这几个月其实很多小伙伴联系柱哥咨询未来的建议，有工作两三年的也有硕士甚至本科生。他们在刚接触这个领域时，往往会遇到很多问题。从模块化的量产算 法发展到端到端，再到如今的VLA。核心算法涉及BEV感知、视觉语言模型VLM、扩散模型、强化学习、世界模型等等。通过学习端到端与VLA自动驾驶，可以 掌握学术界和工业界最前沿的技术方向。据现有行业的发展来看，端到端和VLA的岗位快要饱和，留下的窗口期没多久了...... 很多同学的咨询如何快速高效的入门端到端和VLA。因此自动驾驶之心联合了 工业界 和 学术界 的大佬开展了 《端到端与VLA自动驾驶小班课》 和 《自动驾驶 VLA和大模型实战课程》 ！ 扫码报名！优惠名额仅剩6个 扫码报名！抢占课程名额 课程大纲 自动驾驶VLA与大模型实战课程 由学术界大佬带队！ 这门课程聚焦在VLA领域，从VLM作为自动驾驶解释器开始，到模块化VLA、一体化VLA，再到当前主流的推理增强VLA。三大自动驾驶 VLA领域全面梳理， 非常适合刚接触大模型、VLA的同学。 课程也配套了详细的理论基础梳理，Vision/Language/Acition三大模块、强化学习、扩散模型等等基 础， ...

Core Insights - There is a significant demand for end-to-end and VLA (Vision-Language-Action) technical talent in the automotive industry, particularly among major manufacturers and suppliers [1][3] - The industry is evolving from modular production algorithms to end-to-end solutions and now to VLA, with core algorithms involving BEV perception, VLM, diffusion models, reinforcement learning, and world models [3] Group 1: Industry Demand and Trends - The demand for end-to-end and VLA technology talent is high, with inquiries from multiple companies, including three major manufacturers and several suppliers [1] - The industry primarily operates under two paradigms: single-stage and two-stage approaches, with UniAD being a representative of the single-stage model [1] - The end-to-end approach has diversified into various subfields, especially those based on VLA, with a surge in related academic publications and industrial applications in recent years [1] Group 2: Educational Initiatives - The company has launched courses focused on end-to-end and VLA autonomous driving, aimed at helping individuals quickly and efficiently enter these fields [3][12] - The "VLA and Large Model Practical Course" covers VLA from VLM as an autonomous driving interpreter to modular and integrated VLA, including detailed theoretical foundations and practical assignments [3][12] - The "End-to-End and VLA Autonomous Driving Course" focuses on key algorithms and theoretical foundations, including BEV perception, large language models, diffusion models, and reinforcement learning [12][14] Group 3: Instructor Expertise - The courses are led by experts from both academia and industry, with backgrounds in multimodal perception, autonomous driving VLA, and large model frameworks [8][11][14] - Instructors have published numerous papers in top-tier conferences and possess extensive experience in research and practical applications in autonomous driving and large models [8][11][14] Group 4: Target Audience - The courses are designed for individuals with a foundational knowledge of autonomous driving, familiar with basic modules, and concepts such as transformer models, reinforcement learning, and BEV perception [15][16] - Participants are expected to have a background in probability theory, linear algebra, and programming skills in Python and PyTorch [15][16]

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 approaches and the recent focus on Vision-Language Models (VLA) [1][3]. Group 1: End-to-End Algorithms - End-to-end algorithms are central to the current mass production of autonomous driving technology, involving a rich technology stack [1]. - There are two main paradigms in the industry: single-stage and two-stage approaches, with UniAD being a representative of the single-stage paradigm [1]. - The single-stage approach can be further categorized into several subfields, including perception-based, diffusion model-based, world model-based, and VLA-based end-to-end algorithms [1]. Group 2: VLA and Course Offerings - The article mentions the recent surge in interest regarding how to efficiently learn about end-to-end and VLA technologies, leading to the creation of specialized courses [3]. - The "End-to-End and VLA Autonomous Driving Course" focuses on VLA, covering topics from VLM as an autonomous driving interpreter to modular and integrated VLA approaches [3]. - The course includes a detailed theoretical foundation and practical assignments to help participants build their own VLA models and datasets [3]. Group 3: Course Instructors - The course features a team of instructors with significant academic and practical experience in multi-modal perception, autonomous driving VLA, and large model frameworks [7][9]. - Instructors have published numerous papers in top international conferences and have hands-on experience in developing and implementing cutting-edge algorithms in the field [7][9][10]. Group 4: Target Audience and Requirements - The courses are designed for individuals with a foundational understanding of autonomous driving and familiarity with key technologies such as transformer models, reinforcement learning, and BEV perception [13]. - Participants are expected to have a basic knowledge of probability theory, linear algebra, and proficiency in Python and PyTorch [13].

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][10] 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] VLA and Course Offerings - The article mentions the launch of courses aimed at helping individuals quickly and efficiently learn about end-to-end and VLA in autonomous driving, featuring collaboration between industry and academia [3] - The "VLA and Large Model Practical Course" focuses on VLA, covering topics from VLM as an autonomous driving interpreter to modular and integrated VLA approaches [3] Course Structure and Faculty - The course structure includes a comprehensive overview of VLA, with detailed theoretical foundations in Vision, Language, and Action, as well as practical assignments to build VLA models and datasets from scratch [3][10] - The teaching team consists of experienced professionals from top academic institutions and industry, with backgrounds in multimodal perception, autonomous driving, and large model frameworks [7][9][10] Target Audience and Requirements - The courses are designed for individuals with a foundational understanding of autonomous driving and familiarity with key technologies such as transformer models, reinforcement learning, and BEV perception [13] - Participants are expected to have a basic knowledge of probability theory, linear algebra, and programming skills in Python and PyTorch [13]

Core Viewpoint - VLA (Vision-Language-Action) is emerging as a mainstream keyword in autonomous driving, with new players rapidly entering the field and industrial production accelerating, while academia continues to innovate and compete [1][2]. Summary by Sections 1. VLA Research and Development - The VLA model represents a shift from traditional modular architectures to a unified end-to-end model that directly maps raw sensor inputs to driving control commands, addressing previous bottlenecks in autonomous driving technology [3][4]. - Traditional modular architectures (L2-L4) have clear advantages in terms of logic and independent debugging but suffer from cumulative error effects and information loss, making them less effective in complex traffic scenarios [4][5]. 2. VLA Model Advantages - The introduction of VLA models leverages the strengths of large language models (LLMs) to enhance interpretability, reliability, and the ability to generalize to unseen scenarios, thus overcoming limitations of earlier models [5][6]. - VLA models can explain their decision-making processes in natural language, improving transparency and trust in autonomous systems [5][6]. 3. Course Objectives and Structure - The course aims to provide a systematic understanding of VLA, helping participants develop practical skills in model design and research paper writing, while also addressing common challenges faced by newcomers in the field [6][7]. - The curriculum includes 12 weeks of online group research, followed by 2 weeks of paper guidance and 10 weeks of paper maintenance, focusing on both theoretical knowledge and practical coding skills [7][8]. 4. Enrollment and Requirements - The program is designed for a small group of 6 to 8 participants, targeting individuals with a foundational understanding of deep learning and basic programming skills [11][16]. - Participants are expected to engage actively in discussions and complete assignments on time, maintaining academic integrity throughout the course [20][29]. 5. Course Highlights - The course offers a comprehensive learning experience with a multi-faceted teaching approach, including guidance from experienced mentors and a structured evaluation system to track progress [23][24]. - Participants will gain access to essential resources, including datasets and baseline codes, to facilitate their research and experimentation [24][25].

Core Viewpoint - End-to-End Autonomous Driving (E2E) is identified as the core algorithm for intelligent driving mass production, with significant advancements and competition emerging in the industry following the recognition of UniAD at CVPR [2][3] Group 1: E2E Autonomous Driving Overview - E2E systems directly model the relationship between sensor inputs and vehicle control information, avoiding error accumulation seen in traditional modular approaches [2] - The introduction of BEV perception has bridged gaps between modular methods, leading to a significant technological leap [2] - The emergence of various algorithms indicates that UniAD is not the ultimate solution for E2E, highlighting the rapid development in this field [2] Group 2: Learning Challenges in E2E - The fast-paced development in E2E technology has made previous educational resources inadequate, necessitating a comprehensive understanding of multiple domains such as multimodal large models, BEV perception, and reinforcement learning [3][4] - Beginners face challenges due to fragmented knowledge and the overwhelming volume of literature, often leading to abandonment before mastering the concepts [3] Group 3: Course Development - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address learning challenges, focusing on practical and theoretical integration [4][5][6] - The course aims to provide a structured framework for understanding E2E research and enhance research capabilities by categorizing papers and extracting innovative points [5] Group 4: Course Structure - The course includes five chapters covering topics from the introduction of E2E algorithms to practical applications involving RLHF fine-tuning [9][10][11][12][13] - Key areas of focus include the evolution of E2E paradigms, the significance of VLA in the current landscape, and practical implementations of diffusion models [11][12] Group 5: Expected Outcomes - Participants are expected to achieve a level equivalent to one year of experience as an E2E autonomous driving algorithm engineer, mastering various methodologies and key technologies [18] - The course aims to facilitate the application of learned concepts in real-world projects, enhancing employability in the autonomous driving sector [18]

Core Viewpoint - End-to-End Autonomous Driving is the core algorithm for the next generation of intelligent driving mass production, marking a significant shift in the industry towards more integrated and efficient systems [1][3]. Group 1: End-to-End Autonomous Driving Overview - End-to-End Autonomous Driving can be categorized into single-stage and two-stage approaches, with the former directly modeling vehicle planning and control from sensor data, thus avoiding error accumulation seen in modular methods [1][4]. - The emergence of UniAD has initiated a new wave of competition in the autonomous driving sector, with various algorithms rapidly developing in response to its success [1][3]. Group 2: Challenges in Learning and Development - The rapid advancement in technology has made previous educational resources outdated, creating a need for updated learning paths that encompass multi-modal large models, BEV perception, reinforcement learning, and more [3][5]. - Beginners face significant challenges due to the fragmented nature of knowledge across various fields, making it difficult to extract frameworks and understand development trends [3][6]. Group 3: Course Structure and Content - The course on End-to-End and VLA Autonomous Driving aims to address these challenges by providing a structured learning path that includes practical applications and theoretical foundations [5][7]. - The curriculum covers the history and evolution of End-to-End algorithms, background knowledge necessary for understanding current technologies, and practical applications of various models [8][9]. Group 4: Key Technologies and Innovations - The course highlights significant advancements in two-stage and single-stage End-to-End methods, including notable algorithms like PLUTO and DiffusionDrive, which represent the forefront of research in the field [4][10][12]. - The integration of large language models (VLA) into End-to-End systems is emphasized as a critical area of development, with companies actively exploring new generation mass production solutions [13][14]. Group 5: Expected Outcomes and Skills Development - Upon completion of the course, participants are expected to reach a level equivalent to one year of experience as an End-to-End Autonomous Driving algorithm engineer, mastering various methodologies and key technologies [22][23]. - The course aims to equip participants with the ability to apply learned concepts to real-world projects, enhancing their employability in the autonomous driving sector [22][23].