The "China Autonomous Driving Data Closed Loop Research Report, 2025" by ResearchAndMarkets.com highlights the critical role of vehicle-cloud integrated data closed-loop systems in accelerating the development of autonomous driving technologies. As the industry transitions from initial deployment to a phase focused on high-quality, high-efficiency performance, the ability to rapidly iterate and improve intelligent driving systems hinges on efficient data management and processing. A central theme of the report is the growing reliance on synthetic data, which now accounts for over 50% of training data in 2025—up from 20% to 30% in 2023. While real-world data remains foundational for establishing baseline capabilities, synthetic data is instrumental in overcoming challenges related to long-tail scenarios—rare but critical driving situations that are difficult to capture through physical testing alone. By leveraging advanced techniques such as GANs and diffusion models, synthetic data helps fill coverage gaps, enhances model generalization, and reduces dependency on costly real-world data collection. The report emphasizes the shift toward full-process automated toolchains that span the entire data lifecycle—from vehicle-side data collection and edge processing to cloud-based annotation, model training, simulation, and over-the-air (OTA) deployment. This end-to-end automation, powered by AI large models and cloud-edge collaboration, enables self-evolving systems that continuously improve performance. Companies like ExceedData have developed integrated solutions that significantly reduce infrastructure costs. Their vehicle-cloud architecture includes edge computing engines, data storage units, and cloud-based development platforms. According to an OEM case study, this approach can cut data transmission costs by 75%, cloud storage by 90%, and cloud computing by 33%, leading to an overall cost reduction of up to 85%. Xpeng Motors serves as a leading example, with its self-built cloud-side model factory boasting 10 EFLOPS of computing power by 2025. This infrastructure enables an average end-to-end iteration cycle of just five days. Xpeng also launched China’s first 72-billion-parameter multimodal world model for L4 autonomy, featuring chain-of-thought reasoning to simulate human-like decision-making. Using model distillation, these advanced capabilities are adapted for deployment on lightweight vehicle-side models, enabling personalized, high-intelligence performance. The report also examines the collaboration models between OEMs and technology suppliers. Leading players such as SAIC Group, Changan Automobile, Geely, FAW Group, Li Auto, Huawei, Bosch, and others are adopting solutions from providers like ExceedData, MindFlow, MAXIEYE, and Ruqi Mobility. These partnerships are helping automakers build scalable, efficient data pipelines that support rapid innovation. As the intelligent driving landscape evolves, the delivery model is shifting from one-time software updates to subscription-based cloud services. This transition underscores the importance of a seamless, collaborative vehicle-cloud data closed-loop as the backbone of continuous improvement. With the integration of multimodal inputs and end-to-end AI models, the future of autonomous driving lies in systems that are not only smarter but also more efficient, cost-effective, and adaptable.