智能驾驶行业：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 "second half," where high-level autonomous driving will gradually achieve large-scale production, making data processing and efficient mining essential challenges for companies. The data closed-loop is a key factor driving the continued development of intelligent driving [2] - The new ecosystem of data closed-loop in the intelligent driving field has significant advantages, including automated data processing, continuous optimization of data management, and the ability to address data fragmentation issues, thereby empowering high-level autonomous driving [2] - The technology of the new ecosystem data closed-loop encompasses four stages: data collection, processing, analysis, and management, utilizing sensor technology, cloud computing, edge computing, and machine learning [2] Summary by Sections 1.1 Intelligent Driving Technology Upgrade - The new ecosystem data closed-loop represents a shift from traditional methods, which were inefficient and heavily reliant on manual processes, to a more automated and scalable approach [10][12] - Traditional data closed-loop systems faced high costs and low efficiency, while the new ecosystem aims to reduce costs and enhance efficiency through automation [12] 1.2 Data Collection and Processing - Data collection is crucial for ensuring the quality of the data closed-loop, with various sensor technologies playing a key role [15] - Cloud computing and edge computing provide the necessary technical support for the development of intelligent driving data closed-loop [18] 1.3 Opportunities and Challenges - The intelligent driving data closed-loop faces both opportunities and challenges, including supportive government policies and technological advancements in AI [2][32] - The rapid growth of data volume necessitates effective compliance measures to address privacy and security concerns [90] 2.1 Industry Players - The report identifies four main types of players in the intelligent driving data closed-loop ecosystem: OEMs, Tier 1 suppliers, data service providers, and chip manufacturers, each with distinct advantages [2][34] 2.2 AI and Automation - AI technologies enable efficient data annotation and processing, enhancing the capabilities of intelligent driving systems [45][60] - Cloud simulation and digital twin technologies provide realistic testing environments for autonomous driving systems, improving safety and efficiency [52][55] 3.1 Market Expansion - The report highlights the growing international market for intelligent driving, particularly in North America and Europe, with significant growth potential for ADAS [84][87] - The rapid development of the data closed-loop in China is expected to support domestic companies in expanding their operations overseas [84] 3.2 Compliance Issues - Data compliance remains a critical challenge for the intelligent driving industry, impacting user privacy and national security [90]