Core Viewpoint - The article discusses the current state and future prospects of end-to-end autonomous driving, emphasizing the concept of a "World Engine" to address challenges in the field [2][21]. Definition of End-to-End Autonomous Driving - End-to-end autonomous driving is defined as "learning a single model that directly maps raw sensor inputs to driving scenarios and outputs control commands," replacing traditional modular pipelines with a unified function [3][6]. Development Roadmap of End-to-End Autonomous Driving - The evolution of end-to-end autonomous driving has progressed from simple black-and-white image inputs over 20 years to more complex methods, including conditional imitation learning and modular approaches [8][10]. Current State of End-to-End Autonomous Driving - The industry is currently in the "1.5 generation" phase, focusing on foundational models and addressing long-tail problems, with two main branches: the World Model (WM) and Visual Language Action (VLA) [10][11]. Challenges in Real-World Deployment - Collecting data for all scenarios, especially extreme cases, remains a significant challenge for achieving Level 4 (L4) or Level 5 (L5) autonomous driving [17][18]. Concept of the "World Engine" - The "World Engine" concept aims to learn from human expert driving and generate extreme scenarios for training, which can significantly reduce costs associated with large fleets [21][24]. Data and Algorithm Engines - The "World Engine" consists of a Data Engine for generating extreme scenarios and an Algorithm Engine, which is still under development, to improve and train end-to-end algorithms [24][25].

MeWM团队 投稿 量子位 | 公众号 QbitAI 医学领域，也有自己的世界模型了。 来自香港科技大学（广州）、约翰霍普金斯大学等机构的学者联合提出了提出 医学世界模型 （Medical World Model, MeWM） ，赋予了 AI"预演"疾病发展的能力。 MeWM可以 基于临床治疗决策，模拟未来肿瘤演化过程 ，可以为个性化治疗提供可视化、可评估、可优化的辅助。 初始阶段会并行生成B个治疗组合 （称为protocol beams） ，覆盖不同策略空间。 随后， 动态模型 （Dynamics Model） 会针对每个候选方案，利用3D条件扩散模型模拟治疗后肿瘤形态，逐步构建方案执行轨迹。生成的 每一组术后肿瘤候选将交由启发式函数评估。 在这一过程当中， 逆动态模型 （Inverse Dynamics Model） 还会在每一步中对所有候选肿瘤图像进行生存风险的打分。 基于启发式函数输出风险值，并动态替换掉风险最高的beam方案，从而实现低风险方案的优先保留与高风险方案的迭代优化。 具体来说，MeWM主要有三大核心功能： 什么是医学世界模型？ MeWM引入了世界模型 （WM） 的理念，构建了"观察–模拟 ...