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].

Core Viewpoint - The article discusses the current state and future directions of autonomous driving technology, highlighting the maturity of certain technologies, the challenges that remain, and the emerging trends in the industry. Group 1: Current Technology Maturity - The introduction of BEV (Bird's Eye View) and OCC (Occupancy) perception methods has matured, with no major players claiming that BEV is unusable [2][13] - The main challenge remains corner cases, where 99% of scenarios are manageable, but complex situations like rural roads and large intersections still pose difficulties [13] - E2E (End-to-End) models have not yet demonstrated clear advantages over two-stage models in practical applications, despite their theoretical appeal [4][5] Group 2: Emerging Technologies - VLA (Vision-Language Alignment) is gaining attention as it simplifies tasks and potentially addresses corner cases more effectively than traditional methods [5][6] - The efficiency of models is a critical issue, with discussions around using smaller models to achieve performance close to larger ones [6][30] - Reinforcement learning has not yet proven to be significantly impactful in autonomous driving, with a need for better simulation environments to validate its effectiveness [7][51] Group 3: Future Directions - There is a consensus that VLA and VLM (Vision-Language Model) will be key areas for future development, focusing on enhancing reasoning capabilities and safety [45][48] - The industry is moving towards a more data-driven approach, where the efficiency of data collection, cleaning, and training will determine competitive advantage [28][40] - The integration of world models and closed-loop simulations is seen as essential for advancing autonomous driving technologies [47][50] Group 4: Industry Perspectives - The shift towards VLA/VLM is viewed as a necessary evolution, with the potential to improve user experience and safety in autonomous vehicles [28][45] - The debate between deepening expertise in autonomous driving versus transitioning to embodied intelligence reflects the industry's evolving landscape and personal career choices [22][27] - The current focus on safety and robustness in L4 (Level 4) autonomous driving indicates a divergence in technical approaches between L2+ and L4 players [25][36]