特斯拉世界模拟器来了！ 这些看似真实的驾驶场景，全都是用模拟器生成： 这个模拟器在今年的计算机视觉顶会ICCV上亮相，由特斯拉自动驾驶副总裁Ashok Elluswamy亲自讲解。 网友看了之后表示，这个模型实在是泰裤辣。 同时，Elluswamy也首次揭秘了特斯拉的自动驾驶技术路线图，表示端到端才是智能驾驶的未来。 世界模拟器生成自动驾驶场景 除了开头看到的多场景驾驶视频，特斯拉的世界模拟器还可以为自动驾驶任务生成新的挑战场景。 比如右侧的车辆突然连并两条线，闯入预设的驾驶路径。 也可以让AI在已有的场景中执行自动驾驶任务，躲避行人和障碍物。 模型生成的场景视频，除了让自动驾驶模型在里面练手，也可以当成电子游戏，供人类玩耍体验。 当然除了驾驶相关，对其他具身智能场景——比如特斯拉的擎天柱机器人——也同样有用。 与这个模型一同被揭秘的，还有特斯拉在自动驾驶上的一整套方法论。 特斯拉VP：端到端才是自动驾驶的未来 ICCV演讲中，特斯拉自动驾驶副总裁Ashok Elluswamy揭秘了特斯拉FSD的技术细节，同时还在X上发表了文字版本。 Ashok首先明确，端到端AI才是自动驾驶的未来。 端到端方法可轻松扩展以 ...

Core Viewpoint - Tesla has unveiled a world simulator for autonomous driving, showcasing its potential to generate realistic driving scenarios and enhance the training of AI models for self-driving technology [1][4][12]. Group 1: World Simulator Features - The simulator can create new challenging scenarios for autonomous driving tasks, such as unexpected lane changes by other vehicles [4][5]. - It allows AI to perform driving tasks in existing scenarios, avoiding pedestrians and obstacles [7][9]. - The generated scenario videos can also serve as a gaming experience for human users [9]. Group 2: End-to-End AI Approach - Tesla's VP Ashok Elluswamy emphasized that end-to-end AI is the future of autonomous driving, applicable not only to driving but also to other intelligent scenarios like the Tesla Optimus robot [12][13][14]. - The end-to-end neural network utilizes data from various sensors to generate control commands for the vehicle, contrasting with modular systems that are easier to develop initially but less effective in the long run [17]. - The end-to-end approach allows for better optimization and handling of complex driving situations, such as navigating around obstacles [18][21]. Group 3: Challenges and Solutions - One major challenge for end-to-end autonomous driving is evaluation, which Tesla addresses with its world simulator that trains on a vast dataset [22][24]. - The simulator can also facilitate large-scale reinforcement learning, potentially surpassing human performance [24]. - Other challenges include the "curse of dimensionality," interpretability, and safety guarantees, which require processing vast amounts of data [26][27][28]. Group 4: Data Utilization - Tesla collects data equivalent to 500 years of driving every day, using a complex data engine to filter high-quality samples for training [29][30]. - This extensive data collection enhances the model's generalization capabilities to handle extreme situations [30]. Group 5: Technical Approaches in the Industry - The industry is divided between two main approaches: VLA (Vision-Language Architecture) and world models, with companies like Huawei and NIO representing the latter [38][39]. - VLA proponents argue it leverages existing internet data for better understanding, while world model advocates believe it addresses the core issues of autonomous driving [41][42]. - Tesla's approach is closely watched due to its historical success in selecting effective strategies in autonomous driving development [43][44].