Core Viewpoint - The future of autonomous driving technology will focus on safer driving, better user experience, and comprehensive scenario coverage, necessitating a robust operational model from both manufacturers and suppliers [1]. Group 1: Data-Driven Technology - Future autonomous driving companies are expected to resemble "data-driven technology companies," where competition will shift from algorithms to the efficiency of data loops [2]. - The ability to quickly collect, clean, label, train, and validate data will be crucial for gaining a competitive edge, requiring advanced automation tools and AI-driven data pipelines [2]. - The architecture involving VLA/VLM will be essential for enhancing user experience, with a focus on building robust, efficient, and low-cost closed-loop simulations [2]. Group 2: Algorithm and Data Services - When considering algorithms, the supporting data services and automated labeling infrastructure must also be taken into account, especially for companies under profit pressure [3]. - The industry is exploring solutions like DiffVLA to transition smoothly into the VLA era while leveraging existing data and tools [3]. - Current research focuses on introducing new data sources and learning paradigms, indicating that the field remains open for exploration and innovation [3]. Group 3: Simulation and Training - There is a consensus in academia and industry on the importance of closed-loop systems involving agent simulators, sensor simulators, and driving policies [4]. - Companies that can effectively address the sim-to-real domain gap and build efficient closed-loop training systems will likely lead the autonomous driving market [4]. - Without a data loop, end-to-end/VLA systems are considered incomplete [5]. Group 4: Community and Knowledge Sharing - The "Autonomous Driving Knowledge Planet" community aims to provide a platform for technical exchange and problem-solving among members from leading universities and companies in the autonomous driving sector [12]. - The community has compiled extensive resources, including over 40 technical routes and numerous datasets, to facilitate learning and application in projects [12]. - Regular discussions with industry leaders on trends and challenges in autonomous driving are part of the community's offerings [12].