2024中国智能驾驶数据闭环应用新生态分析报告

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The development of intelligent driving has entered a new phase where high-level autonomous driving will gradually achieve large-scale production, with data closure becoming a key factor for continued development [2][22] - The new ecosystem of data closure in intelligent driving offers significant advantages, including automated data processing, continuous optimization, and the ability to address corner case scenarios effectively [22][29] - The report identifies four main players in the intelligent driving data closure ecosystem: OEMs, Tier 1 suppliers, data service providers, and chip companies, each with distinct roles and advantages [22][63] Summary by Sections 1.1 Overview of the New Data Closure Ecosystem - The new data closure ecosystem in intelligent driving integrates advanced technologies such as AI large models and cloud simulation to enhance data processing capabilities and efficiency [14][16] - The evolution of intelligent driving technology has led to increased demand for data, necessitating higher requirements for data closure systems [14][15] 1.2 Technical Breakdown of the New Data Closure Ecosystem - The new ecosystem encompasses four key stages: data collection, processing, analysis, and management, utilizing sensors, cloud computing, edge computing, and machine learning [22][31] - Cloud computing provides powerful processing capabilities and storage, while edge computing reduces latency and enhances data privacy [35][37] 1.3 Driving Factors - The report highlights the growing importance of data compliance and government policies supporting the development of intelligent driving data closure systems [42][43] - AI technology and cloud simulation are rapidly advancing, facilitating the transition to a new ecosystem of data closure [45][94] 2.1 New Industry Landscape - The intelligent driving data closure industry is evolving with a focus on data collection, processing, storage, and simulation, involving various stakeholders [60][63] - OEMs are increasingly prioritizing the development of data closure capabilities to enhance intelligent driving functionalities [60][63] 2.2 New Technology Applications - AI automatic labeling significantly improves the efficiency and quality of data processing, reducing costs and enhancing collaboration between humans and machines [74][76] - Cloud simulation and digital twin technologies provide realistic testing scenarios, essential for the development of high-level autonomous driving systems [86][89] 2.3 New Scenario Applications - The report discusses the need for diverse and complex testing scenarios to meet the demands of high-level autonomous driving, emphasizing the role of cloud simulation [86][89] 2.4 New Market Changes - The intelligent driving market is witnessing rapid growth, with increasing data volumes and the need for effective data management solutions [22][48]