Core Insights - The article discusses the current state and future prospects of autonomous driving technology, emphasizing the importance of AI and various modeling approaches in achieving higher levels of automation [4][6][9]. Group 1: Industry Development - The autonomous driving industry is rapidly evolving, with significant advancements expected in the next few years, particularly in AI and related fields [4]. - Companies like Waymo and Tesla are leading the way in achieving Level 4 (L4) automation, while Level 5 (L5) may take at least five more years to realize [4][6]. - The integration of Vision-Language Models (VLA) is seen as a key to enhancing decision-making capabilities in autonomous vehicles, addressing long-tail problems that pure end-to-end models may struggle with [6][9]. Group 2: Technical Approaches - The article outlines different modeling approaches in autonomous driving, including end-to-end models and the emerging VLA paradigm, which combines language processing with visual data to improve reasoning and decision-making [5][9]. - The effectiveness of current autonomous driving systems is still limited, with many challenges remaining in achieving full compliance with traffic regulations and safety standards [10][14]. - The discussion highlights the importance of data and cloud computing capabilities in narrowing the performance gap between domestic companies and leaders like Tesla [14][15]. Group 3: Talent and Education - There is a recognized talent gap in the autonomous driving sector, with a strong recommendation for students to pursue AI and computer science to prepare for future opportunities in the industry [4][6]. - The article suggests that practical experience in larger autonomous driving companies may provide better training and growth opportunities compared to smaller robotics firms [16][20].

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].

Core Viewpoint - The Medical World Model (MeWM) has been developed to enhance personalized treatment in oncology by simulating tumor evolution and optimizing clinical decision-making through AI technology [1][2]. Group 1: Overview of MeWM - MeWM introduces the concept of a world model, creating a closed-loop process of "observe-simulate-evaluate-optimize" [3]. - The model uses imaging observations as input, generating initial states and predicting future states based on various interventions [4]. Group 2: Core Functions of MeWM - MeWM consists of three main components: a strategy model that generates treatment combinations, a dynamic model that simulates tumor morphology post-treatment, and an inverse dynamics model that scores survival risks for each candidate tumor image [5][6][7]. - The strategy model generates multiple treatment combinations (protocol beams) covering different strategy spaces, while the dynamic model simulates the tumor's response to each treatment [6][11]. Group 3: Clinical Decision-Making Process - The process involves generating treatment combinations, simulating tumor evolution, and evaluating survival risks to select the optimal intervention path [9][13]. - MeWM's approach allows for a data-driven, personalized treatment decision-making process in real liver cancer scenarios [13]. Group 4: Validation and Performance - MeWM has been validated through systematic experiments on both private and public datasets, demonstrating its effectiveness in optimizing treatment decisions [17]. - In visual Turing tests, MeWM's generated images were misidentified as real images at a rate of 79%, indicating high specificity compared to existing methods [16][19]. Group 5: Risk Assessment and Comparison - MeWM's heuristic model shows higher accuracy in survival risk assessment compared to traditional Cox proportional hazards models, with a mean squared error (MSE) of 0.2142 versus 0.3550 for Cox models [21][22]. - Kaplan-Meier analysis indicates that MeWM has superior risk stratification capabilities, achieving a C-Index of 0.752 [23]. Group 6: Clinical Application and Impact - In TACE treatment exploration, MeWM achieved an F1-score of 52.38% on private datasets, outperforming other multimodal models by over 10% [29]. - The integration of MeWM into clinical workflows can enhance pre-treatment outcome predictions by an average of 13% in F1-score, aligning closely with expert recommendations [30].