Core Viewpoint - Tesla is shifting to a single, large end-to-end neural network that directly generates control actions from pixel and sensor data, eliminating explicit perception modules [1][34]. Group 1: Reasons for Transition to End-to-End Neural Networks - Integrating human values (like driving smoothness and risk assessment) into code is extremely challenging [3]. - Poor interface definitions between traditional perception, prediction, and planning can lead to information loss [4]. - The end-to-end approach is easier to scale for handling long-tail problems in the real world [5]. - It allows for homogeneous computation with deterministic latency, which is crucial for real-time systems [6]. Group 2: Challenges in Learning "Pixel to Control" - The primary challenges include the curse of dimensionality, interpretability and safety guarantees, and evaluation [7][8][9]. - The input context can be extensive, with a 30-second window potentially reaching 2 billion tokens [10][49]. - Tesla leverages its vast fleet data to extract valuable corner case data through complex, trigger-based data collection methods [11][51][56]. Group 3: Solutions to Challenges - For the curse of dimensionality, Tesla refines its extensive driving data to ensure the right correlations are captured [51][56]. - Interpretability is addressed by prompting the end-to-end model to predict various auxiliary outputs for debugging and safety assurance [12][60]. - Evaluation challenges are tackled by creating a neural network-based world simulator that can generate consistent video streams from multiple cameras [19][79]. Group 4: Future Developments - The next step involves the Cyber Cab, a next-generation vehicle designed specifically for robotaxi services, utilizing the same neural network technology [25][83]. - The technology developed for autonomous driving is also being adapted for humanoid robots, such as Optimus [26][86].

【文/观察者网 熊超然】在本月发表于《航空学报》的一篇同行评议论文中，中国空气动力研究与发展 中心研究员黄江涛领导的团队研发出一项革命性的软件设计方案，他们表示，这将帮助克服隐形飞机研 发过程中面临的一大障碍。 这一新型平台使飞机设计师能够自由增加设计变量，却无需提升计算负荷，这一突破长期被航空界认为 是"不可能实现的技术壮举"。中方研究团队的这一创新，被视为破解了所谓的"维度魔咒"，他们以美国 海军X47B试验型隐身无人机为例，说明了该系统的运作原理。 "采用带进气道的X47B外形开展基于表面灵敏度的大规模设计变量气动隐身优化，优化外形相对于初始 外形阻力、RCS大幅度下降，总压恢复系数少量提升，优化后进气道弯度增加，RCS减缩效果显著且保 持了初始进气道的良好的总压恢复和总压畸变性能。" 黄江涛团队表示，优化结果表明，他们所建立的基于阻抗边界条件表面灵敏度的气动隐身优化方法能够 实现考虑吸波材料涂覆的大规模设计变量气动隐身优化，为低可探测飞行器设计提供技术支撑。 2012年12月10日，美国海军发布拍摄照片，在大西洋一个未公开的位置上，X-47B无人空战系统验证机 在"杜鲁门"号（CVN 75）航空母舰的 ...