整車廠商及一級供應商的L3級自動駕駛技術展望研究（2025）

目前，城市自動駕駛（NOA）功能已廣泛應用於售價15萬元人民幣以上的車型，競爭障礙的消失加速了產業同質化進程。現階段，L3級技術對於汽車廠商而言是吸引用戶、提升品牌價值的關鍵突破。唯有在L3級技術上取得突破，廠商才能滿足使用者對更安全、更可靠駕駛體驗的高階需求，並建立差異化優勢。 L3級技術不僅是廠商技術能力的考驗，也是提升品牌價值的關鍵一步。作為邁向真正自動駕駛的關鍵一步，L3級技術必須克服監管合規、先進感測器融合等諸多挑戰，其可靠性直接反映了廠商的技術實力。率先量產L3技術的汽車製造商可以迅速樹立 "技術領先" 和 "高端智慧" 的品牌形象，進一步提升所有車型的價值，並拉開與競爭對手的差距。

以吉利馳銳為例，其智慧駕駛技術的演進路徑顯然面向L3。2023年12月，該公司發布了自主研發的全端智慧駕駛系統，實現了高速公路NOA和APA功能。 2024年12月，推出了完全無需地圖的城市NOA功能。計畫於2026年6月實現門到門（D2D）功能。 L3和L4的突破將是未來科技演進的核心方向。

趨勢1：消費者對高階自動駕駛功能的需求正在重塑市場，33%的消費者希望從城市NOA升級到L3/L4功能。

數據顯示，2023年至2025年，中國新上市車輛中配備自動駕駛功能的乘用車數量呈現顯著兩極化。 L2.5/L2.9高階自動駕駛功能大幅成長，而傳統的L1-L2+功能則持續下降，清楚展現了產業向智慧化和加速迭代的趨勢。截至2023年，L2.5/L2.9自動駕駛功能在市場上仍屬小眾，分別僅佔新上市車輛的4.57%及3.3%。然而，從2025年1月到4月，兩者均實現了快速成長。配備L2.5智慧駕駛功能的新車比例飆升至34.8%，而配備L2.9功能的新車比例也達到了34.82%，市場滲透率極高。相較之下，同期傳統L1-L2+級自動駕駛功能的安裝率則呈現下降趨勢。這種兩極化清晰地表明，消費者對高階自動駕駛功能的偏好正在改變市場供給結構，高階自動駕駛功能已成為新車市場競爭的核心焦點。

此趨勢的核心推動力是城市NOA和高速公路NOA功能的加速普及。這兩項功能不僅在推動自動駕駛技術從概念走向實際應用方面發揮關鍵作用，而且在消費者教育方面也發揮著至關重要的作用。在通勤和長途旅行等頻繁駕駛場景中，消費者直觀地體驗到高級智慧駕駛的價值，並逐漸建立對這項技術的認知和信任。 "實際應用→滿意度→渴望進階功能" 的需求循環進一步加速了使用者對進階智慧駕駛的期望。

此外，透過與ADAS的長期共存，使用者逐漸了解了現有自動駕駛功能的局限性，並建立了一定的信任度。然而，L1-L2級自動駕駛功能正接近其功能極限，例如難以應對複雜的城市道路狀況。這項限制正促使用戶需求轉向更進階的自動駕駛功能。

麥肯錫 "2025年中國汽車消費者洞察" 調查數據直接印證了 "科技採納→消費者認知" 的良性循環。與2023年相比，消費者對自動駕駛功能的接受度與滿意度顯著提升。尤其是在核心功能方面，到2024年，46%的用戶將對其目前的城市NOA（帶導航的自動駕駛）系統感到滿意，而33%的城市NOA用戶明確希望將其現有的城市NOA系統升級到L3/L4級別。

本報告透過15家整車廠商（包括8家中國廠商和7家國際廠商）和9家一級供應商（涵蓋半導體、雷射雷達、網域控制器、ADAS等）的詳細調查，深入分析了中國汽車產業及L3級自動駕駛的核心佈局。

目錄

第一章：L3有條件自動駕駛的商業化進程及政策解讀

• L3有條件自動駕駛的定義與分類標準
• 關於L3有條件自動駕駛的國家法律、法規與政策詳述
• L3有條件自動駕駛的國家標準
• 關於自動駕駛的全球政策和法規
• L3實施的核心標準：自動駕駛冗餘系統的設計與價值

第二章：L3製造商智慧駕駛技術路徑的基準分析及產業發展趨勢

• 國際智慧駕駛市場及L3市場滲透率
• 中國及全球L2至L5自動駕駛市場滲透率（2025-2035）
• L3級有條件自動駕駛的推動因素（1）
• L3級有條件自動駕駛的推動因素（2）
• 消費者對高階智慧駕駛功能的需求正在改變市場結構
• L3級有條件自動駕駛實現帶來的新商業成長（1）
• L3級有條件自動駕駛實現帶來的新商業成長（2）
• 2025年至2030年，中國L3級自動駕駛市場將強勁成長，到2035年，中國L3級自動駕駛市場的潛在收入將達到1億美元，利潤預計將達到70億美元。
• 政策與技術雙重驅動：中國L3/L4自動駕駛市場進入大規模商業化快車道（2025-2030年）
• 四大關鍵技術模組的同步升級將共同推動自動駕駛技術從L3逐步邁向L5。
• 產業專家對L3有條件自動駕駛發展的預測（1）
• 產業專家對L3有條件自動駕駛發展的預測（2）
• L3/L4智慧駕駛的發展節奏
• L3大規模應用的五大挑戰
• L3應用的技術挑戰（1）
• L3應用的技術挑戰（2）
• L3應用的技術挑戰（3）
• 趨勢一：清晰窗口期－眾多國內OEM廠商將2025-2027年定位為這是L3級自動駕駛大規模生產和安裝的關鍵時期，硬體預先整合成為主流策略。
• 國內廠商L3佈局的四大特點
• 趨勢二：國際陣營集中佈局L3級自動駕駛，引發全球智慧競爭。
• 趨勢三：感測器廠商、算力平台和智慧駕駛演算法供應商攜手合作，推動L3級自動駕駛的大規模部署和向L4級自動駕駛的演進。
• 趨勢四：主要汽車廠商採用L3和L4雙線佈局策略，在技術、資金和策略層面進行綜合考量。 (1)
• 趨勢四：主要汽車廠商採用L3和L4雙線佈局策略，在技術、資金和策略層面進行綜合考量。 （2）
• 趨勢五：L3技術路線呈現 "三支柱" 模式：自主研發、合作研發與自主研發雙軌制以及外部供應商。 （1）
• 趨勢五：L3技術路線呈現 "三支柱" 模式：自主研發、合作研發與自主研發雙軌制以及外部供應商。 （2）
• 趨勢五：L3技術路線呈現 "三支柱" 模式：自主研發、合作研發與自主研發雙軌制以及外部供應商。 （3）
• 趨勢六：多通道雷射雷達越來越受到OEM廠商的青睞。 （1）
• 趨勢六：多通道光達已成為OEM廠商確保L3智慧駕駛佈局和安全冗餘的重要選擇。 （2）
• 趨勢 7：L3 智慧駕駛的運算能力需求正在急劇增長，1000 TOPS 將成為主流標準。 （1）
• 趨勢 8：隨著智慧駕駛水準的提高，對運算能力、資料和訓練資源的需求將逐步成長。
• 趨勢 9： "端到雲端協同" 將成為大多數汽車製造商 L3 智慧駕駛佈局的核心架構，從而克服運算能力的限制。
• 趨勢10：L3智慧駕駛邁向端到端2.0，汽車製造商聚焦 "VLA+端到雲端協作+世界模型" 架構（1）
• 趨勢10：L3智慧駕駛邁向端到端2.0，汽車製造商聚焦 "VLA+端到雲端協作+世界模型" 架構（2）

第三章 OEM L3智慧駕駛產品與技術

• Geely-ZEEKR
• SAIC-IM Motors
• XPeng Motors
• Li Auto
• Huawei
• GAC Group
• Voyah
• Changan Automobile
• BMW
• Mercedes-Benz
• Audi
• Stellantis
• Honda
• Rivian
• Tesla
• 其他國外汽車廠商

第四章：一級供應商 L3 智慧駕駛產品及技術

• NVIDIA
• Horizon Robotics
• Qualcomm
• Black Sesame Technologies
• Hesai Technology
• RoboSense
• Bosch
• Mobileye
• Zhuoyu Technology

L3 Research: The Window of Opportunity Has Arrived - Eight Trends in L3 Layout of OEMs and Tier 1 Suppliers

Through in-depth research on 15 OEMs (including 8 Chinese and 7 foreign OEMs) and 9 Tier 1 suppliers (covering chips, lidar, domain controllers, ADAS, etc.), ResearchInChina analyzes the core layout of L3 intelligent driving of the two groups. For OEMs, this report comprehensively combs through their L3 intelligent vehicle development strategies, key launch nodes, and first L3 models, as well as sensor hardware solutions, intelligent driving chip selection, technology path planning, and redundancy strategy design. For Tier 1 suppliers, it focuses on exploring the R&D and implementation progress of their L3 intelligent driving products. Based on the above research, it finally summarizes eight major development trends of L3 intelligent driving in the Chinese market over the next 3 years.

Currently, urban NOA has been extended to vehicle models priced at RMB150,000. The competitive barrier disappears and industry homogenization intensifies. At this time, L3 has become a key breakthrough for OEMs to compete for users and achieve brand upgrading. Only by making breakthroughs in L3 can OEMs meet users' high-level demands for "more worry-free and safer" driving, and establish differentiated advantages. L3 is not only a touchstone for technical strength but also an amplifier of brand value. As a crucial step towards true autonomous driving, L3 needs to overcome challenges such as regulatory compliance and advanced sensor fusion, and its reliability directly reflects OEMs' technical capabilities. OEMs that take the lead in mass-producing L3 can quickly establish labels of "technological leadership" and "high-end intelligence", drive up the value of the full range of their models, and widen the gap with competitors.

In Geely Zeekr's case, its intelligent driving evolution path clearly points to L3: launched a self-developed full-stack intelligent driving system in December 2023, realizing highway NOA and APA; fully rolled out mapfree urban NOA in December 2024; will implement Door-to-Door (D2D) function in June 2026. Making breakthroughs in L3 and L4 is the core direction of its next technical evolution.

Trend 1: Consumers' Demand for Higher-level Intelligent Driving Functions Is Reshaping the Market Structure, with 33% of Consumers Hoping to Upgrade Urban NOA to L3/L4 Functions.

From the data of newly launched vehicles, the installation of intelligent driving in passenger cars in China featured a greatly polarized pattern from 2023 to 2025: L2.5/L2.9 high-level intelligent driving functions enjoyed leapfrog growth, while traditional L1-L2+ intelligent driving functions continued to decline, clearly reflecting the trend of faster industry intelligence and iteration. In 2023, L2.5 and L2.9 intelligent driving were still niche configurations in the market, with installation rates of only 4.57% and 3.3% in newly launched models, respectively. However, from January to April 2025, both boomed: the proportion of new cars equipped with L2.5 intelligent driving soared to 34.8%, and those with L2.9 even took a 34.82% share, showing a very high market penetration. In sharp contrast, the installation rate of traditional L1-L2+ intelligent driving functions was on the decline during this period. This polarization trend clearly indicates that consumers' preference for higher-level intelligent driving functions has begun to reshape the market supply structure, and high-level intelligent driving is gradually becoming the core focus of competition in the new car market.

The core driving force behind this trend lies in faster implementation of urban NOA and highway NOA functions. These two types of functions are not only key carriers for autonomous driving technology to move from "concept" to "practical application" but also assume the important role of "consumer education". In high-frequency scenarios such as daily commuting and long-distance driving, they allow consumers to intuitively perceive the value of high-level intelligent driving and gradually establish cognition and trust in the technology. The demand closed loop of "practical use - satisfaction - desire for upgrading" further catalyzes users' expectations for higher-level intelligent driving.

Moreover, as users have long "coexisted" with ADAS, they have gradually figured out the capability boundaries of existing intelligent driving and have built up basic trust. However, the functional ceiling of L1-L2 intelligent driving, e.g., difficult to cope with complex urban road conditions, is about to be reached, and this limitation is continuously encouraging user demand to shift to higher-level intelligent driving.

The research data from the China Auto Consumer Insights 2025 by McKinsey directly confirms the positive cycle of "technology penetration - consumer recognition". Compared with 2023, consumers' acceptance and satisfaction with autonomous driving functions have significantly improved. Specifically for core functions, 46% of users were satisfied with the current urban NOA in 2024, and 33% of urban NOA users clearly hoped to upgrade the existing urban NOA to L3/L4.

Trend 2: From the Supply Side, the Window of Opportunity Brought by L3 Is Clear, Multiple Chinese OEMs Have Taken the Period from 2025 to 2027 as a Critical Phase for Mass Production and Installation of L3 Intelligent Driving, and Pre-embedded Hardware Becomes the Mainstream Strategy.

From the supply side, the window of opportunity for commercialization of L3 intelligent driving is clear. Leading OEMs such as NIO, Xpeng, Geely, and Huawei-affiliated OEMs have regarded 2025-2026 as the critical period for mass production. Pre-embedded hardware has become the mainstream industry strategy: by pre-equipping components such as lidar and high-compute chips, they can quickly activate functions to win a place and gain first-mover advantages after regulatory relaxation.

Their commercial implementation follows a clear path of "highway -> urban area", "closed -> open", and "business -> consumer". Policy breakthroughs and cost reduction constitute a dual engine: in 2025, Beijing and Shanghai have explicitly defined the liability division for highway L3 accidents, and highway L3 functions of players such as Huawei and Xpeng have been delivered for production vehicles; the significant reduction in cost of hardware such as lidar has paved the way for technology popularization. Wherein, highway scenarios have become the first "test field" to implement L3 due to highly structured roads and easy unification of regulations.

Although the consumer market still needs to break through the bottlenecks of user trust and cost sensitivity, as the industry chain matures, it is expected that L3 models will enter the mid-range price segment in the next 3 years. The concentrated mass production of multiple OEMs during 2025-2026 indicate that L3 technology has entered a new phase of "large-scale commercial implementation" from "testing and verification".

Trend 3: L3 and L4 Dual-line Layout: OEMs' Technical Collaboration and Ecosystem Competition

Some leading OEMs are betting on both L3 and L4, which is essentially a deep binding at the technology, capital, and strategy levels. By means of two-way technical enablement and commercial complementarity, they build an irreproducible competitive barrier.

At the technology level, L3 and L4 form a "symbiotic evolution" closed loop. Both are highly universal in hardware such as lidar and high-level intelligent driving chips, as well as in automotive redundancy design, with interoperable core capabilities. L3 production vehicles can collect a mass of edge case data such as "takeover" scenarios, becoming a "training library" for L4 algorithms. The high-level algorithms of L4, after being downscaled to designated scenarios, can directly improve the performance reliability of L3. This synergy of "data feedback + technology downscaling" enables the two technology paths to achieve an iteration efficiency of 1+1>2.

At the commercial level, both form an ecosystem combination of "short-term blood transfusion + long-term occupation". L3 quickly recovers funds and verifies the market through private car sales, transfusing L4 R&D. L4 targets the Robotaxi market worth RMB1 trillion and lays out the future mobility ecosystem. More importantly, private cars and Robotaxi fleets can share resources such as HD maps and cloud platforms to form operational synergy. OEMs that take the lead in overcoming L4 are expected to become the definers of the future mobility ecosystem. This dual-line strategy is not only a pragmatic choice to reduce technical R&D risks but also a strategic layout to have a say in the intelligent driving era.

Trend 4: L3 Technology Path Shows a "Three-legged Stool" Pattern: Independent R&D, Dual-track (Co-development + Independent R&D), and External Suppliers.

In China, L3 intelligent driving technology path has formed a "three-legged stool" pattern of "full-stack independent R&D, co-development + independent R&D, and external cooperation". This is essentially a differentiated choice of OEMs based on their technical reserves, capital strength, and strategic rhythm - seeking the optimal balance between "technical sovereignty" and "commercial efficiency".

Full-stack Independent R&D: Exchange high investment for long-term technical moat

Leading OEMs such as NIO, Xpeng, Li Auto, and Geely have anchored full-stack independent R&D, the core of which is to master the full-link dominance of underlying hardware (such as self-developed chip adaptation) and top-layer algorithms (end-to-end large models). This model can build an exclusive data closed loop, continuously collect edge data such as "takeover" scenarios via production vehicles to reversely feed algorithm iteration, and build an irreproducible technical barrier. However, the cost is high, and there are technical trial and error and cycle risks.

Co-development + Independent R&D: Balance independent control and R&D efficiency

Cases such as SAIC IM's co-development with Momenta and Dongfeng Voyah's "strategy implementation by brand" (independent R&D for Taishan + Dreamer equipped with Huawei's solution) represent the flexibility of the mixed route. Its core logic is "independent R&D or co-development of core technologies + outsourcing of the non-core": OEMs control key links such as decision algorithms, and entrust heavy-asset links such as perception fusion and data annotation to professional partners. This not only avoids resource waste of fully independent R&D but also gets rid of the risk of depending on single ones. This model has become the preferred choice for most traditional OEMs. For example, BYD, while independently developing "God's Eye", works with Momenta to implement high-level functions.

Choosing External Suppliers: Use mature solutions to take a place quickly and shorten L3 R&D cycle

Typified by Huawei's cooperation with OEMs, this model quickly enters the market by virtue of "packaged solutions". With its ADS 4.0 system featuring integration of "chip - algorithm - redundancy architecture", Huawei covers more than 7 OEMs. It empowers JAC STELATO S800 to implement highway L3 intelligent driving and build a flagship intelligent model.

Trend 5: Multi-channel Lidar Becomes an Important Choice for OEMs to Lay out L3 Intelligent Driving and Ensure Safety Redundancy.

Global L3 intelligent driving sensor solutions show a clear differentiation: only Tesla and Xpeng adhere to the vision-only route, while other mainstream OEMs inside and outside China and pilot manufacturers take lidar as the core configuration. Chinese ones include Huawei-affiliated OEMs, Geely, GAC, SAIC IM, and NIO; foreign ones such as BMW, Mercedes-Benz, Honda that have piloted L3, and European and American giants that have not conducted road tests.

Markus Schafer, CTO of Mercedes-Benz, pointed out that L3 requires multi-sensor redundancy to ensure safety, and as vehicle speed increases, higher-performance lidar is needed for long-distance small obstacle detection, reserving sufficient processing time for the system and the driver. Chen Xiaozhi, Chief AI Technology Officer of Zhuoyu Technology, also emphasized that the hardware safety redundancy of L3 requires sensor complementarity (not just relying on algorithms), and the core value of lidar is to provide safety redundancy.

As the core indicator of lidar resolution (representing the number of vertical laser beams), the number of channels directly matches the upgrade of intelligent driving levels: early 16-channel lidars are suitable for low-speed scenarios, the 32/64-channel serve low-to-mid level ADAS, and currently the 128-channel have become a mainstream automotive solution. L3 models require >=128-channel lidars, and the mainstream configuration has been upgraded to 192-channel, 520-channel, or even 700-channel. The dense point cloud brought by multi-channel lidars can realize accurate recognition of small obstacles 170 meters away, which is an essential safety requirement for L3 in the scenario of liability transfer.

Trend 6: The Computing Power Required for L3 Intelligent Driving Shows an Exponential Leap, with 1000TOPS Becoming the Mainstream Threshold.

The computing power required for autonomous driving is not blindly piled up, but is deeply bound to levels, scenarios, and algorithm models. L2 deals with basic scenarios such as lane keeping and adaptive cruise control, and >=50TOPS dense computing power is sufficient. Excessive stacking will only lead to resource waste and high cost.

Due to the need to take on the main driving responsibility, L3 needs to cope with complex urban traffic, various traffic participants, dynamic environmental changes and other scenarios. It requires large-scale neural network models for real-time reasoning. The expansion of end-to-end large model parameters results in the demand for higher vehicle computing power. After combining end-to-end technology and VLM into VLA, the vehicle-side model parameters become larger. It not only needs efficient real-time reasoning capability but also has the ability to recognize the complex world and give suggestions. Deploying VLA models will pose quite high requirements for vehicle chip hardware. The demand for sparse computing power directly jumps to 1000-2000TOPS level, and the dense computing power threshold rises to >=200TOPS.

The sparse acceleration ratio varies in scenarios. For structured roads such as highways, the proportion of effective information exceeds 50%, the sparsity is low, the acceleration ratio is only 2-3 times, and the equivalent computing power of 400-600TOPS is sufficient. For long-tail scenarios of complex urban road conditions, the proportion of effective information is lower than 10%, the sparsity is high, the acceleration ratio can reach 8-10 times, and the equivalent computing power can be increased to 1600-2000TOPS, which accurately matches the computing power requirements of complex environments.

Trend 7: Device-cloud Collaboration, the Core Architecture for Breaking through Computing Power Constraints in L3 intelligent Driving Layout

"Device-cloud collaboration" has become a consensual choice for mainstream OEMs laying out L3 intelligent driving to break through computing power. Its essence is to solve the core contradiction between the performance requirements of large models and the limitations of vehicle computing power through the division of labor of building capabilities in the cloud and implementing applications on vehicles. Xpeng's technical practice is a typical example of this path.

The underlying technical logic is driven by the Scaling Law: the number of parameters and data volume directly determine the performance of models, but vehicle computing power is difficult to support the operation of 10-billion-parameter large models. By training models with 1 billion to 72 billion parameters and feeding more than 20 million clips of video data, Xpeng's team first verified that this law is still available in an autonomous driving VLA model. The 72-billion-parameter cloud large model can accurately handle complex scenarios, and then generate a small model suitable for vehicles using distillation technology, which can preserve core capabilities to the greatest extent possible and break through computing power constraints.

The "Xpeng World Foundation Model" released in April 2025 is the carrier of implementing this logic. As a cross-terminal "super parent body", it realizes full-link production through the "cloud model factory" built by Xpeng: forming a closed loop from multi-modal pre-training, reinforcement learning post-training, to model distillation and vehicle-end deployment. Relying on the 10,000-card intelligent computing cluster, the iteration cycle is compressed to an average of once every 5 days.

Its evolution core lies in the "Dual Loop Collaboration Mechanism": the Inner Loop completes the efficient transfer of large model capabilities to the vehicle-end in three stages of "pre-training - reinforcement learning - distillation"; the Outer Loop continuously reversely feeds cloud model iteration relying on the perception data of real vehicles, user feedback and extreme cases, completely solving the problem of disconnection between simulation and real scenarios. This closed loop of "training intelligence in the cloud, using intelligence at the vehicle-end, and returning intelligence with data" not only enables a small vehicle-end model to have generalization capabilities close to that of large models but also realizes continuous self-evolution of intelligent driving systems, laying a technical foundation for safe implementation of L3 and advancement to L4.

Trend 8: L3 Intelligent Driving Moves Towards End-to-end 2.0, and VLA Becomes One of the Mainstream Routes to Break Through Experience Bottlenecks for L3 Intelligent Driving.

L3 intelligent driving is moving from "modular splicing" to the "end-to-end 2.0" era. The core evolution logic is the deepening of multi-modal fusion. The combination of "VLA (Vision-Language-Action model) + device-cloud collaboration + world model" is becoming the mainstream path to break through technical limitations and realize commercial use. SAIC IM's three-stage evolution route accurately embodies this leap process from "technology availability" to "experience reliability".

2025 is the "foundation building period": by implementing the one-model end-to-end (E2E) architecture, break the module barriers of traditional perception, decision, and control, and realize lossless information transmission and global optimization, with L3 having been 90% production-ready in terms of technical maturity. This step solves the core sore points of "information loss and accumulated errors" in modular systems and lays a solid foundation for high-level intelligent driving.

2026 enters the "capability leap period": introducing multi-modal large models (E2E+VLM) on the basis of end-to-end enables the system to have initial scenario semantic understanding capabilities, integrating visual perception, voice commands, and map information to make decisions, instead of relying solely on sensor data. This upgrade directly makes up for key shortcomings in L3 commercialization, making it meet the mass production condition of "high reliability".

2027 and beyond move towards the "ultimate form period": evolve into full-link multi-modal end-to-end (VLA), and realize a "one-stop" closed loop from multi-modal input to driving action output. Models can simultaneously recognize traffic signs, understand user commands, analyze complex road conditions, and output human-like coherent decisions, achieving the core user value of "low takeover rate and high trust".

SAIC IM's evolution path confirms the industry consensus: the implementation of L3 is not only the stacking of computing power and sensors but also the iteration of architectural logic-from "execution automation" of a single modality to "cognitive intelligence" of multi-modal fusion, and VLA is the ultimate carrier of this process.

From the iteration of consumer demand to the strategic positioning at the supply side, from the differentiation of technology paths to the upgrading of hardware computing power, the eight major development trends of L3 intelligent driving Are essentially a panoramic microcosm of the industry's leap from "intelligence 1.0" to "Intelligence 2.0". It is no longer a breakthrough in a single technology, but a systematic project of multi-dimensional collaboration of "demand - technology - commerce - ecosystem". In the future, L3 will not only be a "core weapon" for OEMs to break through homogenization and achieve brand upgrading but also a "key bridge" connecting L2 popularization and L4 ecosystem. When production vehicle models are intensively launched during 2025-2027, and the regulatory dividends of highway scenarios extend to urban areas, L3 intelligent driving will no longer be a synonym for "high-end configuration", but a "tipping point" that redefine automobile value and opens up a new smart mobility ecosystem.

Table of Contents

1 Commercialization Progress and Policy Interpretation of L3 Conditional Autonomous Driving

• 1.1 Definition and Classification Standards of L3 Conditional Autonomous Driving
• International Classification Standards for L3 Intelligent Driving: SAE J3016 (1)
• International Classification Standards for L3 Intelligent Driving: SAE J3016 (2)
• China National Standard (GB/T 40429-2021): Definition of L3 Conditional Autonomous Driving, Requirements for L3 Systems, Interpretation of User Roles
• Differences between L3 Intelligent Driving and L2 Assisted Driving (1)
• Differences between L3 Intelligent Driving and L2 Assisted Driving (2)
• Differences between L3 Intelligent Driving and L2 Assisted Driving (3)
• Differences between L3 Intelligent Driving and L2 Assisted Driving (4): Responsibility Transfer Logic between "System Driving" and "Driver Takeover"
• Differences between L3 Intelligent Driving and L2 Assisted Driving (5): Driver Status Monitoring System 1
• Differences between L3 Intelligent Driving and L2 Assisted Driving (5): Driver Status Monitoring System 2
• Differences between L3 Intelligent Driving and L2 Assisted Driving (5): Driver Status Monitoring System
• Conceptual Design of L3 Autonomous Driving System Solution (1): Operational Design Domain (ODD)
• Conceptual Design of L3 Autonomous Driving System Solution (2): Core Functional Scenarios
• Full-Process System for Autonomous Driving Safety Assessment: Safety Assessment during Autonomous Driving Function Activation
• Full-Process System for Autonomous Driving Safety Assessment: Safety Assessment during Autonomous Driving Function Operation
• 1.2 Detailed Explanation of National Laws, Regulations and Policies on L3 Conditional Autonomous Driving
• Analysis of Laws, Regulations and Policies on L3 Conditional Autonomous Driving: Overview of National Laws and Policies (1)
• Analysis of Laws, Regulations and Policies on L3 Conditional Autonomous Driving: Overview of National Laws and Policies (2)
• Analysis of Laws, Regulations and Policies on L3 Conditional Autonomous Driving: Local Pilot Policies and Practices
• Policy Comparison among Wuhan, Beijing and Shenzhen: Liability Definition for L3 Traffic Accidents
• Policy Comparison among Wuhan, Beijing and Shenzhen: L3 Road Access Process
• "Work Plan for Stabilizing Growth in the Automobile Industry (2025-2026)": Clearly Stipulates "Conditional Approval of Production Access for L3 Models"
• First Batch of 9 Automakers Admitted to the Pilot Program for Access and Road Operation of L3 Intelligent Connected Vehicles
• First Batch of L3 Conditional Autonomous Driving Access Vehicle List
• China's Laws and Regulations on L3 Conditional Autonomous Driving: Interpretation of "Notice of the Four Ministries and Commissions on Carrying out the Pilot Program for Access and Road Operation of Intelligent Connected Vehicles" (1)
• China's Laws and Regulations on L3 Conditional Autonomous Driving: Interpretation of "Notice of the Four Ministries and Commissions on Carrying out the Pilot Program for Access and Road Operation of Intelligent Connected Vehicles" (2)
• China's Laws and Regulations on L3 Conditional Autonomous Driving: Interpretation of "Notice of the Four Ministries and Commissions on Carrying out the Pilot Program for Access and Road Operation of Intelligent Connected Vehicles" (3)
• China's Laws and Regulations on L3 Conditional Autonomous Driving: Interpretation of "Notice of the Four Ministries and Commissions on Carrying out the Pilot Program for Access and Road Operation of Intelligent Connected Vehicles" (4)
• Summary of Insurance-Related Laws, Regulations, Policies and Standards for L3 Conditional Autonomous Driving
• 1.3 National Standards for L3 Conditional Autonomous Driving
• Interpretation of General Technical Requirements for Autonomous Driving Systems of Intelligent Connected Vehicles (National Standard)
• Analysis of Safety Bottom-Line Process for ADS Dynamic Driving Task Backup in GB/T 44721-2024
• Human-Machine Interaction in GB/T 44721-2024: "Activation" Logic of ADS
• Human-Machine Interaction in GB/T 44721-2024: "Exit" and "Intervention" Logic of ADS
• Human-Machine Interaction in GB/T 44721-2024: Core Prompts for Different States of ADS
• Technical Specifications for Accident Definition and Data Collaboration of L3 Conditional Autonomous Driving Issued by CAAM
• Interpretation of GB 44497-2024 Standard (1): Autonomous Driving Data Recording System is an Indispensable Technical Basis for Accident Identification
• Interpretation of GB 44497-2024 Standard (2): Comprehensive Comparison between Type I and Type II Autonomous Driving Data Recording Systems
• Interpretation of GB 44497-2024 Standard (3): Full Link of "Event-Triggered" Data Recording
• Interpretation of GB 44497-2024 Standard (4): Full Link of "Real-Time Continuous Recording" Data Recording
• 1.4 Global Autonomous Driving Policies and Regulations
• Global Autonomous Driving Industry Has Witnessed Substantive Policy Promotion
• The Czech Republic Becomes the Second Country in Europe After Germany to Allow L3 Autonomous Driving on Public Roads
• Global Laws and Regulations on L3/L4 Autonomous Driving: Japan's "Road Traffic Act" Allows L4 Autonomous Driving Vehicles and Autonomous Driving Robots on the Road
• Global Laws and Regulations on L3/L4 Autonomous Driving: Measures for the Construction of Autonomous Driving Environment in Japan
• Global Laws and Regulations on L3/L4 Autonomous Driving: Development Goals of Autonomous Driving in Japan
• Global Laws and Regulations on L3/L4 Autonomous Driving: Japan's RoAD to the L4 Project
• 1.5 L3 Core Threshold for L3 Implementation: Design and Value of Autonomous Driving Redundancy Systems
• Failure Response Modes for Various Levels of Autonomous Driving
• Single-Channel Systems Have Serious Safety Hazards for L3 Autonomous Driving
• A Reasonably Designed Redundant Architecture Can Enhance the Overall Performance and Safety of L3 Systems
• L3 Autonomous Driving Shifts from Single-Channel Architecture to Multi-Channel Architecture
• Conceptual Design of L3 Autonomous Driving System Solution: Communication Redundancy
• Conceptual Design of L3 Autonomous Driving System Solution: Analysis of Power Supply Redundancy Solution (1)
• Conceptual Design of L3 Autonomous Driving System Solution: Analysis of Power Supply Redundancy Solution (2)
• Conceptual Design of L3 Autonomous Driving System Solution: Controller Redundancy
• Conceptual Design of L3 Autonomous Driving System Solution: Actuator Redundancy (1)
• Conceptual Design of L3 Autonomous Driving System Solution: Actuator Redundancy (2)
• L3 Intelligent Driving Redundancy

2 Benchmarking of Intelligent Driving Technology Routes for L3 Manufacturers and Industry Evolution Trends

• International Intelligent Driving Market and L3 Market Penetration Rate
• Penetration Rates of L2-L5 Autonomous Driving in China and Global Markets, 2025 - 2035E
• Driving Forces for L3 Conditional Autonomous Driving (1)
• Driving Forces for L3 Conditional Autonomous Driving (2)
• Consumers' Demand for Higher-Level Intelligent Driving Functions is Reshaping the Market Structure
• New Commercial Increment Brought by the Implementation of L3 Conditional Autonomous Driving (1)
• New Commercial Increment Brought by the Implementation of L3 Conditional Autonomous Driving (2)
• In 2025-2030, Strong Growth of L3 in China Market; the Revenue Potential of L3 in China Market is Expected to Reach 7 Billion USD by 2035
• Policy + Technology Dual-Driver: China's L3/L4 Autonomous Driving Market Enters the Fast Lane of Large-Scale Commercialization, 2025-2030E
• Synchronous Upgrades of Four Major Technical Modules Jointly Promote the Step-by-Step Implementation of Autonomous Driving from L3 to L5
• Predictions of Industry Experts on the Development of L3 Conditional Autonomous Driving (1)
• Predictions of Industry Experts on the Development of L3 Conditional Autonomous Driving (2)
• Development Rhythm of L3 and L4 Intelligent Driving
• Five Major Challenges for Large-Scale Implementation of L3
• Technical Challenges of L3 Implementation (1)
• Technical Challenges of L3 Implementation (2)
• Technical Challenges of L3 Implementation (3)
• Trend 1: Clear Window Period - Many Domestic OEMs Have Listed 2025-2027 as a Critical Phase for Mass Production and Installation of L3 Autonomous Driving; Hardware Pre-Embedding Has Become a Mainstream Strategy
• Four Major Characteristics of Domestic Automakers' Layout of L3
• Trend 2: Intensive Layout of L3 Autonomous Driving by International Camp is Triggering a Global Competition in Intelligence
• Trend 3: Sensor Manufacturers, Computing Power Platforms and Intelligent Driving Algorithm Suppliers Collaborate to Promote the Large-Scale Implementation of L3 and Evolution towards L4 Autonomous Driving
• Trend 4: Some Leading Automakers Adopt a Dual-Line Layout Strategy of L3 and L4, A Multiple Consideration at Technical, Capital and Strategic Levels (1)
• Trend 4: Some Leading Automakers Adopt a Dual-Line Layout Strategy of L3 and L4, A Multiple Consideration at Technical, Capital and Strategic Levels (2)
• Trend 5: L3 Technical Routes Present a "Three-Pillar" Pattern: Independent R&D, Dual-Track of Co-R&D + Independent R&D, External Suppliers (1)
• Trend 5: L3 Technical Routes Present a "Three-Pillar" Pattern: Independent R&D, Dual-Track of Co-R&D + Independent R&D, External Suppliers (2)
• Trend 5: L3 Technical Routes Present a "Three-Pillar" Pattern: Independent R&D, Dual-Track of Co-R&D + Independent R&D, External Suppliers (3)
• Trend 6: Multi-Channel Lidar Has Become an Important Choice for OEMs to Layout L3 Intelligent Driving and Ensure Safety Redundancy (1)
• Trend 6: Multi-Channel Lidar Has Become an Important Choice for OEMs to Layout L3 Intelligent Driving and Ensure Safety Redundancy (2)
• Trend 7: The Computing Power Demand for L3 Intelligent Driving Shows an Exponential Leap; 1000 TOPS Has Become a Mainstream Threshold (1)
• Trend 7: The Computing Power Demand for L3 Intelligent Driving Shows an Exponential Leap; 1000 TOPS Has Become a Mainstream Threshold (2)
• Trend 8: The Upgrade of Intelligent Driving Levels Drives the Step-by-Step Growth of Demand for "Computing Power - Data - Training Resources"
• Trend 9: "End-Cloud Collaboration" Has Become the Core Architecture for Most Automakers Layout L3 Intelligent Driving to Break Through the Constraints of Computing Power
• Trend 10: L3 Intelligent Driving Moves Towards End-to-End 2.0; Automakers Collectively Bet on the "VLA + End-Cloud Collaboration + World Model" Architecture (1)
• Trend 10: L3 Intelligent Driving Moves Towards End-to-End 2.0; Automakers Collectively Bet on the "VLA + End-Cloud Collaboration + World Model" Architecture (2)

3 L3 Intelligent Driving Products and Technologies of OEMs

• 3.1 Geely-ZEEKR
• Geely Automobile Accelerates L3/L4 Layout: Driven by Independent R&D and Strategic Ecological Cooperation
• At the Technical Evolution Level, L3 is the Core Key for ZEEKR's Next Breakthrough
• Four Pillars of L3 Breakthrough: Collaborative Closed Loop of Data, AI Large Model, Simulation Technology and Computing Power
• ZEEKR L3 Intelligent Driving: Hardware Layout
• ZEEKR L3 Intelligent Driving: End-to-End Large Model
• Differences in Development Concepts between L2, L3 and Above Autonomous Driving
• ZEEKR 9X Glory: The First Model of ZEEKR L3 Implementation
• 3.2 SAIC-IM Motors
• Full-Stack Layout Strategy for Autonomous Driving
• Development Plan of L3 Autonomous Driving Technology
• Technical Base for L2/L3/L4: Interpretation of End-to-End Large Model (1)
• Technical Base for L2/L3/L4: Interpretation of End-to-End Large Model (2)
• Technical Base for L2/L3/L4: Domain Controller and Sensor Hardware Configuration
• Technical Base for L2/L3/L4: Safety Redundancy
• Technical Base for L2/L3/L4: Digital Chassis (1)
• Technical Base for L2/L3/L4: Digital Chassis (2)
• Progress of Robotaxi Layout
• 3.3 XPeng Motors
• L3 Layout Plan
• Transformation Layout of "AI Defined Vehicle"
• Launches Intelligent Driving Vehicles with L3 Computing Power
• World Foundation Model (1)
• World Foundation Model (2)
• World Foundation Model (3)
• Cloud Factory
• G7 Ultra: The First AI Vehicle Equipped with L3 Computing Power Platform
• L4 Autonomous Driving Plan: Officially Launching Pre-Installed Robotaxi in 2026
• 3.4 Li Auto
• Accelerates L3/L4 Layout and Will Further Extend to AGI in the Future
• Understanding of L3 Conditional Autonomous Driving
• Evolution of Intelligent Driving Technology Route: VLA is Expected to Move Towards Higher-Level Autonomous Driving (1)
• Evolution of Intelligent Driving Technology Route: VLA is Expected to Move Towards Higher-Level Autonomous Driving (2)
• Core Technology of MindVLA
• 3.5 Huawei
• Implementation Plan of L3/L4 Intelligent Driving in China
• Solutions to Address Technical and Commercial Closed-Loop Challenges
• Qiankun Intelligent Driving Comprehensive Safety System CAS 4.0
• L3 Intelligent Driving: Analysis of Sensor Hardware Configuration (1)
• L3 Intelligent Driving: Analysis of Sensor Hardware Configuration (2): High-Precision Solid-State Lidar
• L3 Intelligent Driving: Analysis of Sensor Hardware Configuration (3): In-Cabin Laser Vision Limera
• ADS 4.0 (1): WEWA Architecture
• ADS 4.0 (2): WEWA Architecture
• ADS 4.0 (3): Comparison between L3 Intelligent Driving Version and Autonomous Driving Version
• ADS 4.0 (4): Comparison of Four Intelligent Driving Versions
• L3 Intelligent Driving: Self-Developed AOS+ Launches Hybrid Redundant Architecture to Build Autonomous Driving Safety Base
• Digital Chassis Engine XMC (1)
• Digital Chassis Engine XMC (2): Core Advantages
• L3 Intelligent Driving Mass-Produced Models: List of Huawei-Series Models Equipped with Huawei L3 Intelligent Driving Functions and Hardware Price Configuration
• 3.6 GAC Group
• Product Layout Plan of L3/L4 Autonomous Driving (1)
• Product Layout Plan of L3/L4 Autonomous Driving (2)
• Robotaxi Layout
• Construction of Computing Power Cluster
• Evolution History of ADIGO System: Launching L3 Intelligent Driving System ADGO GSD in 2025
• L3 Intelligent Driving Adopts a Dual-Track Strategy of "Independent R&D as the Mainstay + Cooperation as the Supplementary"
• Adopts Multi-Brand Hierarchical Layout to Accelerate the Popularization of Intelligence
• Technical Solution of L3 Intelligent Driving
• Design of L3 Intelligent Driving System: Global Safety Technology
• Design of L3 Intelligent Driving System: Dual Redundancy Design of Eight Key Systems
• Design of L3 Intelligent Driving System: Active-Passive Integrated Safety (1)
• Design of L3 Intelligent Driving System: Active-Passive Integrated Safety (2)
• Design of L3 Intelligent Driving System: Battery Safety
• Design of L3 Intelligent Driving System: Intelligent Chassis Safety
• GAC Group Has Obtained Approval for the Pilot Program for Access and Road Operation of Intelligent Connected Vehicles, Becoming One of the First Batch of Automakers in China Approved to Carry Out L3 Autonomous Driving Road Operation Pilots
• 3.7 Voyah
• Layout and Important Nodes of L3 Intelligent Driving
• Technical Architecture of L3 Intelligent Driving: Tianyuan
• Qingyun L3 Architecture
• L3 Intelligent Driving System: Hardware Configuration
• Analysis of Kunpeng L3 Intelligent Safety Driving System (1)
• Analysis of Kunpeng L3 Intelligent Safety Driving System (2)
• 3.8 Changan Automobile
• L3 Plan in Dubhe Plan 2.0
• Leads the L3 Intelligent Driving Access List
• Intelligent Driving Solution for L3 Models
• L3 Road Test of Deepal in Chongqing
• Layout of L4 Robotaxi
• 3.9 BMW
• L3 Layout
• Future Mobility Development Center
• Personal Pilot L3
• Personal Pilot L3 ODD and Map Drawing
• Redundancy Design of Personal Pilot L3
• L3 Multi-Modal Sensor Suite
• New-Generation EE Architecture
• Core of New-Generation EE Architecture: Four High-Performance Computers "Super Brain"
• Other Designs of New EE Architecture
• 3.10 Mercedes-Benz
• Committed to the R&D and Upgrade of L3 Autonomous Driving Technology
• Introduction to DRIVE PILOT
• Defined Boundaries of DRIVE PILOT: Operational Design Domain (ODD)
• Sensor Configuration of DRIVE PILOT
• Redundancy Design of DRIVE PILOT
• Other L3 Designs
• Formed a Multi-Line Intelligent Driving Path of L2, L3 and L4
• 3.11 Audi
• Defined Boundaries of 3: Operational Design Domain (ODD)
• Core Computing Architecture of L3: zFAS Computer Platform
• Detailed Configuration Interpretation of zFAS
• Overview of L3 Sensor Configuration
• Analysis of L3 Sensor Configuration
• L3 Chassis and Actuator Redundancy
• Other L3 Designs
• 3.12 Stellantis
• STLA AutoDrive 1.0
• Technologies of STLA AutoDrive L3
• Basic Architecture: STLA Brain
• Collaborative Layout
• 3.13 Honda
• SENSING Elite
• Defined Boundaries of L3: Operational Design Domain (ODD)
• Configuration of L3 Autonomous Driving System and Collaboration Logic Between Modules (1)
• Configuration of L3 Autonomous Driving System and Collaboration Logic Between Modules (2)
• Redundancy Design and Core Decision of L3
• Overview of L3 Sensor Configuration
• Overview of L3 Algorithm and Model Configuration
• L3 Future Strategy
• 3.14 Rivian
• Layout of L3 Autonomous Driving Platform
• L3 Zonal E/E Architecture
• L3 Sensor Configuration
• L3 Computing Platform Configuration and Chassis Control
• Software Algorithm Design of L3: Perception and Prediction
• Software Algorithm Design of L3: Planning
• Other L3 Designs
• 3.15 Tesla
• Transformation of Future Strategy
• Parameters of AI5 Chip
• Paradigm Shift from Dual-Chip Redundancy to Single-Chip Integration
• Key Technologies of AI5
• Development Direction of FSD Model Training Process
• 3.16 Other Foreign Automakers
• Overview of L3 Layout Plans for Other Automakers

4 L3 Intelligent Driving Products and Technologies of Tier 1 Suppliers

• 4.1 NVIDIA
• Full-Stack L3 Autonomous Driving System Alpamayo
• Technical Evolution Route of Alpamayo
• Basic Architecture of Alpamayo (1)
• Basic Architecture of Alpamayo (2)
• Network Architecture of Alpamayo
• Alpamayo Model Training Process Table
• Alpamayo Model Training Process
• Alpamayo L3 Architecture
• Redundancy Design of Alpamayo L3 Architecture
• Halos Overall Safety System
• 4.2 Horizon Robotics
• Implementation Path of L3
• Judgment on the Large-Scale Development of Various Levels of Intelligent Driving
• Computing Power Demand and Products of L3
• Mass Production Solution of L3
• 4.3 Qualcomm
• Planning of Snapdragon Ride Platform
• Flagship Cockpit-Driving Integration Chip Platform 8797
• Parameter Configuration of Snapdragon 8797 Chip
• 4.4 Black Sesame Technologies
• Introduction to Huashan A2000 Chip
• Parameter Configuration of Huashan A2000 Chip
• Architecture of Huashan A2000 Chip
• Memory Architecture of Huashan A2000
• Universal AI Toolchain BaRT for Huashan A2000
• Future-Oriented Design of Huashan A2000
• Redundancy Design of Safety Base
• 4.5 Hesai Technology
• L3 Solution - Infinity Eye B
• Parameters of Infinity Eye B ETX Long-Range Lidar
• Innovative Technology of ETX Long-Range Lidar
• Parameters of Infinity Eye B Second-Generation Solid-State Lidar FTX
• The Fourth-Generation Chip
• 4.6 RoboSense
• L3 Solution - EM4+E1
• Parameter Configuration of Ultra-Long-Range Lidar EM4
• Core Technical Features of EM4 Lidar for L3
• Parameter Configuration of Blind-Spot Radar E1
• Receiving and Processing SOC Chip
• Application Cases of L3 Solution
• 4.7 Bosch
• L3 Development Plan
• Cooperates with CARIAD to Develop AI-Based L2/L3 Autonomous Driving Software Stack
• Mainstream Path of High-Level Assisted Driving: One-Model End-to-End (1)
• Mainstream Path of High-Level Assisted Driving: One-Model End-to-End (2)
• 4.8 Mobileye
• Profile and Product Portfolio
• Redefines Autonomous Driving into Four Levels from the Perspective of Consumer-Oriented Product
• L3 Autonomous Driving Chips Will Achieve Mass Production in 2025, Promoting the Large-Scale Implementation of High-Level Intelligent Driving Technology
• Comparison of EyeQ6H and EyeQ5H (1)
• Comparison of EyeQ6H and EyeQ5H (2)
• Full-Stack Autonomous Driving Product Portfolio Matrix for L1-L4 (1)
• Full-Stack Autonomous Driving Product Portfolio Matrix for L1-L4 (2)
• Sensor Hardware Configuration of L3 Intelligent Driving Products (Scenarios Such as Highways/Cities/Countryside)
• Comparison of Mass-Produced Customers, Mass-Produced Models and Target Markets of L2/L3 Intelligent Driving Products
• Algorithm Architecture of Chauffeur
• Product Evolution Route Plan: It is Expected That CH (L3) Products Will Account for 10% of the Company's Total Revenue in the Second Phase
• Cooperates with Volkswagen to Launch Its First L4 Autonomous Vehicle (1)
• Cooperates with Volkswagen to Launch Its First L4 Autonomous Vehicle (2)
• 4.9 Zhuoyu Technology
• Time Planning for the Implementation of L3 Intelligent Driving
• VLA Large Model and L3/L4 Intelligent Driving Plan
• Evolution Trend of Intelligent Driving Products
• Layout of L3 In-vehicle Hardware Products: Inertial Navigation Three-Camera and Lidar Assembly
• Layout of L3 In-vehicle Hardware Products: Intelligent Driving Domain Controller