive, understand, and change the real physical world. It requires AI to be embodied: relying on carriers including autonomous vehicles and general-purpose robots to directly perceive real-world environments, develop physical intuition, and make autonomous decisions to execute physical actions, completing a full closed-loop from perception to action.

Autonomous vehicles will emerge as benchmark applications for this paradigm. They will evolve into super onboard agents capable of understanding voice commands and completing multiple tasks end-to-end. Leveraging multimodal perception together with linkage between vehicle status and environmental data, they anticipate user intentions prior to explicit requests and achieve full integration of cockpit and driving functions. At this stage, automobiles will break away from their definition purely as transportation tools and transform into full-scenario intelligent hubs connecting mobility, office work, home life, and third-party ecosystems.

Underlying logic positioning L4 as the optimal gateway to Physical AI stems from the fact that the automotive industry has already completed partial engineering pathways for Physical AI.

First, the full intelligent vehicle industry chain is mature. Any physically capable autonomous agent requires complete hardware links covering perception, computation, and chassis control. Automotive chassis, sensors (like lidar and radar), automotive-grade high-compute chips, and intelligent driving domain controllers are highly standardized and support rapid deployment of tens of thousands of physical agents. Humanoid robots, by contrast, remain in a critical stage of development of robots, reducers, and dexterous hands, with high hardware costs and reliability insufficient for volume production.

Second, autonomous driving represents the sole Physical AI segment that enables "data closed-loop + commercial closed-loop". Two major sore points hinder Physical AI implementation. One is the shortage of massive diverse real-world physical interaction data essential for iterative model training. The other is a viable commercial closed-loop. Large-scale regular deployment cannot be sustained through perpetual costly testing alone. Compared with humanoid robots, autonomous driving offers natural data closed-loop advantages. Intelligent vehicles themselves serve as mobile data collection terminals. With penetration rates of L2 and higher-level intelligent driving vehicles exceeding 70% in China, vehicle travel continuously generates massive volumes of real-world scenario data. Around-the-clock operation of Robotaxi, Robobus, and unmanned logistics delivery fleets feeds back diverse road condition samples, refining models for physical causal reasoning. This forms a complete link: real-road operation -> collection of physical interaction data -> cloud simulation training -> model deployment onto vehicles -> renewed on-road data collection.

Additionally, autonomous driving features controllable task complexity and structured scenarios, focusing solely on planar movement to address the problem of mobile agent subsets. Its core capabilities include travel, obstacle avoidance, and traffic participant prediction, with outputs of steering, braking and throttle control commands. Fine-grained grasping and manipulation of flexible objects are not required, and the mechanical dynamics of the vehicle remain limited in complexity. By contrast, humanoid robots represent the second half of EAI. Equipped with dozens to hundreds of joints, they need to perform actions such as grasping, twisting and pressing, which involve full-body three-dimensional dynamics and tactile interaction. Their task complexity rises exponentially. Furthermore, road environments are relatively structured and constrained by traffic rules, lane markings and traffic lights, unlike highly unstructured scenarios such as homes and wild fields.

Compounded by large-scale deployment of intelligent driving large models in vehicles, if massive datasets fuel autonomous driving as the first half of Physical AI, end-to-end intelligent driving AI large models serve as core engines converting data into practical value. Early autonomous driving implementations depended on hand-written rules and modular development, with perception, prediction, and planning & control operating as independent modules. This block-style approach delivers weak generalization capabilities and struggles to migrate onto general-purpose robot carriers. Intelligent driving large models such as End-to-end, VLM, VLA and world model began to be implemented in 2025. Multiple technical paradigms converge and reshape industry logic in 2026. Each approach carries distinct advantages and disadvantages bounded by the data-computing-power-safety triangle constraint. Players explore differentiated solutions based on their available resources.

Finally, autonomous driving has a well-defined regulatory and safety verification pathway. Mature global grading regulations for autonomous driving have been established, enabling phased deployment starting from closed campuses and urban secondary trunk roads, with safety validation completed step by step. In contrast, safety standards, liability definitions and relevant regulations for humanoid robots in open environments are largely blank, and they do not yet possess the fundamental prerequisites for large-scale public deployment.

Trend 3: ADAS Tier1 suppliers make cross-border inroads into EAI, and pursue breakout growth via technical homology and tap into second-curve business opportunities.

Automotive ADAS Tier1 suppliers are undergoing a second industrial migration and capability reconstruction. Amid stock-market saturation, intensifying price competition, and pressure from self-development by automakers squeezing supply chain profit margins, leading local Tier1 suppliers collectively initiate horizontal migration from "vehicle intelligent assets to general-purpose physical intelligence assets".

Research examining strategies and product layouts of 13 domestic ADAS Tier1 suppliers within EAI sectors identifies five primary directions for product development: hardware products (domain controller architectures), unified AI foundation models (one underlying algorithm model empowering multiple vertical scenarios), Physical AI infrastructure providers (data solution providers/cloud brain + simulation training + AI models/computing power (robot chips) + cerebrum and cerebellum models + RDK-series development platforms), robot components (sensors, joint modules, dexterous hands, robot head assemblies, energy), and robots (charging robots, data collection robots, business-oriented humanoid/wheeled robots for flexible operation).

First, robot domain controllers have become a core strategic focus for ADAS Tier1 suppliers entering the EAI sector. With mature expertise in automotive-grade technology, multiple players have launched standardized, mass-producible robot hardware products.

Desay SV launched AI Cube, its robot intelligent base, in November 2025. It delivers three differentiated computing power configurations adaptable to industrial, household, and automotive scenarios. The product has completed iteration and formally entered the mass-production and delivery phase.

Jingwei Hirain completed strategic brand upgrading, repositioning itself from "electronic system & technology service company" to "intelligent technology company". It expands core businesses comprehensively around Physical AI and leverages mature automotive-grade domain controller technical capabilities to enter EAI sectors. It focuses on robot electronic and electrical hardware bases and system integration businesses, and does not prioritize in-house development of large models and world models. It plans to launch a robot cerebrum domain controller product based on Huixi Guangzhi R1 in 2026, with maximum computing power reaching 500 TOPS.

IMotion iteratively released its new-generation robot main controller iRC600 supporting rapid end-side AI-model deployment. Its previous-generation product iRC100P has achieved stable mass-production and delivery.

TZTEK established wholly-owned subsidiary Stellarmind to deepen engagement in the Physical AI sector. Focusing on the R&D of end-side Physical AI infrastructure, it has launched the Star Series edge intelligent computing units, forming a matrix covering all computing power ranges: Star L is a 100 TOPS low-compute version adapted for quadruped robot scenarios; Star M is a 200-500 TOPS medium-compute version targeting wheeled robots; Star H is a 500-1000 TOPS high-compute version precisely tailored to advanced requirements of humanoid robots.

Beyond hardware domain controllers, Physical AI foundation (data + algorithms + computing power) is the second core layout direction for the industry. Data, algorithms, and computing power jointly form three cornerstones enabling Physical AI implementation. Leveraging long-term ecosystem expertise and engineering deployment experience in the automotive sector, Huawei and Horizon Robotics are building comprehensive Physical AI underlying infrastructure covering robot computing power, scenario data, cloud intelligent brains, and robot cerebrum-cerebellum layered architecture. They open up standardized underlying platforms to ADAS Tier1 suppliers and robot manufacturers across the industry, assuming the role of infrastructure providers within the industrial chain. This creates an industrial landscape featuring "collaboration and complementarity with partial competition" alongside the hardware foundation businesses of Tier1 suppliers.

Specifically, based on the computing power of its Sunrise series chips, Horizon Robotics' supporting robot development stack extending upwards, data toolchain and cerebrum-cerebellum model system provide standardized edge computing power and software development services for humanoid robot manufacturers, substantially cutting customers' costs of hardware adaptation and model migration.

Huawei has built a complete layered technical architecture of "cloud brain (simulation + model training) + robot cerebrum (Pangu Models) + robot cerebellum", covering the entire industrial chain of computing power, data and algorithms. NavInfo focuses on agent data services and develops an integrated data solution spanning from data collection to full-chain compliance control, filling gaps in Physical AI data deployment.

The third major layout direction is core components for EAI. Manufacturers continuously expand their product portfolios by extending technologies from automotive EEA to achieve full coverage of core robot components. In the perception component field, Baolong Automotive, as a representative player, develops multiple types of critical robot sensors for perception, force sensing and position detection to meet the fundamental requirements of robots for environmental perception, posture recognition and precise localization. In the actuation component segment, Joyson Electronics leverages mature R&D and mass-production experience in vehicle body actuation systems to expand into core actuation parts such as robot head assemblies and TeleHand dexterous hands. These products are designed to address key problems of robots including bionic interaction, fine manipulation and anthropomorphic motion, and complete the hardware system of the robot actuation layer.

Trend 4: The pace of deploying commercial vehicle NOA quickens, and Tier1 suppliers capture incremental opportunities in trunk-line intelligent driving leveraging technical homology between passenger cars and commercial vehicles.

Starting from January 14, 2026, the Announcement No.21 issued by the Ministry of Transport revised four operational vehicle standards including the Safety Specifications for Commercial Bus. Newly qualified models of commercial buses, trucks and hazardous goods transport vehicles applying to enter the road transport market must be equipped with active safety devices such as AEBS and LDWS. New models of operating commercial vehicles in China have officially entered an era where ADAS is mandatory standard configuration.

Meanwhile, commercial vehicle NOA is advancing from trials to mass production. The commercial rollout of commercial vehicle NOA hinges on whether transportation is safer, drivers feel more relaxed, and operating costs are lower. Mandatory ADAS configuration directly addresses the requirement for "enhanced safety", while NOA further enables "reduced driver burden" and "lower operating costs" on this basis. Mandatory ADAS installation acts as a compliance threshold, whereas NOA serves as an efficiency tool.

Case Study of Zhuoyu Technology's Commercial Vehicle NOA Mass Production:

From a strategic perspective, Zhuoyu Technology's core strategy for 2026 is a comprehensive transformation from an "intelligent driving Tier1" to a "Mobile Physical AI company". Its self-developed native multimodal large model technology will constitute a core component of Zhuoyu's mobility foundation. That is, the multimodal foundation model enables autonomous mobility for diverse mobile platforms across multiple vertical sectors, scenarios and countries. It not only supports L2-L4 passenger cars, commercial vehicles, logistics vehicles and Robotaxis, but also adapts to various mobility scenarios for embodied robots.

From a manufacturing perspective, the Changzhou factory represents a critical step for Zhuoyu's "Intelligentize All Mobility" strategy to accelerate the intelligent transformation of its commercial vehicle business. Officially commissioned in July 2026, the Changzhou factory establishes a dual manufacturing base layout alongside the Shenzhen facility. The new factory mainly undertakes the manufacturing and inspection of intelligent driving components, including visual perception systems, domain controllers and LiDAR. Phase 1 has a planned capacity of 100,000 sets of intelligent driving domain controllers, and Phase 2 targets 500,000 sets. The long-term employee headcount will exceed 100. Zhuoyu expanded the Changzhou factory to broaden the boundaries of its intelligent driving business, migrating intelligent driving capabilities accumulated in the passenger car sector to diversified scenarios such as heavy-duty trucks, buses, unmanned logistics vehicles and Robotaxis. As business expands across multiple scenarios, manufacturing capacity needs to be expanded in tandem. At present, passenger cars remain Zhuoyu's core mass-production business, and the new capacity mainly targets incremental business opportunities in commercial vehicles and autonomous vehicles.

From a business perspective, Zhuoyu Technology has formed partnerships with all six top heavy-duty truck brands in China, including FAW Jiefang, Sinotruk, Shaanxi Automobile, XCMG and Foton Motor. Mass production of its first heavy-duty truck has started, with dozens of vehicle models which are to be produced from June 2026 to the first half of 2027. The company is also accelerating overseas projects. It collaborates with Sinotruk on a commercial vehicle intelligent driving project for the European market, which was showcased at the IAA Transportation Hannover.

Looking at the overall development landscape of China's automotive intelligent driving industry, 2024-2026 marks a pivotal transition period, during which the industry shifts from large-scale popularization toward higher-level functionality, universal accessibility and diversification. In the short term, L2.9 will become a mainstream standard intelligent driving configuration for passenger cars. Lower-tier markets serve as the core growth engine for NOA. Domestic intelligent driving chips achieve large-scale substitution, and low-cost, highly adaptable medium-compute solutions become the market mainstream. Industry competition has completely moved away from the high-end premium pricing model and turned into a contest over mass-production capability and cost control.

In the medium and long term, industrial boundaries keep expanding. Technologically, the industry advances from L2 driver assistance to L3 high-level autonomous driving and L4 full-domain autonomous driving. Relying on the underlying autonomous driving technology foundation, it enters new segments of Physical AI and EAI. In terms of market coverage, the business scope extends beyond the single passenger car scenario to diversified fields covering trunk logistics for commercial vehicles, unmanned delivery and intelligent robots, unlocking the second growth curve for the industry. With continuous improvement of L3/L4 policies and standards, constant breakthroughs in domestic technologies and deeper industrial chain collaboration, China's intelligent driving industry will break free from the single attribute of traditional automotive hardware, gradually evolve into a full-domain intelligent mobility ecosystem, and complete the core transformation from vehicle intelligence to industrialized Physical AI, gaining competitive edges in the global intelligent transportation and EAI sectors.

Product Code: DTT013

1 In-depth Analysis of Chinse ADAS Market

Installations and Installation Rate of ADAS in Passenger Cars in China, 2024-2026H1 (1)
Installations and Installation Rate of ADAS in Passenger Cars in China, 2024-2026H1 (2)
Installations and Installation Rate of L2 ADAS in Passenger Cars in China, 2024-2026H1 (1)
Installations and Installation Rate of L2 ADAS in Passenger Cars in China, 2024-2026H1 (2)
Installations and Installation Rate of L2+ ADAS in Passenger Cars in China, 2024-2026H1 (1)
Installations and Installation Rate of L2+ ADAS in Passenger Cars in China, 2024-2026H1 (2)
Installations and Installation Rate of L2.5 ADAS in Passenger Cars in China, 2024-2026H1 (1)
Installations and Installation Rate of L2.5 ADAS in Passenger Cars in China, 2024-2026H1 (2)
Installations and Installation Rate of L2.9 ADAS in Passenger Cars in China, 2024-2026H1 (1)
Installations and Installation Rate of L2.9 ADAS in Passenger Cars in China, 2024-2026H1 (2)
Sales Volume and Penetration Rate of Highway NOA-equipped Vehicle Models (By Energy Type), 2024-2026H1
Overall Sensor Solutions of Highway NOA-equipped Vehicle Models, 2024-2026H1
Sales Volume and Penetration Rate of Urban NOA-equipped Vehicle Models, 2024-2026H1 (By Energy Type)
Overall Sensor Solutions of Urban NOA-equipped Vehicle Models, 2024-2026H1
Exploration of Competitive Landscape of Chinese Tier1 Suppliers (1)
Exploration of Competitive Landscape of Chinese Tier1 Suppliers (2): Market Analysis of L2~L2+ Intelligent Driving Suppliers
Exploration of Competitive Landscape of Chinese Tier1 Suppliers (3): Market Analysis of L2.5 Intelligent Driving Suppliers
Exploration of Competitive Landscape of Chinese Tier1 Suppliers (4): Market Analysis of L2.9 Intelligent Driving Suppliers
Exploration of Competitive Landscape of Chinese Tier1 Suppliers (5): Market Analysis of L2.5 Intelligent Driving Chip Suppliers
Exploration of Competitive Landscape of Chinese Tier1 Suppliers (6): Market Analysis of L2.9 Intelligent Driving Chip Suppliers
Analysis on Distribution of Computing Power Ranges of Intelligent Driving Chips Equipped on Highway NOA and Urban NOA, 2026
Exploration of Competitive Landscape of Chinese Tier1 Suppliers (7): Development Trends of Intelligent Driving Solutions
Exploration of Competitive Landscape of Chinese Tier1 Suppliers (8): Analysis 1 of Vehicle Models Equipped with L2.9 ADAS and Installations
Exploration of Competitive Landscape of Chinese Tier1 Suppliers (8): Analysis 2 of Vehicle Models Equipped with L2.9 ADAS and Installations
Summary of Four Major Insights into China's Intelligent Driving Market, 2024-2026H1

2 Development Trends and Forward-looking Research of ADAS Tier 1 Market

Trend 1: the Watershed of Urban NOA Landscape Emerges in 2025, the Third-Party Algorithm Track Bids Farewell to the Dual-Strong Pattern, and A Multi-Strong Competition Pattern Is Established in 2026
Trend 2: From the Perspective of Technical Paths, End-To-End, VLA, World Model, VLM and Fusion Paradigm Have Become the Mainstream Routes for Urban NOA Implementation
Trend 3: ADAS Tier1 Suppliers Accelerate the Layout of L3 Autonomous Driving, 2026 Is the Window Period for Solution Verification, and Mass Production and Implementation of Intelligent Driving Products Arrives in 2027 (1)
Trend 3: ADAS Tier1 Suppliers Accelerate the Layout of L3 Autonomous Driving, 2026 Is the Window Period for Solution Verification, and Mass Production and Implementation of Intelligent Driving Products Arrives in 2027 (2)
Trend 4: Following Large-Scale Implementation of Urban NOA, Intelligent Driving Suppliers Take Physical AI as the Next Key Target, Accelerating the Advent of Physical AI
Trend 4: L4 Autonomous Driving Is the Optimal Entrance to Physical AI, and Also the First Half of Industrial Implementation of Physical AI
Three Core Findings on L4 Layout of ADAS Tier 1 Suppliers
Timeline for L4 Scenario Implementation of ADAS Tier 1 Suppliers
Definition of Physical AI and Overview of AI Evolution Process
Autonomous Vehicles Are the First Stop for Physical AI Implementation and the Main Battlefield of Physical AI in the Next Decade
Data + Large Model + Commercial Monetization = "First Principle" of Physical AI
Case Study 1: Hyundai Releases Overall Physical AI Strategy
Case Study 1: Hyundai Takes L4 Autonomous Driving as the First Stop for Physical AI Implementation
Case Study 2: QCraft Launches Robo-X to Build An "Android Platform for Autonomous Driving"
Trend 5: Entering the AI-Driven Intelligent Vehicle Era, AI Models Shift from "Rule-Based Driving" to "AI-enabled Human-like Driving", and Vehicles Evolve from "Passive Interaction" to "Onboard AI Agent"
Trend 6: ADAS Tier1 Suppliers Make Cross-Border Inroads into EAI, and Pursue Breakout Growth via Technical Homology and Tap into Second-Curve Business Opportunities
Five Major Product Layout Directions for ADAS Tier1 to Enter EAI (1)
Five Major Product Layout Directions for ADAS Tier1 to Enter EAI (2)
Commercial Implementation Progress of EAI Products Launched by ADAS Tier1 Suppliers
Trend 7: The Pace of Deploying Commercial Vehicle NOA Quickens, and Tier1 Suppliers Capture Incremental Opportunities in Trunk-Line Intelligent Driving Leveraging Technical Homology between Passenger Cars and Commercial Vehicles (1)
Trend 7: The Pace of Deploying Commercial Vehicle NOA Quickens, and Tier1 Suppliers Capture Incremental Opportunities in Trunk-Line Intelligent Driving Leveraging Technical Homology between Passenger Cars and Commercial Vehicles (2)
Trend 8: From Four Wheelers to Two Wheelers, Make Cross-Border Layout of Incremental Products
Trend 9: ADAS Tier1 Suppliers Accelerate Global Layout, and the Overseas Layout Model Evolves from Vehicle-Attached Overseas Expansion 1.0 to Overseas Factory Construction + Localized R&D and Verification (1)
Trend 9: ADAS Tier1 Suppliers Accelerate Global Layout, and the Overseas Layout Model Evolves from Vehicle-Attached Overseas Expansion 1.0 to Overseas Factory Construction + Localized R&D and Verification (2)
Trend 9: ADAS Tier1 Suppliers Accelerate Global Layout, and the Overseas Layout Model Evolves from Vehicle-Attached Overseas Expansion 1.0 to Overseas Factory Construction + Localized R&D and Verification (3)
Case Study: Accelerate Overseas Expansion and Comprehensively Promote Global Layout (1)
Case Study: Accelerate Overseas Expansion and Comprehensively Promote Global Layout (2)

3 Benchmarking Analysis of Products and Solutions of ADAS Tier1 Suppliers

Comparison of Radar Layout of Major Chinese Tier1 Suppliers
Comparison of Front-view Camera Layout of Major Chinese Tier1 Suppliers (1)
Comparison of Front-view Camera Layout of Major Chinese Tier1 Suppliers (2)
Front-view Camera Product Evolution Trend 1: Triple Breakthrough of Perception Technology Iteration (Pixel / Algorithm / Chip), Intelligent Driving Functions and Market Boundaries
Comparison of LiDAR Layout between Major Chinese Tier1 Suppliers
Evolution Direction of LiDAR Products
Comparison of Domain Controller / Computing Platform between Major Chinese Tier1 Suppliers (1)
Comparison of Domain Controller / Computing Platform between Major Chinese Tier1 Suppliers (2)
Comparison of Domain Controller / Computing Platform between Major Chinese Tier1 Suppliers (3)
Comparison of Domain Controller / Computing Platform between Major Chinese Tier1 Suppliers (4)
Comparison of Domain Controller / Computing Platform between Major Chinese Tier1 Suppliers (5)
Comparison of Domain Controller / Computing Platform between Major Chinese Tier1 Suppliers (6)
Comparison of Intelligent Driving Solutions between Major Chinese Tier1 Suppliers (1)
Comparison of Intelligent Driving Solutions between Major Chinese Tier1 Suppliers (2)
Comparison of Intelligent Driving Solutions between Major Chinese Tier1 Suppliers (3)
Comparison of Intelligent Driving Solutions between Major Chinese Tier1 Suppliers (4)
Comparison of Intelligent Driving Solutions between Major Chinese Tier1 Suppliers (5)
Comparison of Intelligent Driving Solutions between Major Chinese Tier1 Suppliers (6)
Comparison of Intelligent Driving Solutions between Major Chinese Tier1 Suppliers (7)
ADAS Tier 1: Benchmarking Research on L3/L4 Planning and Commercialization Progress (1)
ADAS Tier 1: Benchmarking Research on L3/L4 Planning and Commercialization Progress (2)
ADAS Tier 1: Benchmarking Research on L3/L4 Planning and Commercialization Progress (3)
ADAS Tier 1: Benchmarking Research on L3/L4 Planning and Commercialization Progress (4)
ADAS Tier 1: Benchmarking Research on L3/L4 Planning and Commercialization Progress (5)
ADAS Tier 1: Benchmarking Research on L3/L4 Planning and Commercialization Progress (6)
ADAS Tier 1: Benchmarking Research on L3/L4 Planning and Commercialization Progress (7)
Comparative Analysis of EAI Layout Strategy, Core Products and Commercialization Progress between Desay SV and Jingwei HiRain
Comparative Analysis of EAI Layout Strategy, Core Products and Commercialization Progress between Freetech and iMotion
Comparative Analysis of EAI Layout Strategy, Core Products and Commercialization Progress between NavInfo and Zhuoyu Technology
Comparative Analysis of EAI Layout Strategy, Core Products and Commercialization Progress between Huawei and Horizon Robotics
EAI Layout Strategy, Core Products and Commercialization Progress of Joyson Electronics
Comparative Analysis of EAI Layout Strategy, Core Products and Commercialization Progress between Huaqin Technology and TZTEK
Comparative Analysis of EAI Layout Strategy, Core Products and Commercialization Progress between Lenovo Vehicle Computing and Baolong Automotive

4 Products and Solutions of ADAS Tier1 Suppliers

4.1 Desay SV
- Profile
- 2026 Strategic Priorities
- Four Major Businesses and New Product Layout
- Overview of Operation, 2022-2026H1
- Intelligent Driving Layout
- EAI Layout
- Intelligent Driving Sensor Layout
- Front-view Camera Product: 17MP
- Radar Series: FRD02/03
- Radar Series Products: CRD03P, CRD03H
- Radar Series: 8T8R 4D Radar
- Intelligent Driving Domain Controller Product Matrix (1)
- Intelligent Driving Domain Controller Product Matrix (2)
- Intelligent Driving Domain Controller Product Matrix (3)
- New-generation Central Computing Platform: ICPS02H
- Cockpit-Driving Integration Solution: ICPS01E (1)
- Cockpit-Driving Integration Solution: ICPS01E (2)
- L4 Intelligent Driving Products
- Intelligent Driving Cooperation Model
- Smart Solution
- Global Layout
- Overseas Layout Path: Supply Chain Localization
- Major Customers and Ecosystem Partners
4.2 Jingwei HiRain
- Business 1: Intelligent Automotive Electronic Products Business
- Revenue, 2024-2026H1
- Business 1: Electronic Products for High-end Equipment
- Business 2: Large Assemblies and Special Vehicles
- Business 3: Intelligent Transportation
- Business 4: Industrial Intelligence
- EAI Layout: Products (1)
- EAI Layout: Products (2)
- Overseas Expansion Strategy
- Core Progress in Key Intelligent Driving Technologies, 2026
- Intelligent Driving Layout (1)
- Intelligent Driving Layout (2)
- Sensors
- Radar Layout
- 4D Imaging Radar: LRR610/LRR620
- Long-range Imaging Radar: LRR615
- Jingwei HiRain and Arbe Announce Winning L3 Imaging Radar Designation from a Global Leading OEM
- LiDAR Layout
- High-precision Positioning Module
- Evolution History of IFC Series
- M57 IFC
- Driving-Parking Integrated Domain Controller ADCU Product Matrix
- Driving-Parking Integrated Domain Controller ADCU (1)
- Driving-Parking Integrated Domain Controller ADCU (2): ADCU II Based on TDA4 Chip Architecture
- Driving-Parking Integrated Domain Controller ADCU (3): ADCU II Based on Huaxi R1 / Mobileye Chip Solution
- Key Upgrades of Second-generation Driving-Parking Integrated Domain Controller, 2026
- High-performance Computing Platform: HPC 2.5
- Central Computing Platform: CCP
- Ecosystem Partners
4.3 Huawei
- Cooperation Model Between Huawei Intelligent Automotive Solutions and OEMs
- Timeline of Yinwang Spin-off
- Equity Structure of Yinwang Technology
- Mid-term Planning of Yinwang: Extend to a Physical Industrial Platform of "Intelligent Driving Solution + Self-produced Core Components"
- Radar (1)
- Radar (2)
- Qiankun LiDAR (1): 96-channel and 192-channel
- Qiankun LiDAR (2): 96-channel and 192-channel
- Qiankun LiDAR (3): High-precision Solid-state LiDAR
- Qiankun LiDAR (4): 896-channel Dual Optical Path Image-grade LiDAR
- Qiankun LiDAR Product Portfolio and Technology Evolution Direction (1)
- Qiankun LiDAR Product Portfolio and Technology Evolution Direction (2)
- MDC Intelligent Driving Computing Platform (1)
- MDC Intelligent Driving Computing Platform (2)
- MDC Intelligent Driving Computing Platform (3)
- HI Full-stack Intelligent Automotive Solution
- Evolution Roadmap of Qiankun Intelligent Driving Foundation Model (ADS2.0->ADS5)
- ADS 5 (1): WEWA 2.0 Architecture
- Comparation Between WEWA2.0 and WEWA1.0
- ADS 5 (2): Computing Power
- Disassembly of Yinwang "DriveVLA-W0" Model
- Benchmarking of Four Major Intelligent Driving Versions of Qiankun ADS 5
- ADS 4.0 (1): WEWA Architecture
- ADS 4.0 (2): Sensor Hardware Configuration
- Qiankun L3/L4 Planning
- Qiankun L3/L4 Development Progress (1)
- Qiankun L3/L4 Development Progress (2)
- Qiankun L3/L4 Development Progress (3)
- Qiankun-enabled L3 Vehicle Models
- Enters L4 Unmanned Sanitation Market
- Production Vehicle Models and Mass-production Customers
- Strategic Goals in Humanoid Robot Segment
- CloudRobo Cloud Platform
- Commercialization Progress in Humanoid Robot Segment
4.4 Neusoft Reach
- Profile
- Hardware-software Integrated Full-stack Intelligent Driving Solution for Passenger Cars
- Intelligent Driving Product Matrix
- Front-view All-in-one (1): X-Cube3.0
- Front-view All-in-one (2): X-Cube4.0
- Front-view All-in-one (3): X-Cube-Lite
- Evolution Roadmap of Front-view Smart Camera X-Cube Series
- Driving-Parking Integrated Domain Controller (1): X-BOX3.0 & X-BOX4.0
- Driving-Parking Integrated Domain Controller (2): X-BOX3.0 & X-BOX4.0
- Driving-Parking Integrated Domain Controller (2): X-BOX 5.0
- Driving-Parking Integrated Domain Controller: X-BOX3.0 - X-BOX6.0 Series Product Evolution Diagram (1)
- Driving-Parking Integrated Domain Controller: X-BOX3.0 - X-BOX6.0 Series Product Evolution Diagram (2)
- 5th-generation Intelligent Driving System Base Architecture: NeuAUTO V
- Technical Architecture of One-model End-to-end Foundation Model
- Two-model End-to-end Foundation Model (1)
- Two-model End-to-end Foundation Model (2)
- Hardware-software Integrated Full-stack Intelligent Driving Solution for Commercial Vehicles
- Intelligent Driving Products and Solutions for Commercial Vehicles
- Central Computing Platform (1)
- Central Computing Platform (2)
- SOA Software Architecture
- Basic Software: NeuSAR (1)
- Basic Software: NeuSAR (2)
- Intelligent Driving Partners and Mass-production Customers
4.5 Freetech
- Positioning and Hardware-software Integrated Product Matrix
- Strategic Goal: Evolve from Full-stack AI Mass-production Base for Intelligent Driving -> Build Intelligent Infrastructure for the Physical AI World
- Operating Revenue and Mass-production Project Changes of Three Types of Intelligent Driving Solutions, 2023-2025
- Intelligent Driving AI Foundation Model: Introduce Cloud FUGA-VL Foundation Model + Edge E2E
- Data Closed-loop Solution: FUGA
- L4 Layout
- Intelligent Driving Solutions
- ODIN Intelligent Digital Base: ODIN 1.0->ODIN 3.5
- Domain Controller Solutions
- NOA Mass-production: Benchmarking of NOA-enabled Production Vehicle Models, Installations and Configurations, 2022-May 2026
- Domain Controller Solution: ADC25-J "Intelligent Driving for All" Version
- Domain Controller Solution: ADC25 Architecture
- Domain Controller Solution: Fuxing Series
- Camera Products: Passenger Car Front-view Camera
- Camera Products: FVC3
- Camera Module Products (1)
- Camera Module Products (2)
- Radar (1)
- Radar (2): FVR60 Series
4.6 iMotion
- Three Strategic Priorities, 2026-2027
- Exploration of Innovative Business: Enter Robot Segment
- Overview of Operating, 2025
- Business Model
- Intelligent Driving Product Matrix
- Front-view All-in-one: iFC Series
- Domain Controller Products (1): SuperVison
- Domain Controller Products (2): iDC100/iDC300/iDC500
- Domain Controller Products (2): IDC 310/IDC 510 PRO/IDC 700
- Domain Controller Products (3): IDC 510Pro VS. IDC 510 Based on Horizon J6 Series Solution
- Application of Intelligent Driving Product Portfolio in Commercial Vehicles
- Intelligent Driving Solutions
- Evolution Roadmap of Intelligent Driving Technology
- Intelligent Driving Foundation Model: Aimoxing
- Robot Domain Controller Products: iRC100P, iRC600
- Global Layout
- Intelligent Driving Overseas Layout: Overseas Markets and Strategies
- Intelligent Driving Overseas Products
- Overseas Data Link
- Production Vehicle Models and Major Partners
4.7 SenseAuto
- Profile
- Strategic Priorities in Intelligent Driving, 2025
- Intelligent Driving Layout
- "Cockpit-Driving-Cloud" Trinity Layout
- Intelligent Driving Solutions
- Comparison of Intelligent Driving Solutions between Five Major Intelligent Driving Suppliers Based on Horizon J6M Chip Solution
- End-to-end Autonomous Driving Evolution Diagram
- Generative Intelligent Driving R-UniAD (1)
- Generative Intelligent Driving R-UniAD (2)
- Comparison of Tier 1 Solutions Adopting New End-to-end Intelligent Driving Paradigm with Deep Integration of "Reinforcement Learning + World Model"
- Intelligent Simulation Toolchain and Technology Innovation for Cost Reduction
- Mass-production
4.8 Momenta
- Overall Development Strategy
- Focus on Mass Production, L4 Deployment and Underlying R&D (1)
- Focus on Mass Production, L4 Deployment and Underlying R&D (2)
- Overview of Operation, 2025-2026H1
- Mpilot Intelligent Driving Solution
- Comparison of Intelligent Driving Solutions Based on Cross-chip Platform
- TOP15 Production Vehicle Models with Momenta NOA, 2025-2026H1
- Evolution Roadmap of Intelligent Driving Algorithm
- Three-layer Architecture of R7 World Model
- End-to-end Solution
- L3 Layout Planning and Progress
- L3 Business Model
- L4 Robovan: Planning and Commercial Deployment Progress, 2026-2028
- Overview of L4 Robotaxi Layout
- L4 Robotaxi Layout: Domestic Market
- L4 Robotaxi Layout: Overseas Market (1)
- L4 Robotaxi Layout: Overseas Market (2)
- Overseas Layout Path (1)
- Overseas Layout Path (2)
- Intelligent Driving Overseas Layout Path (3)
- Signed Strategic Cooperation Agreement with Valeo
4.9 NavInfo
- Profile
- Overview of Operation, 2025
- Key Business Layout Priorities, 2026-2027
- Development Progress of Four Major Business Lines
- Strategic Upgrade: Intelligent Driving (1)
- Strategic Upgrade: Intelligent Driving (2)
- Strategic Upgrade: Intelligent Driving (2)
- Strategic Upgrade: Intelligent Chip (2)
- Layout in EAI Sector
- Configurations and Intelligent Driving Functions of PhiGo Entry-enabled Production Vehicle Models
- Configurations and Intelligent Driving Functions of PhiGo Pro-enabled Production Vehicle Models
- End-to-end Foundation Model (1): Artificial General Intelligence Paradigm Based on End-to-end and Generative Foundation Model
- End-to-end Foundation Model (2): Won Designation Projects Based on Progressive End-to-end
- End-to-end Foundation Model (3): Reinforcement Learning Link + Cloud World Model Training
- Cockpit-Driving Integration Product Matrix
- Driving-Parking Integrated Product: Based on SA8620/SA8650
- Cockpit-Parking Integrated Product: SA8255 Chip Solution
- Cockpit-Driving-Parking Integrated Product: AC8025+J3
- Major Ecosystem Partners and Customers, 2025-2026 (1): OEMs
- Major Ecosystem Partners and Customers, 2025-2026 (2): Chip and Technology Ecosystem Strategic Partners
4.10 QCraft
- Corporate Development Vision: Ultimately Achieve Artificial General Intelligence and Currently Accelerate Evolution from the Second Stage to the Third Stage
- QCraft Is Evolving from A Technology R&D Enterprise to A Comprehensive Autonomous Driving Enterprise of "Technology + Scale + Globalization"
- L2++ and L4 "Dual-line Development Strategy" to Realize Autonomous Driving Deployment and Iteration
- "Dual-engine Strategy"
- Logic of "Dual-engine Strategy": From Technology Closed-loop to Global Layout
- Disassembly of Physical AI Model
- Physical AI Layout: L4 Unmanned Logistics Products and Solutions
- Intelligent Driving Product Matrix (1): QPilot 2.0 - High-level Intelligent Driving Product Based on Horizon Intelligent Driving Chip
- Intelligent Driving Product Matrix (2): QPilot 2.0 - High-level Intelligent Driving Product Based on Horizon Intelligent Driving Chip
- Mass-production of End-to-end Urban NOA Solution Based Single Journey 6M
- Core Technology for Single J6M-based Urban NOA Deployment: Interpretable One-model End-to-end
- Core Technology of Ultimate Urban NOA Experience: VLA and World Model Architecture
- Overview of Intelligent Driving Product Matrix
- L4 Product Matrix (1): Robo-Bus Product Layout
- L4 Product Matrix (2): Large-scale Deployment of Robo-Bus
- L4 Product Matrix (3): Enter L4 Unmanned Delivery Logistics Vehicle Scenario
- Logic of L4 Unmanned Delivery Robovan Layout
- Intelligent Driving Solution Evolution Diagram
- Data and Model Training Closed-loop
- Intelligent Driving Overseas Layout: Products, Strategies, Markets
- Overseas Representative L2 ADAS Production Vehicle Model 1: Rox ADAMAS
- Overseas Representative L2 ADAS Production Vehicle Model 2: SAIC MG4
- Strategic Ecosystem Partners
4.11 Afari Technology
- Profile
- Overview of Operation
- AI + Vehicle Strategy
- Top-level Strategy and Business Closed-loop
- Ecosystem Alliance
- Disassembly of G-ASD Intelligent Driving Solution
- Installations of Intelligent Driving Solution in Representative Vehicle Models, 2025-2026H1 (1)
- Installations of Intelligent Driving Solution in Representative Vehicle Models, 2025-2026H1 (2)
- Judgment on Next-generation End-to-end Architecture Trends (1)
- Judgment on Next-generation End-to-end Architecture Trends (2)
- Judgment on Next-generation End-to-end Architecture Trends (3)
- End-to-end Foundation Model Architecture: E2E2.0+VLA
- E2E Architecture
- World Model Closed-loop Simulation Architecture
- Native Intelligent Driving Foundation Model
- Core Business (1)
- Core Business (2): Robotaxi Deployment Planning, 2026-2030
- Evolution Roadmap of Intelligent Driving Solutions (ASD1.0->ASD4.0) and End-to-end Foundation Model
4.12 Zhuoyu Technology
- Core 2026 Strategy - Building Mobile Intelligent Foundation Model (1): ADAS Tier 1 -> Mobile Physical AI Company
- Core 2026 Strategy - Building Mobile Intelligent Foundation Model (2): ADAS Tier 1 -> Mobile Physical AI Company
- Core 2026 Strategy - Building Mobile Intelligent Foundation Model (3)
- Launches Mobile Physical AI Foundation Model in 2026: Native Multimodal Foundation Model
- Strategic Priorities, 2026-2027 (1): Manufacturing
- Strategic Priorities, 2026-2027 (2): Business
- Automotive Product Layout: Hardware
- Automotive Product Layout: Inertial-Aided Stereo and Tele Camera System and LiDAR Assembly
- Automotive Product Layout: Jimu 2.0 (1)
- Automotive Product Layout: Jimu 2.0 (2)
- Automotive Product Layout: Automotive Blind Spot Radar - Zhizhou
- Intelligent Driving Domain Controller Product Series (1)
- Intelligent Driving Domain Controller Product Series (2)
- Intelligent Driving Domain Controller Product Series (3): L3/L4
- Intelligent Driving Domain Controller Product Series (4): L4 Robotaxi Intelligent Driving Domain Controller
- ClixPilot Platform
- L4 Unmanned Logistics Vehicle Layout
- Evolution Diagram of ClixPilot Platform Intelligent Driving Solution
- Evolution Roadmap of ClixPilot Intelligent Driving End-to-end Foundation Model
- Intelligent Driving Ecosystem Partners
4.13 Horizon Robotics
- Positioning and Business Model
- Final Strategy Roadmap: 2025-2030+
- Strategic Priorities, 2026-2028
- Product Planning, 2026-2028
- Overview of Operation, 2024-2026H1
- Core Intelligent Driving Business
- L3/L4 Commercial Deployment Progress
- EAI Product Matrix
- EAI Layout: HoloBrain
- EAI Layout: HoloMotion-1
- New Product Release, 2026
- Cockpit-Driving Integration Chip Layout: Launch Starry Sky 6P Chip in 2026
- Organizational Restructuring of Intelligent Driving Team: Core Goal of Focusing on High-level Intelligent Driving R&D and Optimizing Resource Allocation
- Intelligent Driving Solutions (1)
- Intelligent Driving Configuration Solutions and Installations of Representative Vehicle Models Based on Horizon HSD, 2025-2026H1
- One-Model End-to-end + VLM Solution
- Introduce Reinforcement Learning + World Model
- Horizon HSD V2.0 Officially Released
- Active Safety of Horizon HSD V2.0
- Intelligent Driving Chip Series
- Benchmarking Analysis of Production Vehicle Models Based on Horizon J6P Intelligent Driving Chip
- Benchmarking Analysis of Horizon J6M-based Production Vehicle Models with NOA from Third-party Intelligent Driving Suppliers
- Benchmarking Analysis of Horizon J6M-based Production Vehicle Models with NOA from OEMs
- Overseas Layout: Intelligent Driving Products
- Overseas Layout: Strategy Roadmap
4.14 DeepRoute
- Vision: Build AI Infrastructure for the Physical World
- Unified Foundation Model in 2026
- Principle and Technical Highlights of Foundation Model (40B VLA) (1)
- Principle and Technical Highlights of Foundation Model (40B VLA) (2)
- Principle and Technical Highlights of Foundation Model (40B VLA) (3)
- Intelligent Driving Technology Evolution Roadmap
- Intelligent Driving Solutions (1): Driving-Parking Integration
- Intelligent Driving Solutions (2): DeepRoute IO Intelligent Driving Platform Based on End-to-end 1.0 Model
- Intelligent Driving Solutions (3): Intelligent Driving Solution Based on Qualcomm Chip Platform
- Intelligent Driving Solutions (4): DeepRoute IO 2.0 Intelligent Driving Platform Based on VLA Model
- DeepRoute IO 2.0: VLA 2.0 Model
- VLA VS. VLM+E2E
- VLA Model Architecture
- Urban NOA, 2025-2026H1: Comparative Analysis of Supported Vehicle Models, Installations and Technical Solutions
- L4 Layout (1): Overseas Market
- L4 Layout (2): Overseas Market
- L4 Layout: Domestic Market
4.15 WeRide
- Profile
- Disassembly of Business Model
- Overview of Financial Status, 2023-2025
- Five Major Product Matrixes
- Exploration of Business Models for Multi-scenario Application of L4 Autonomous Driving Technology
- Physical AI Flywheel
- Self-developed General Simulation Model: WeRide GENESIS
- "E2E + Traditional Pipeline" Dual-track Architecture
- E2E Model Architecture
- Installations of Urban NOA Algorithms in Production Vehicle Models, 2025-2026H1 (1)
- Installations of Urban NOA Algorithms in Production Vehicle Models, 2025-2026H1 (2)
- Hardware Architecture Diagram of Gen8 L4 Autonomous Driving System
- HPC 3.0
- Ecosystem Partners
4.16 CARIZON
- Profile
- Disassembly of Intelligent Driving Solution (1): Mass-produced Solutions
- Disassembly of Intelligent Driving Solution (2): High-level Intelligent Driving Planning, 2026-2027+
- C7H Intelligent Driving Chip Self-development Plan
- Underlying Logic of Self-developed SoC
- Intelligent Driving Cost Roadmap: 2026-2029
- First Production Vehicle Model with CARIZON Highway NOA, Intelligent Driving Solution and Installations, 2025-2026H1
- First Production Vehicle Model with CARIZON Urban NOA: ID. ERA 5S
- Production Vehicle Models and Intelligent Driving Solutions Based on Journey J6H Intelligent Driving Chip
4.17 neueHCT
- Profile
- Strategic Analysis 1: Realize Inclusive Intelligent Driving Downward and Enter L4 Scenarios Upward (1)
- Strategic Analysis 1: Realize Inclusive Intelligent Driving Downward and Enter L4 Scenarios Upward (2)
- Strategic Analysis 2: Go Out and Accelerate Overseas Market Layout
- Key Factors for Transnational Deployment of Intelligent Driving
- Intelligent Driving Overseas Products
- neueHCT Signs A Strategic Agreement with Here Technologies
4.18 MINIEYE
- Profile (1)
- Profile (2)
- Overview of Operation in Recent 4 Years
- Product Matrix
- Product Layout in Intelligent Driving (1)
- Product Layout in Intelligent Driving (2)
- iPilot Series: iPilot 4 and iPilot 4 Plus
- iPilot Series: iPilot 2/3/3 +
- iPilot Series: iPilot 1/1 eco/1 +
- iSafety Product: D2
- iPilot 1 Driving-Parking Integrated Hardware Structure
- iPilot 1 Driving-Parking Integrated System Composition
- iPilot 1 Driving-Parking Integrated Time-sharing Multiplexing
- Developments, Customers and Development Direction, 2022-2025
4.19 Joyson Electronics
- Profile of Joyson Electronics
- Product and Solution Portfolio of Joyson Electronics
- Overview of Operation of Joyson Electronics, 2024-2026H1
- Relationship Between JOYNEXT and Joyson Electronics
- Profile of JOYNEXT
- JOYNEXT Driving-Parking Integrated Domain Controller Products: nDrive 1H/nDrive M
- JOYNEXT Front-view All-in-one: nCam Series
- JOYNEXT Cockpit-Driving Integrated Solution: nCCU Series
- Mass-production Progress of JOYNEXT Intelligent Driving Solutions in 2026
- JOYNEXT L3/L4 Planning and Deployment Progress
- JOYNEXT EAI Layout: Overview of Product Matrix
- EAI Robot Head Assembly
- EAI Robot Cerebrum Host
- Dexterous Hand Product: Lingxi "TeleHand" Series
- EAI Energy Product Line
- Ecosystem Partners in Intelligent Driving Sector
4.20 Huaqin Technology
- Operation, 2023-2026H1
- Innovative Business 1: Automotive Electronics
- Innovative Business 2: Robot
- Layout in Robot Sector: Product Matrix and Commercialization Progress (1)
- Layout in Robot Sector: Product Matrix and Commercialization Progress (2)
- Product Layout in Automotive Electronics Sector
- Intelligent Driving Domain Controller Products: Based on NVIDIA Thor
- Intelligent Driving Domain Controller Products: Based on Horizon J6E/J6M
- Other Intelligent Driving Hardware Products
- Ecosystem Partners in Intelligent Driving Sector
4.21 TZTEK
- Profile
- Operation, 2023-2026H1
- Physical AI Layout
- Intelligent Driving Edge Physical AI Computing Unit: Starlight Series Product Matrix
- EAI Edge Physical AI Computing Unit: Starry Series Product Matrix
- EAI Edge AI Computing Unit: Starry H-N06
- Evolution Diagram of EAI Domain Controller Products
- EAI Edge AI Computing Unit: Starwise 001
- Layout in Intelligent Driving Sector
- Passenger Car Front-end Market Domain Controller Products: Horizon Series (1)
- Passenger Car Front-end Market Domain Controller Products: Horizon Series (2)
- L4 Domain Controller Products: Starlight M05
- L4 Domain Controller Products: TADC-J6P
- L4 Domain Controller Products: TADC-Orin-2
4.22 Lenovo Vehicle Computing
- Strategy 1.0 Era
- Strategy 2.0: Build Infrastructure for Physical AI Era (1)
- Layout in Robot Sector (1): RH1
- Layout in Robot Sector (2): EAH-1
- Intelligent Driving Product Layout: AD1/AH1/XH2 Domain Controller
- Mass-production Schedule and Customers of Intelligent Driving Products
4.23 Keboda
- Profile
- Overview of Operation, 2024
- Layout in Intelligent Driving Sector (1)
- Layout in Intelligent Driving Sector (2)
- Layout in Intelligent Driving Sector (3)
4.24 Baolong Automotive
- Profile (1)
- Profile (2)
- Overview of Operation, 2024-2026H1
- Entry Into EAI Sector
- Layout in Intelligent Driving Sector (1)
- Layout in Intelligent Driving Sector (2)
- Intelligent Driving Solutions
- Layout of Core Intelligent Driving Products
- Driving-Parking Domain Controller Products
- Front-view All-in-one (1)
- Front-view All-in-one (2)
- 11V ADAS Full Vision Perception Solution
- Radar Products
- 2nd-generation 4D Radar Products
- Ultrasonic Radar Products

ADAS and Autonomous Driving Tier 1 Suppliers Research Report, 2026 - Chinese Companies