Dublin, Jan. 19, 2026 (GLOBE NEWSWIRE) -- The "China Autonomous Driving Data Closed Loop Research Report, 2025" report has been added to ResearchAndMarkets.com's offering.
Data Closed-Loop Research: Synthetic Data Accounts for Over 50%, Full-process Automated Toolchain Gradually Implemented
Key Points:
- From 2023 to 2025, the proportion of synthetic data increased from 20%-30% to 50%-60%, becoming a core resource to fill long-tail scenarios.
- Full-process automated toolchain from collection to deployment is gradually implemented, helping reduce costs and improve efficiency.
- Efficient collaboration of the vehicle-cloud integrated data closed-loop is a key factor in achieving faster iterations.
The essence of autonomous driving data closed-loop is a cyclic optimization system of "collection-transmission-processing-training-deployment". In 2025, the industry is accelerating from the "0?1" stage to the "high-quality and high-efficiency" era, with core contradictions focusing on long-tail scenario coverage and cost control. OEMs and Tier 1 suppliers are actively establishing their own data closed-loop solutions. Through efficient data collection, processing and analysis processes, they continuously improve autonomous driving algorithms, thereby significantly enhancing the accuracy and stability of intelligent driving systems.
From 2023 to 2025, the Proportion of Synthetic Data Increased from 20%-30% to Over 50%
The efficiency of acquiring high-quality data determines the evolution speed of intelligent driving. Currently, data sources in the automotive field include mass-produced vehicle-triggered data transmission, high-value specific scenario data collection by collection vehicles, engineering practices for physical world restoration through roadside real data, and data synthesis technology based on world models. The core path for the large-scale application of autonomous driving technology, real data anchors basic capabilities, and synthetic data breaks through capability boundaries. From 2023 to 2025, the proportion of real data and synthetic data in autonomous driving training data has undergone significant changes, gradually shifting from a real data-dominated model in the early stage to a hybrid model with an increasing proportion of synthetic data.
Full-process Automated Toolchain from Collection to Deployment is Gradually Implemented, Helping Reduce Costs and Improve Efficiency
The autonomous driving data closed-loop has shifted from focusing on a single link (such as improving annotation efficiency) in the early stage to an end-to-end automated architecture covering "collection-annotation-training-simulation-deployment". The core breakthrough is to break through data flow barriers through AI large models and cloud-edge collaboration technology, realizing closed-loop self-evolution.
Currently, MindFlow empowers customers including SAIC Group, Changan Automobile, Great Wall Motors, Geely Automobile, FAW Group, Li Auto, Huawei, Bosch, ECARX, MAXIEYE, NavInfo and RoboSense.
Efficient Collaboration of the Vehicle-Cloud Integrated Data Closed-Loop is a Key Factor in Achieving Faster Iterations
The essence of the vehicle-cloud integrated data closed-loop is to build a collaborative system of "vehicle-side lightweight + cloud-side intelligence", break through data flow barriers, and realize the continuous evolution of intelligent vehicles. The vehicle side is responsible for real-time collection of environmental perception data (such as road conditions, vehicle operation data), which is uploaded to the cloud after desensitization, encryption, and compression. The cloud processes massive amounts of data (PB/EB level), performs annotation, model training, and algorithm optimization, generates new capabilities, and issues them to the vehicle side to realize OTA upgrades.
The ExceedData data closed-loop solution is a vehicle-cloud integrated solution, which has gained the trust and mass production application of more than 15 automotive OEMs and is deployed in more than 30 mainstream models.
The composition of the ExceedData data closed-loop solution includes the vehicle-side edge computing engine (vCompute), edge data engine (vADS), edge database (vData), as well as the cloud-side algorithm development tool (vStudio), cloud computing engine (vAnalyze), and cloud management platform (vCloud). This solution can reduce data transmission costs by 75%, cloud storage costs by 90%, and cloud computing costs by 33%. According to the calculation of an OEM case cooperating with ExceedData: the total cost optimization can be reduced by 85%.
In terms of OEMs, take Xpeng Motors as an example. Its self-built "cloud-side model factory" has a computing power reserve of 10 EFLOPS in 2025, and the end-to-end iteration cycle is shortened to an average of 5 days, supporting rapid closed-loop from cloud-side pre-training to vehicle-side model deployment.
Xpeng launched China's first 72 billion parameter multimodal world base model for L4 high autonomous driving, which has chain-of-thought (CoT) reasoning capabilities and can simulate human common-sense reasoning and generate control signals. Through model distillation technology, the capabilities of the base model are migrated to the vehicle-side small model, realizing personalized deployment of "small size and high intelligence".
High-value data (such as corner cases) is initially screened through the vehicle-side rule engine. The cloud combines synthetic data generation technologies (such as GAN, diffusion models) to fill data gaps and improve model generalization capabilities. At the same time, end-to-end (E2E) and VLA models integrate multimodal inputs to directly output control commands, relying on cloud-side large model training (such as Xpeng's 72 billion parameter base model) to achieve lightweight deployment on the vehicle side.
With the comprehensive modeling of the entire intelligent driving system, car companies are pursuing "better cost, higher efficiency, and more stable services" in the data closed-loop. The delivery method of intelligent driving is accelerating from delivering code for single-vehicle deployment to a subscription-based cloud service as the core. The efficiently collaborative data closed-loop of vehicle-cloud integration is the key for intelligent vehicles to achieve faster iterations driven by AI.
Key Topics Covered:
1 Overview/Trends of Autonomous Driving Data Closed-Loop
1.1 Overview of Data Closed-Loop
1.2 Data Closed-Loop Moves Towards the Era of Full-Stack Self-Evolution
1.3 Summary of Data Closed-Loop Progress Cases
1.4 Data Closed-Loop Cooperation Models
1.5 Summary of OEMs' Data Closed-Loop Related Cooperation
2 Research on High-Quality Data Collection/Synthetic Simulation
2.1 High-Quality Data Collection
2.2 Synthetic/Simulation Data
3 Research on Data Storage/Processing
3.1 Data Storage
3.2 Efficient Data Processing
4 Research on Automated (AI) Annotation
4.1 Rere Data
4.2 MindFlow
4.3 StardustAI
4.4 Datatang
4.5 Databaker Technology
4.6 Boden AI
4.7 ByteTree AI
5 Research on Algorithms and Model Training
6 Research on Representative Suppliers of Data Closed-Loop Technology
6.1 WUWEN.AI
6.2 LiangDao Intelligence
6.3 ExceedData
6.4 Freetech
6.5 MAXIEYE
6.6 Ruqi Mobility
6.7 Yoocar
6.8 Roadgrids
6.9 NavInfo
6.10 Kotei Informatics
7 Research on Typical OEMs' Data Closed-Loop7.1 XPeng Motor
7.2 Xiaomi Auto
7.3 NIO
7.4 Li Auto
7.5 Leapmotor
7.6 IM Motors
7.7 Tesla
7.8 BYD
7.9 Geely Automobile
7.10 FAW Group
7.11 GAC
7.12 Summary of Changan Automobile Data Closed-Loop and Software Supply Chain
7.13 Dongfeng Motor's "One Core, Two Bases, Two Elements" System
7.14 Summary of Dongfeng Nissan Data Closed-Loop and Software Supply Chain
7.14 Dongfeng Nissan Autonomous Driving Software Solutions and Supply Chain Construction
7.15 Summary of Volkswagen Data Closed-Loop and Software Supply Chain
7.16 Summary of Toyota Data Closed-Loop and Software Supply Chain
For more information about this report visit https://www.researchandmarkets.com/r/3802hx
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