语言或许不是自驾的「终极答案」，但它无疑是当下最可行的路径...

Core Insights - The article emphasizes that the current development of autonomous driving systems relies heavily on data-driven models, but the next breakthrough must focus on enhancing reasoning capabilities [2][4][7]. Group 1: Current State of Autonomous Driving - The industry predominantly uses a classic data flywheel model for production models, which includes deployment, effect verification, data mining, retraining, and redeployment [4]. - As data scales have increased to tens of millions, the performance gains from merely increasing data size have diminished, leading to higher costs and more complex challenges [4][7]. - Companies like Tesla, Li Auto, Xiaomi, and Xpeng have recognized this shift and are adapting their strategies accordingly [4]. Group 2: Insights from Other Fields - The article draws parallels between autonomous driving and robotics, noting that while autonomous driving has benefited from abundant data, robotics has faced data scarcity [7]. - Recent advancements in robotics, such as large datasets and new algorithms, have emerged from this scarcity, potentially paving the way for more robust capabilities in embodied intelligence [7]. Group 3: Future Directions in Autonomous Driving - The article identifies a critical flaw in current autonomous driving systems: the lack of deep reasoning capabilities [7]. - To transition to the next phase of autonomous driving (referred to as Autonomous Driving 3.0), four pillars are necessary: reasoning ability, common-sense cognition, long-term memory, and explanation and interaction [7][9]. - NVIDIA's upcoming Alpamayo-R1 model aims to integrate explicit causal reasoning with trajectory planning within a unified VLA architecture, highlighting a shift towards reasoning-driven approaches [7]. Group 4: Community and Learning Resources - The article promotes a community platform for knowledge sharing in autonomous driving, which includes resources for beginners and advanced learners, as well as opportunities for networking with industry experts [13][28]. - The community has compiled extensive resources, including over 40 technical directions and nearly 60 datasets related to autonomous driving, facilitating easier access to information for both newcomers and experienced professionals [28][50].