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시장보고서
상품코드
2017480
중국 독립계 OEM의 ADAS 및 자율주행(2026년)Chinese Independent OEMs ADAS and Autonomous Driving Report, 2026 |
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2023-2025년 중국의 승용차 자율주행 탑재 구조는 단계적 업그레이드와 구조적 대체라는 뚜렷한 추세를 보이고 있습니다. 비자동운전 레벨(NL)과 저수준 자동운전(L1)의 탑재 수는 감소하는 추세이며, 주류 시장에서 점차 철수하고 있습니다. 이는 자율주행이 승용차의 표준 장비로 완전히 자리를 잡아가고 있음을 반증하는 것입니다. 한편, 기본적인 고수준 자율주행(L2)은 여전히 업계의 절대적인 기반이 되고 있습니다. 2025년 탑재량이 소폭 감소했지만 기본 시장은 안정적이며, 업계 성장의 초점은 분명히 높은 수준의 운전 지원으로 옮겨가고 있습니다. 그 중 L2.5 고속도로 NOA와 L2.9 도시 NOA가 핵심 성장 동력이 되고 있으며, 2025년에 각각 탑재량이 크게 증가하여 높은 수준의 NOA 기능 보급률이 빠르게 상승하고 있습니다. 반면, L2+의 탑재 수는 소폭 감소하여 L2+의 기능적 가치가 L2.5 및 L2.9와 같은 보다 완성도 높은 솔루션으로 대체되고 있음을 보여줍니다. 지능형 주행 시장 전체적으로는 '저사양의 도태, 기본 시장의 안정, 그리고 고사양의 급격한 확대'라는 뚜렷한 추세를 볼 수 있습니다.
OEM의 구조를 보면, 독립 브랜드와 합작 및 외자 브랜드는 자율주행 업그레이드에서 '급진적 건너뛰기'와 '보수적 점진적 진행'의 뚜렷한 차이를 볼 수 있습니다.
독립 브랜드는 낮은 수준을 건너뛰고 높은 수준의 자동 운전을 적극적으로 개발하고 있습니다. 비자동운전(NL) 비율은 2022년 60.9%에서 2025년 36.0%로 급격하게 감소하고, L0-L1의 저수준 자동운전은 거의 제로(1.3%)에 가까워졌습니다. 같은 기간 L2 이상의 고수준 자동운전 비율은 2배인 62.7%에 달했습니다. 그 중 L2.9 도시 NOA 탑재율은 2022년 2.1%에서 2025년 17.2%로 상승하여 '비자율주행이 주류'에서 '고수준 자율주행이 주류'로의 비약적인 구조 전환을 이루었습니다.
반면, 합작 및 외자 브랜드는 꾸준한 대체와 단계적 진화라는 보수적인 노선을 채택하고 있습니다. 비자동운전 비율은 40.0%에서 14.5%로 낮아졌지만, L0-L1의 저수준 자동운전은 여전히 16.4%로 상당한 점유율을 유지하고 있습니다. L2 이상의 고수준 자율주행 비율은 69.0%까지 상승했지만, 시장 전체적으로는 여전히 '비자동화+저수준+고수준'을 포함한 다단계가 공존하는 분산형 추세를 유지하고 있어 독립 OEM 브랜드의 급진적인 노선과 뚜렷한 대조를 이룹니다.
2026년은 중국 자동차 산업이 자율주행 능력에서 '양적 변화'에서 '질적 변화'로 전환하는 중요한 전환점이 될 전망입니다. ResearchinChina는 2023-2026년 중국 15개 독립 OEM의 자율주행 전략, 전략적 레이아웃, 기술 경로, 실행 진행 상황을 체계적으로 정리하여 4가지 핵심적인 내용을 정리했습니다.
발견1: 자율주행 경쟁의 핵심은 기반 아키텍처의 세대 혁신으로 옮겨갔습니다. 업계는 규칙 프로그래밍에 의존하는 전통적인 운전 지원에서 대규모 모델이 주도하는 물리적 AI의 시대로 완전히 진입하여 인간과 같은 의사 결정을 실현하고 있습니다.
발견2: 자율주행 기능은 '고정밀 지도가 필요 없는 도시 NOA'에서 'D2D(Door-to-Door)'로 진화하고 있습니다.
발견3: L3 기술은 2026년에 실용화되어 초기에는 상용화의 전환점을 모색하고, L4는 2027-2030년에 유행기를 맞이할 것으로 보입니다.
발견 4: 조종석과 운전의 융합이 가속화될 것으로 예상되며, 자동차는 빠르게 AI 슈퍼 에이전트로 진화하고 있습니다.
본 보고서는 중국의 자동차 산업에 대해 조사 분석했으며, 독립계 OEM의 ADAS와 자율주행에 관한 레벨별 탑재 대수 및 탑재율, 레이아웃 및 동향, 각 사의 제품 등의 정보를 전해드립니다.
Research on OEMs' Intelligent Driving: Era of Physical AI, Standard Configuration of D2D, and Initial Exploration of L3 Commercial Pilot Projects
From 2023 to 2025, the intelligent driving installation structure of passenger cars in China has shown a clear trend of stepped upgrading and structural substitution. Non-intelligent driving level (NL) and low-level intelligent driving (L1) have seen declining installation volume, and have gradually withdrawn from mainstream market, confirming that intelligent driving is fully becoming a standard configuration for passenger cars; basic high-level intelligent driving (L2) remains the absolute foundation of the industry. Although its installation volume slightly declined in 2025, the basic market is stable, and the industry's growth focus has clearly shifted to higher-level assisted driving. Wherein, L2.5 highway NOA and L2.9 urban NOA have become the core growth engines, with their installation volumes achieving a substantial jump in 2025 respectively, and the penetration rate of high-level NOA functions rises rapidly. Yet the installation volume of L2+ has shrunk slightly, indicating that its functional value is being replaced by more complete high-level solutions such as L2.5/L2.9. The overall intelligent driving market presents a clear pattern of "low configuration clearance, stabile basic market, and high-level outbreak".
In terms of OEM structure, independent brands and joint venture/foreign brands show a distinct differentiation of "radical skipping" and "conservative progressive progress" in intelligent driving upgrading:
Independent brands have skipped low-level and vigorously developed high-level intelligent driving. The proportion of non-intelligent driving (NL) dropped sharply from 60.9% in 2022 to 36.0% in 2025, and low-level intelligent driving of L0-L1 was almost zero (1.3%). In the same period, the proportion of high-level intelligent driving such as L2 and above doubled to 62.7%. Among them, the installation rate of L2.9 urban NOA has risen from 2.1% in 2022 to 17.2% in 2025, realizing a leapfrog structural shift from "dominated by non-intelligent driving" to "dominated by high-level intelligent driving".
In contrast, joint venture/foreign brands have adopted a conservative route of steady substitution and hierarchical iteration. The proportion of non-intelligent driving has dropped from 40.0% to 14.5%, but low-level intelligent driving of L0-L1 still retains a considerable share of 16.4%. Although the proportion of high-level intelligent driving of L2 and above has risen to 69.0%, the overall market still maintains a decentralized pattern of coexistence of multiple levels including "non-intelligent driving + low-level + high-level", forming a sharp contrast with the radical route of independent OEM brands.
2026 will become a key inflection point for China's automotive industry to move from "quantitative change" to "qualitative change" in intelligent driving capabilities. By systematically sorting out intelligent driving strategies, strategic layout, technical routes and implementation progress of 15 Chinese independent OEMs from 2023 to 2026, ResearchinChina has summarized four core insights.
Insight 1: the core of intelligent driving competition has shifted to generational innovation of underlying architectures. The industry is fully entering the era of physical AI driven by large models from traditional assisted driving relying on rule programming, realizing human-like decision.
The essence of physical AI is to deeply integrate physical laws, large models and world common sense into intelligent driving system, fundamentally solving the shortcoming of traditional AI's "physical blindness". Traditional artificially rule-driven intelligent driving can only identify targets such as vehicles, pedestrians and traffic cones, lacking an understanding of physical world and causal logic, and unable to predict behavioral intentions. It is prone to jamming, sudden braking, misjudgment and other problems in long-tail scenarios not covered by rules.
Physical AI intelligent driving, through multi-modal perception, not only identifies pixel information, but also understands 3D space, depth, motion state, object material and physical properties in an integrative way, accurately grasps spatial constraints, motion trends and causal relationships, realizes intention prediction and risk deduction, and completes low-latency execution of perception-inference-control through end-to-end closed loop, making decision closer to the fluency and robustness of human driving.
Intelligent driving algorithms in physical AI era can be roughly divided into the following routes:
VLA (Vision-Language-Action) route: integrates vision, language and action modalities to realize end-to-end intelligent decision from environmental perception and instruction understanding to behavior execution. Representative companies: Li Auto, XPeng, Deeproute.ai, Xiaomi, etc.
World Model route: Constructs an abstract representation of the virtual world by learning dynamic laws of the environment, and optimizes decision strategy of the Agent through low-cost simulation and deduction. Representative company: NIO
One-Model E2E + Reinforcement Learning + World Model: Directly maps raw input to action output, omits the link of manual feature design, and independently and iteratively optimizes decision strategy with the help of environmental reward signals. Representative companies: Momenta, SenseAuto
Hybrid Mode: For example, Geely launched the World Action Model, integrating VLA + End-to-End Safety Adversarial Model + World Model
Li Auto's intelligent driving technical route has undergone several switches: from HD map-dependent, rule-based solution to "end-to-end" -> "dual-system solution (end-to-end + VLM) -> VLA -> MindVLA-01". Li Auto's core of intelligent driving in 2026 is to fully switch to the MindVLA-01 unified foundation model, taking end-to-end VLA + world model + closed-loop reinforcement learning + self-developed chips as the path, aiming to build a general Agent in the physical world, and it will be mass-produced and launched on all-new L9 in 2026 Q2.
MindVLA-01 takes the native multi-modal MoE-Transformer as the unified foundation, and fuses three modalities of vision, language and action at the bottom; realizes accurate environmental perception through 3D space understanding (3D ViT + feedforward 3DGS); has multi-modal prediction and in-depth thinking with the help of predictive latent world model; relies on unified action generation (Action Expert + parallel decoding + discrete diffusion) to output automotive-grade stable control; and realizes rapid model iteration and efficient on-vehicle deployment through large-scale closed-loop reinforcement learning (MindRL) and the software and hardware collaboration of self-developed Mach chip, and comprehensively builds a physical AI intelligent driving brain integrating "seeing - thinking - acting".
Insight 2: intelligent driving functions are evolving from "HD map-free urban NOA" to standard configuration of "Door-to-Door (D2D)".
Emerging OEMs such as XPeng and Li Auto launched parking space -> city -> highway uninterrupted D2D intelligent driving in January 2025. Major mainstream OEMs have accelerated the implementation of D2D from 2025 to 2026, and the industry has entered the era of full-process intelligent driving from "segmented assistance".
In terms of technical routes, emerging OEMs generally adhere to the strategy of independent R&D and tackling key problems to seize technological high ground. XPeng, NIO and Li Auto all adopt a combination of independent algorithms and high-compute chips, relying on about 1000TOPS-level computing power to support full-scenario map-free D2D, with significant technological leadership. Among them, Li Auto plans to launch the self-developed and mass-produced intelligent driving chip Mach 100 for the first time on All-new Li Auto L9 Livis in Q2 2026. The chip adopts a data stream native architecture, which can be deeply adapted to the MindVLA-01 VLA large model, with a single-chip computing power of up to 1280TOPS. In terms of the computing power of self-developed chips (Li Auto Mach 100 (1280TOPS) > NIO (1000TOPS) > XPeng (750TOPS)), the independent R&D camp overall enjoy a bigger lead than the cooperative camp.
Traditional OEMs are more inclined to a pragmatic path of strategic cooperation and rapid implementation. Huawei's partners such as SAIC and BAIC directly use Huawei ADS 4.0 + Ascend chips to realize rapid adoption of D2D functions, with high technology multiplexing rate; Chery and BYD adopt mature solutions such as Horizon and NVIDIA, focusing on cost-effective D2D to cover the mainstream vehicle market of RMB150,000-300,000. It is worth noting that D2D functions do not rely on extremely high computing power. Third-party chips of 200TOPS level (such as Huawei Ascend 610, single Orin-X) can support the full-scenario link, and higher computing power is mostly reserved for technical redundancy and subsequent high-level autonomous driving upgrades.
For chip pattern, the D2D intelligent driving market presents a tripod pattern among NVIDIA, Huawei and Horizon, and self-developed chips are becoming the core technological moat of leading emerging OEMs. NVIDIA is still the absolute mainstream of general-purpose chips in the industry, with Thor-U prevailing among cooperative models at 700TOPS level. Huawei has deeply bound with traditional OEMs with its "chip + ADS full-stack solution" and quickly cut into high-end intelligent driving market. Horizon focuses on mid-end and mainstream mass market, achieving extensive coverage with cost-effective solutions. Simultaneously, self-developed chips have become a key to the differentiation of emerging OEMs, realizing the maximization of model efficiency and intelligent driving experience through deep customized collaboration of computing power and algorithms.
In the future, driven by the maturation of end-to-end large model technology, the cost reduction of intelligent driving chips and scenario closed loop, D2D is expected to evolve from a high-end optional configuration to a mainstream standard configuration, and become the core competitiveness benchmark of intelligent vehicles from 2027 to 2028. With the standardization of D2D functions, competition will shift from "available or unavailable" to "good or bad". The adaptability to extreme scenarios (mountain cities, underground garages, rain and snow weather), zero disengagement and cost control will become core decisive factors.
Insight 3: L3 technology is ready in 2026, exploring the commercial inflection point initially, and L4 will usher in a boom period from 2027 to 2030.
(1) Initial exploration of L3 commercial inflection point: in March 2026, Changan Automobile obtained the official special license plate for L3 autonomous driving, which means that "verification of L3 technology maturity is completed and it has entered the early stage of mass production and commercialization". Previously, vehicles of L3 and above could only operate in closed parks or specific test areas. After obtaining the official license plate, vehicles are allowed to carry out on-road use pilots on expressways and urban trunk roads in Chongqing (such as Inner Ring Expressway and Yudu Avenue). At present, these vehicles are operated by Changan's mobility company, and consumers can now experience them by ride hailing instead of buying them directly. This method can reduce the uncertainty of initial implementation through professional management. As of April 2026, two OEMs, Changan and BAIC, have obtained the official special license plate for L3, with the qualification for commercial pilots on public roads.
(2) OEMs' strategic deployment of L3/L4 intelligent driving: two major routes of L3 skipping and L3+L4 parallel development
At present, the industry has formed two clear strategic paths for high-level intelligent driving:
One is L3 skipping route: take Robotaxi as the core breakthrough, skip L3 mass production in strategy and directly develop L4 technology. Although L3 is compliant, the investment (computing power/redundancy) in it is close to L4 but the experience is limited, leading XPeng/BMW to choose to skip L3. Taking XPeng as an example, it plans to launch 3 mass-produced OEM L4 Robotaxis in 2026, start the normal road test of L4 autonomous driving in H1 2026, officially launch the demonstration operation of Robotaxi in H2 2026, complete the tripartite verification of technology, customers and business, and realize non-safety officer commercial operation in 2027.
The second is L3+L4 parallel development route. The core choice of current mainstream OEMs follows a steady rhythm of "qualification verification -> mass production internal testing -> L3 launch -> L4 implementation", covering both private passenger cars and Robotaxi tracks simultaneously, with a complete technical route and clear mass production rhythm.
In terms of time layout, 2025-2026 is a critical period for L3 road testing, mass production & access and product delivery. SAIC, Geely, BAIC and GAC have clarified product technical readiness of L3 intelligent driving capabilities for private vehicles in 2026 (pending legal permission), marking 2026 as the "mass production first year" of China's L3 autonomous driving. Car owners will legally obtain the right to take their hands off the steering wheel on specific roads (highways/expressways) for the first time. 2026 is the starting point for large-scale operation of Robotaxi. Mainstream OEMs (SAIC, Geely, BYD, BAIC, GAC) all anchor the substantive commercial implementation of L4 in 2026, and focus on core areas of first-tier cities such as Shanghai, Shenzhen and Beijing.
In the medium and long term, 2027-2030 is a window period for the implementation of L4 in complex scenarios and the wide adoption in private cars. Changan and Dongfeng have clarified large-scale mass production of L3/L4 by 2030, indicating that the industry will fully penetrate from the "business/operation end" to "consumer users" in the next 5 years.
Insight 4: Cockpit-driving fusion is expected to accelerate, and automobiles are evolving rapidly into AI Super Agents.
The ultimate form of intelligent vehicles is a digital living body and mobile intelligent terminal with autonomous capabilities. At present, the industry is gradually developing from the architecture of separate cockpit and driving domains to the direction of cockpit-driving fusion, cockpit-driving integration and chassis full-domain fusion, and eventually moving towards the form of intelligent mobile robots with autonomous decision and autonomous execution capabilities.
In March 2026, IM Motors launched the IM Ultra Agent, an AI super agent which realizes in-depth collaboration of three domains of intelligent driving, intelligent cockpit and chassis based on IM Fusion Nova architecture. At the hardware level, full chassis-by-wire is the foundation for realizing full-domain vehicle control. IM Motors LINGXI Digital Chassis adopts full-stack wire-controlled solution, with four-wheel steering response time as low as 20ms, and response efficiency about 4 times that of traditional steering system. It is also equipped with an aviation-grade triple safety redundancy architecture, with the system failure probability lower than 10FIT, providing a stable and reliable hardware foundation for high-level intelligent driving and vehicle dynamic control.
At the software level, vehicles are equipped with Alibaba Tongyi Qwen large model, providing multi-modal interaction and continuous evolution capabilities for IM Ultra Agent. IM AD ZETA, an intelligent driving system jointly developed with Momenta, adopts a new-generation reinforcement learning large model as the physical AI foundation oriented to L4 autonomous driving, realizing the integrated upgrade of perception and decision capabilities on vehicles. The large model can realize real-time linkage between decision layer, intelligent driving domain and chassis domain, and support one-sentence voice commands directly to vehicle control, making cross-domain collaboration and full-scenario assisted driving move from concept to practical application.
When intelligent driving, intelligent cockpit and chassis are integrated, a single AI command can coordinate the intelligent cockpit and intelligent driving AI large models:
Scenario example: During the evening rush hour after work, the user issues the instruction: "I'm too tired, want to go home, and buy a cup of hot Americano by the way, preferably without getting off the car to pick it up."
The on-device Alibaba Tongyi Qwen AI large model completes natural language understanding and user intention disassembly, dividing the requirements into three categories:
Vehicle control requirements: "I'm too tired" -> activate the seat massage function, executed by vehicle control Agent;
Life service requirements: "Buy a cup of hot Americano" -> purchase hot Americano coffee, link with IM Motors' takeaway Agent to complete coffee selection, payment and pick-up point association;
Mobility path requirements: first go to the pick-up point, then return to the destination to go home.
Finally, the IM AD ZETA intelligent driving large model unifies overall planning, completes dynamic path planning, real-time road condition prediction and full-process intelligent driving execution, realizing a one-stop experience of "picking up food without getting off the car + automatic homecoming".
From 2023 to 2025, Chinese passenger car intelligent driving market has completed the initial iteration from "available or unavailable" to "good or bad". The installation structure presents a clear pattern of " stabile basic market and high-level outbreak", and the route differentiation between independent and joint venture brands has become more prominent. In 2026, a key inflection point for China's automotive intelligent driving to move from "quantitative change" to "qualitative change", underlying architecture has fully entered the era of physical AI, with multiple technical routes such as VLA and world model developing in parallel. D2D functions are developing faster from high-end optional configuration to mainstream standard configuration, L3 autonomous driving is ushering in initial commercial exploration, and cockpit-driving fusion is promoting the steady evolution of automobiles into AI Super Agents. The dual paths of chip self-development and strategic cooperation are reshaping the industry competitive pattern. It is foreseeable that in the next two years, the competition in intelligent driving capabilities will no longer be limited to the simple stacking of algorithms and computing power, but will more depend on enterprises' systematic construction of physical world understanding, data closed-loop efficiency, depth of software and hardware collaboration and breadth of scenario coverage. In this process from quantitative change to qualitative change, the real decisive factor will belong to those players who can take the lead in realizing in-depth integration of "cognitive intelligence" and "action intelligence".
ADAS Installation Volume by Intelligent Driving Level in Chinese Market, 2023-2025
ADAS Installation Volume and Installation Rate of Independent + Joint Venture/Foreign Brands by Intelligent Driving Level, 2023-2025 (1)-(2)
Three Major Development Trends of ADAS: Independent Brands VS. Joint Venture/foreign Brands
ADAS Function Installation Volume & Installation Rate of Independent Brands, 2023-2025: By Function (1)-(2)
L2 and Above ADAS Installation Volume and Installation Rate of Independent Brands, 2023-2025
L2 and L2+ ADAS Installation Volume of Independent Brands: By Brand
L2 and L2+ ADAS Installation Rate of Independent Brands: By Brand
Sales Volume and Penetration Rate of L2.5 Models (by Price), 2023-2025
Sales Volume and Penetration Rate of L2.5 Models (by Brand), 2023-2025
L2/2.5/2.9 ADAS Installation Volume and ADAS Supplies List: By Brand (1)-(2)
L2/2.5/2.9 ADAS Installation Volume of Independent Brands: By Model
L2/L2+/2.5/2.9 ADAS Installation Volume of Independent Brands: By Price
L2/L2+/2.5/2.9 ADAS Installation Rate of Independent Brands: By Price
Market Share of L2 and L2+ ADAS Suppliers in Chinese Passenger Car Market
Market Share of L2.5 and L2.9 ADAS Suppliers in Chinese Passenger Car Market
Penetration Rate of Intelligent Driving by Level in Chinese Passenger Car Market, 2023-2030E