Xin Hua Cai Jing·2025-04-30 08:02Core Viewpoint - Li Auto's decision to open-source its self-developed automotive operating system, "Xinghuan OS," aims to address industry-wide challenges and promote collaboration within the automotive sector [2][3]. Group 1: Initial Motivation and Industry Impact - The initial motivation for developing the operating system was to solve internal issues related to chip shortages and operational reliability, which were also common industry problems [3]. - By open-sourcing the operating system, the company aims to prevent resource wastage from redundant development efforts across the industry, fostering a collaborative environment for automotive innovation [3]. Group 2: Interest from Other Manufacturers - There is significant interest from two main types of manufacturers: suppliers (such as chip and solution providers) who see value in leveraging validated technologies to reduce R&D costs, and automotive manufacturers who are currently observing the open-source initiative [4]. Group 3: Responsibility and Risk Management - In the event of issues arising from the open-source system, responsibility is expected to lie primarily with the users and service providers who derive commercial benefits, while contributors focus on industry advancement [5]. - The company plans to establish a systematic process to review external contributions, ensuring project health and quality through automated and manual testing [7]. Group 4: Open-Source Development Timeline - The Xinghuan OS project was initiated in 2021, with the first deployment expected in 2024. The open-source announcement was made in March 2023, with the first batch of code released in April 2023 and further updates planned for June 2023 [6]. Group 5: Benefits and Strategic Goals - The open-source initiative is not aimed at direct financial returns but rather at fostering a collaborative ecosystem where all participants can contribute and innovate together, ultimately striving for the vision of an Intelligent OS [7]. - The operating system is positioned as a critical factor for future competitiveness in intelligent driving, addressing complexities in system performance, hardware iteration cycles, and AI computing needs [8].