最近和业内专家讨论了很多，分享一个最近被问到很多的问题： 世界模型是不是端到端？ 第一个问题的答案是明确的：不是。 世界模型和端到端都不指某个具体的技术，而是一类具备某些特定能力的模型。可以理解为 世界模型只是一种实现端到端自 动驾驶的途径。 目前学术界和工业界把自动驾驶世界模型收敛到生成和重建两个领域，并且主流都在利用世界模型在做闭环仿真，所以我们看到了很多相关工作的推出。这也是业 内风格转换的一个趋势，Corner Case的成本过高，我们需要更有效的的其他手段...... 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 讲师介绍 Jason：C9本科+QS50 PhD，已发表CCF-A论文2篇，CCF-B论文若干。现任国内TOP主机厂算法专家，目前从事端到端、大模型、世界模型等前沿算法的预研和量 产，并已主持和完成多项自动驾驶感知和端到端算法的产品量产交付，拥有丰富的端到端算法研发和实战经验。 课程大纲 这门课程讲如何展开 第一章：世界模型介绍 第一章主要针对自动驾驶世界模型概括性的内容讲解。 这一章老师会先复盘世界模型和端到端自动驾驶的联系，接着讲 ...

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 最近和业内专家jason老师讨论了很多，分享一个最近被问到很多的问题： 世界模型是不是端到端？以及如何看待世界模型最近爆发式的工作发表。 第一个问题的答案是明确的：不是。 世界模型和端到端都不指某个具体的技术，而是一类具备某些特定能力的模型。可以理解为 世界模型只是一种实现端到端自 动驾驶的途径。 早鸟优惠！开课即止~ 目前学术界和工业界把自动驾驶世界模型收敛到生成和重建两个领域，并且主流都在利用世界模型在做闭环仿真，所以我们看到了很多相关工作的推出。这也是业 内风格转换的一个趋势，Corner Case的成本过高，我们需要更有效的的其他手段...... 先前平台和Jason老师共同打造的《端到端与VLA自动驾驶小班课》备受大家好评，因此我们进一步推出这门世界模型小班课， 课程聚焦于通用世界模型、视频生 成、OCC生成等世界模型算法，涵盖特斯拉世界模型、李飞飞团队Marble等。欢迎大家加入学习~ 讲师介绍 Jason：C9本科+QS50 PhD，已发表CCF-A论文2篇，CCF-B论文若干。现任国内TOP主 ...

Overview - The article discusses the ongoing technological competition in the autonomous driving industry, focusing on different approaches to solving corner cases and enhancing safety and efficiency in driving systems [1][3]. Technological Approaches - There is a debate between two main technological routes: single-vehicle intelligence (VLA) and intelligent networking (VLM) [1]. - Major companies like Waymo utilize VLM, which allows AI to handle environmental understanding and reasoning, while traditional modules maintain decision-making control for safety [1]. - Companies such as Tesla, Geely, and XPeng are exploring VLA, aiming for AI to learn all driving skills through extensive data training for end-to-end decision-making [1]. Sensor and Algorithm Developments - The article highlights the evolution of perception technologies, with BEV (Bird's Eye View) perception becoming mainstream by 2022, and OCC (Occupancy) perception gaining traction in 2023 [3][5]. - BEV integrates various sensor data into a unified spatial representation, facilitating better path planning and dynamic information fusion [8][14]. - OCC perception provides detailed occupancy data, clarifying the probability of space being occupied over time, which enhances dynamic interaction modeling [6][14]. Modular and End-to-End Systems - Prior to the advent of multimodal large models and end-to-end autonomous driving technologies, perception and prediction tasks were typically handled by separate modules [5]. - The article outlines a phased approach to modularization, where perception, prediction, decision-making, and control are distinct yet interconnected [4][31]. - End-to-end systems aim to streamline the process by allowing direct mapping from raw sensor inputs to actionable outputs, enhancing efficiency and reducing bottlenecks [20][25]. VLA and VLM Frameworks - VLA (Visual-Language-Action) and VLM (Visual-Language Model) frameworks are discussed, with VLA focusing on understanding complex scenes and making autonomous decisions based on visual and language inputs [32][39]. - The article emphasizes the importance of language models in enhancing the interpretability and safety of autonomous driving systems, allowing for better cross-scenario knowledge transfer and decision-making [57]. Future Directions - The competition between VLA and WA (World Action) architectures is highlighted, with WA emphasizing direct visual-to-action mapping without language mediation [55][56]. - The article suggests that the future of autonomous driving will involve integrating world models that understand physical laws and temporal dynamics, addressing the limitations of current language models [34][54].

Core Insights - There is a significant demand for end-to-end and VLA (Vision-Language Agent) technical talent in the automotive industry, with salaries for experts reaching up to $70,000 per month for positions requiring 3-5 years of experience [1] - The technology stack involved in end-to-end and VLA is complex, covering various advanced algorithms and models such as BEV perception, VLM (Vision-Language Model), diffusion models, reinforcement learning, and world models [2] Course Offerings - The company is launching two specialized courses: "End-to-End and VLA Autonomous Driving Class" and "Practical Course on VLA and Large Models," aimed at helping individuals quickly and efficiently enter the field of end-to-end and VLA technologies [2] - The "Practical Course on VLA and Large Models" focuses on VLA, covering topics from VLM as an autonomous driving interpreter to modular and integrated VLA, including mainstream inference-enhanced VLA [2] - The course includes a detailed theoretical foundation and practical assignments, teaching participants how to build their own VLA models and datasets from scratch [2] Instructor Team - The instructor team consists of experts from both academia and industry, including individuals with extensive research and practical experience in multi-modal perception, autonomous driving VLA, and large model frameworks [7][10][13] - Notable instructors include a Tsinghua University master's graduate with multiple publications in top conferences and a current algorithm expert at a leading domestic OEM [7][13] Target Audience - The courses are designed for individuals with a foundational knowledge of autonomous driving, familiar with basic modules, and who have a grasp of concepts related to transformer large models, reinforcement learning, and BEV perception [15] - Participants are expected to have a background in probability theory and linear algebra, as well as proficiency in Python and PyTorch [15]

Core Insights - There is a significant demand for end-to-end and VLA (Vision-Language Agent) technical talent in the automotive industry, with salaries for experts reaching up to $70,000 per month for positions requiring 3-5 years of experience [1] - The technology stack for end-to-end and VLA is complex, involving various advanced algorithms such as BEV perception, Vision-Language Models (VLM), diffusion models, reinforcement learning, and world models [1] - The company is offering specialized courses to help individuals quickly and efficiently learn about end-to-end and VLA technologies, collaborating with experts from both academia and industry [1] Course Offerings - The "End-to-End and VLA Autonomous Driving Course" focuses on the macro aspects of end-to-end autonomous driving, covering key algorithms and theoretical foundations, including BEV perception, large language models, diffusion models, and reinforcement learning [10] - The "Autonomous Driving VLA and Large Model Practical Course" is led by academic experts and covers VLA from the perspective of VLM as an autonomous driving interpreter, modular VLA, and current mainstream inference-enhanced VLA [1][10] - Both courses include practical components, such as building a VLA model and dataset from scratch, and implementing algorithms like the Diffusion Planner and ORION algorithm [10][12] Instructor Profiles - The instructors include experienced professionals and researchers from top institutions, such as Tsinghua University and QS30 universities, with backgrounds in multimodal perception, autonomous driving VLA, and large model frameworks [6][9][12] - Instructors have published numerous papers in prestigious conferences and have hands-on experience in developing and deploying advanced algorithms in the field of autonomous driving [6][9][12] Target Audience - The courses are designed for individuals with a foundational knowledge of autonomous driving, familiar with basic modules, and concepts related to transformer large models, reinforcement learning, and BEV perception [14] - Participants are expected to have a background in probability theory and linear algebra, as well as proficiency in Python and PyTorch [14]

Core Viewpoint - 2023 marks the year of end-to-end production, with 2024 expected to be a significant year for end-to-end production in the automotive industry, as leading new forces and manufacturers have already achieved end-to-end production [1][3]. Group 1: End-to-End Production Development - The automotive industry is witnessing rapid development in end-to-end methods, particularly the one-stage approach exemplified by UniAD, which directly models vehicle trajectories from sensor inputs [1][3]. - There are two main paradigms in the industry: one-stage and two-stage methods, with the one-stage approach gaining traction and leading to various derivatives based on perception, world models, diffusion models, and VLA [3][5]. Group 2: Course Overview - A course titled "End-to-End and VLA Autonomous Driving" has been launched, focusing on cutting-edge algorithms in both one-stage and two-stage end-to-end methods, aimed at bridging academic and industrial advancements [5][15]. - The course is structured into several chapters, covering the history and evolution of end-to-end methods, background knowledge on VLA, and detailed discussions on both one-stage and two-stage approaches [9][10][12]. Group 3: Key Technologies - The course emphasizes critical technologies such as BEV perception, visual language models (VLM), diffusion models, and reinforcement learning, which are essential for mastering the latest advancements in autonomous driving [5][11][19]. - The second chapter of the course is highlighted as containing the most frequently asked technical keywords for job interviews in the next two years [10]. Group 4: Practical Applications - The course includes practical assignments, such as RLHF fine-tuning, allowing participants to apply their knowledge in real-world scenarios and understand how to build and experiment with pre-trained and reinforcement learning modules [13][19]. - The curriculum also covers various subfields of one-stage end-to-end methods, including those based on perception, world models, diffusion models, and VLA, providing a comprehensive understanding of the current landscape in autonomous driving technology [14][19].

Core Viewpoint - The article discusses the evolving landscape of the autonomous driving industry in China, highlighting the shift in competitive dynamics and the increasing investment in autonomous driving technologies as a core focus of AI development [1][2]. Industry Trends - The autonomous driving sector has undergone significant changes over the past two years, with new players entering the market and existing companies focusing on improving execution capabilities [1]. - The industry experienced a flourishing period before 2022, where companies with standout technologies could thrive, but has since transitioned into a more competitive environment that emphasizes addressing weaknesses [1]. - Companies that remain active in the market are progressively enhancing their hardware, software, AI capabilities, and engineering implementation to survive and excel [1]. Future Outlook - By 2025, the industry is expected to enter a "calm period," where unresolved technical challenges in areas like L3, L4, and Robotaxi will continue to present opportunities for professionals in the field [2]. - The article emphasizes the importance of comprehensive skill sets for individuals in the autonomous driving sector, suggesting that those with a short-term profit mindset may not endure in the long run [2]. Community and Learning Resources - The "Autonomous Driving Heart Knowledge Planet" community has been established to provide a comprehensive platform for learning and sharing knowledge in the autonomous driving field, featuring over 4,000 members and aiming for a growth to nearly 10,000 in the next two years [4][17]. - The community offers a variety of resources, including video content, learning pathways, Q&A sessions, and job exchange opportunities, catering to both beginners and advanced learners [4][6][18]. - Members can access detailed technical routes and practical solutions for various autonomous driving challenges, significantly reducing the time needed for research and learning [6][18]. Technical Focus Areas - The community has compiled over 40 technical routes related to autonomous driving, covering areas such as end-to-end learning, multi-modal models, and various simulation platforms [18][39]. - There is a strong emphasis on practical applications, with resources available for data processing, 4D labeling, and engineering practices in autonomous driving [12][18]. Job Opportunities - The community facilitates job opportunities by connecting members with openings in leading autonomous driving companies, providing a platform for resume submissions and internal referrals [13][22].

Core Insights - The article discusses the evolution of end-to-end algorithms in autonomous driving, highlighting the transition from modular production algorithms to end-to-end and now to Vision-Language Alignment (VLA) models [1][3] - It emphasizes the rich technology stack involved in end-to-end algorithms, including BEV perception, visual language models (VLM), diffusion models, reinforcement learning, and world models [3] Summary by Sections End-to-End Algorithms - End-to-end algorithms are categorized into two main paradigms: single-stage and two-stage, with UniAD being a representative of the single-stage approach [1] - Single-stage can further branch into various subfields, particularly those based on VLA, which have seen a surge in related publications and industrial applications in recent years [1] Courses Offered - The article promotes two courses: "End-to-End and VLA Autonomous Driving Small Class" and "Practical Course on Autonomous Driving VLA and Large Models," aimed at helping individuals quickly and efficiently enter the field [3] - The "Practical Course" focuses on VLA, covering topics from VLM as an autonomous driving interpreter to modular and integrated VLA, along with detailed theoretical foundations [3][12] Instructor Team - The instructor team includes experts from both academia and industry, with backgrounds in multi-modal perception, autonomous driving VLA, and large model frameworks [8][11][14] - Notable instructors have published numerous papers in top-tier conferences and have extensive experience in research and practical applications in autonomous driving and large models [8][11][14] Target Audience - The courses are designed for individuals with a foundational understanding of autonomous driving, familiar with basic modules, and have knowledge of transformer models, reinforcement learning, and BEV perception [15][17]

Core Insights - The article discusses the significant technological advancements in autonomous driving over the past decade, highlighting key innovations such as Visual Transformers, BEV perception, multi-sensor fusion, end-to-end autonomous driving, large models, VLA, and world models [3][4]. Group 1: Technological Milestones - The past ten years have seen remarkable technological developments in autonomous driving, with various solutions emerging through the collision and fusion of different technologies [3]. - A roundtable discussion is set to reflect on the technological milestones in the industry, focusing on the debate between world models and VLA [4][13]. Group 2: Industry Perspectives - The roundtable will feature insights from top industry leaders, discussing the evolution of autonomous driving technology and providing career advice for newcomers in the field [4][5]. - The discussion will also cover the perspectives of academia and industry regarding L3 autonomous driving, emphasizing the convergence of research directions and the practical implementation in engineering [13]. Group 3: Future Directions - The article raises questions about the future direction of autonomous driving technology, particularly the role of end-to-end systems as a foundational element of intelligent driving technology [13]. - It highlights the ongoing competition between academic research and engineering practices in the field, suggesting a need for new entrants to adapt and innovate [13].

Core Insights - The article emphasizes the importance of continuous learning and engagement in the field of autonomous driving technology, highlighting various educational resources and community interactions available for professionals and enthusiasts in the industry. Group 1: Educational Offerings - The platform offers a significant discount on courses, with an 80% off coupon and a 70% discount card available for users [3] - New users can benefit from a 30% discount on renewals and a 50% discount for specific offerings [4] - A comprehensive overview of core content related to autonomous driving is provided, including 40+ learning paths covering advanced topics [5] Group 2: Community Engagement - The platform facilitates direct interactions with industry leaders and academic experts, allowing for face-to-face discussions on cutting-edge topics in autonomous driving [6] - Key discussions include the competition between VLA and WA, future directions of autonomous driving, and the intricacies of world models [6] - The community also features high-level courses on various technical subjects such as trajectory prediction, camera calibration, and 3D point cloud detection [6]