Core Viewpoint - The article emphasizes the convergence of autonomous driving technology, highlighting the shift from numerous diverse approaches to a more unified model, which indicates higher technical barriers in the industry [1] Group 1 - The industry is moving towards a unified solution with models like one model, VLM, and VLA, suggesting a reduction in the need for numerous algorithm engineers [1] - The article encourages the establishment of a large community to support industry professionals, facilitating growth and collaboration among peers [1] - A new job-related community is being launched to discuss industry trends, company developments, product research, and job opportunities [1]

Core Viewpoint - End-to-end (E2E) autonomous driving is currently the core algorithm for mass production in intelligent driving, with significant advancements in the VLM/VLA systems leading to high demand for related positions and salaries reaching up to 1 million annually [2][11]. Group 1: Industry Trends - The concept of E2E has evolved significantly, with various technical schools emerging, yet many still struggle to understand its workings and distinctions between single-stage and two-stage approaches [2][4]. - The introduction of VLA (Vision-Language Architecture) is seen as a new frontier in autonomous driving, with companies actively researching and developing new generation mass production solutions [21][22]. Group 2: Educational Initiatives - A new course titled "End-to-End and VLA Autonomous Driving" has been launched to address the challenges faced by newcomers in the field, focusing on practical applications and theoretical foundations [14][27]. - The course aims to provide a comprehensive understanding of E2E autonomous driving, covering various models and methodologies, including diffusion models and reinforcement learning [6][19][21]. Group 3: Job Market Insights - The job market for VLA/VLM algorithm experts is robust, with salaries for positions requiring 3-5 years of experience ranging from 40K to 70K monthly, indicating a strong demand for skilled professionals [11][12]. - Positions such as VLA model quantization deployment engineers and multi-modal VLA model direction experts are particularly sought after, reflecting the industry's shift towards advanced algorithmic solutions [11][12].

Core Viewpoint - The article emphasizes the establishment of a leading communication platform for autonomous driving technology in China, focusing on industry, academic, and career development aspects [1]. Group 1 - The platform, named "Autonomous Driving Heart," aims to facilitate discussions and exchanges among professionals in various fields related to autonomous driving technology [1]. - The technical discussion group covers a wide range of topics including large models, end-to-end systems, VLA, BEV perception, multi-modal perception, occupancy, online mapping, 3DGS, multi-sensor fusion, transformers, point cloud processing, SLAM, depth estimation, trajectory prediction, high-precision maps, NeRF, planning control, model deployment, autonomous driving simulation testing, product management, hardware configuration, and AI job exchange [1]. - Interested individuals are encouraged to join the community by adding a WeChat assistant and providing their company/school, nickname, and research direction [1].

Group 1 - The article highlights the growing anxiety among job seekers, particularly students and professionals looking to transition into new fields, driven by the desire for better opportunities [1] - It notes that the landscape of autonomous driving technology is becoming more standardized, with a shift from numerous directions requiring algorithm engineers to a focus on unified models like one model, VLM, and VLA, indicating higher technical barriers [1] - The article emphasizes the importance of community building to support individuals in their career growth and industry knowledge, leading to the establishment of a job-related community for discussions on industry trends, company developments, and job opportunities [1]

Core Viewpoint - The article emphasizes the shift in research focus from traditional perception and planning methods to end-to-end Vision-Language-Action (VLA) models in the autonomous driving field, highlighting the emergence of various subfields and the need for researchers to adapt to these changes [2][3]. Group 1: VLA Research Directions - The end-to-end development has led to the emergence of multiple technical subfields, categorized into one-stage and two-stage end-to-end approaches, with examples like PLUTO and UniAD [2]. - Traditional fields such as BEV perception and multi-sensor fusion are becoming mature, while the academic community is increasingly focusing on large models and VLA [2]. Group 2: Research Guidance and Support - The program offers structured guidance for students in VLA and autonomous driving, aiming to help them systematically grasp key theoretical knowledge and develop their own research ideas [7][10]. - The course includes a comprehensive curriculum covering classic and cutting-edge papers, coding implementation, and writing methodologies, ensuring students can produce a solid research paper [8][11]. Group 3: Enrollment and Requirements - The program is open to a limited number of students (6 to 8 per session) who are pursuing degrees in VLA and autonomous driving [6]. - Students are expected to have a foundational understanding of deep learning, Python, and PyTorch, with additional support provided for those needing to strengthen their basics [12][14]. Group 4: Course Structure and Outcomes - The course spans 12 weeks of online group research followed by 2 weeks of paper guidance, culminating in a maintenance period for the research paper [11]. - Participants will produce a draft of a research paper, receive project completion certificates, and may obtain recommendation letters based on their performance [15].

Core Viewpoint - End-to-end (E2E) autonomous driving is currently the core algorithm for mass production in the intelligent driving sector, with significant advancements in VLM (Vision-Language Model) and VLA (Vision-Language Architecture) systems driving the industry forward [2][3]. Group 1: Industry Trends - The E2E approach has become a competitive focus for domestic new energy vehicle manufacturers, with the emergence of VLA concepts leading to a new wave of production scheme iterations [2]. - Salaries for positions related to VLM/VLA are reported to reach up to one million annually, with monthly salaries around 70K [2]. - The rapid development of technology has made previous solutions inadequate, necessitating a comprehensive understanding of various technical fields such as multimodal large models, BEV perception, reinforcement learning, and diffusion models [3][4]. Group 2: Educational Initiatives - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address the challenges faced by learners in this complex field, focusing on practical applications and theoretical foundations [4][5][6]. - The course aims to provide a structured learning path, helping students build a framework for research and enhance their research capabilities by categorizing papers and extracting innovative points [5]. - Practical components are included to ensure a complete learning loop from theory to application, addressing the gap between academic knowledge and real-world implementation [6]. Group 3: Course Structure - The course is divided into several chapters, covering topics such as the history and evolution of E2E algorithms, background knowledge on relevant technologies, and detailed explorations of both one-stage and two-stage E2E methods [9][10][11]. - Key areas of focus include the introduction of various E2E paradigms, the significance of world models, and the application of diffusion models in trajectory prediction [11][12]. - The final chapter includes a major project on RLHF (Reinforcement Learning from Human Feedback) fine-tuning, allowing students to apply their knowledge in practical scenarios [13]. Group 4: Target Audience and Outcomes - The course is designed for individuals with a foundational understanding of autonomous driving and related technologies, aiming to elevate their expertise to a level comparable to that of an E2E autonomous driving algorithm engineer within a year [20]. - Participants will gain a comprehensive understanding of E2E frameworks, including one-stage, two-stage, world models, and diffusion models, as well as deeper insights into key technologies like BEV perception and multimodal large models [20].

Core Insights - The article discusses the advancements in end-to-end autonomous driving algorithms, highlighting the emergence of various models and approaches in recent years, such as PLUTO, UniAD, OccWorld, and DiffusionDrive, which represent different technical directions in the field [1] - It emphasizes the shift in academic focus towards large models and Vision-Language-Action (VLA) methodologies, suggesting that traditional perception and planning tasks are becoming less prominent in top conferences [1] - The article encourages researchers to align their work with large models and VLA, indicating that there are still many subfields to explore despite the challenges for beginners [1] Summary by Sections Section 1: VLA Research Topics - The article introduces VLA research topics aimed at helping students systematically grasp key theoretical knowledge and expand their understanding of the specified direction [6] - It addresses the need for students to combine theoretical models with practical coding skills to develop new models and enhance their research capabilities [6] Section 2: Enrollment Information - The program has a limited enrollment capacity of 6 to 8 students per session [5] - It targets students at various academic levels (bachelor's, master's, and doctoral) who are interested in enhancing their research skills in autonomous driving and AI [7] Section 3: Course Outcomes - Participants will analyze classic and cutting-edge papers, understand key algorithms, and learn about writing and submission methods for academic papers [8][10] - The course includes a structured timeline of 12 weeks of online group research, followed by 2 weeks of paper guidance and a 10-week maintenance period [10] Section 4: Course Highlights - The program features a "2+1" teaching model with experienced instructors providing comprehensive support throughout the learning process [13] - It emphasizes high academic standards and aims to equip students with a rich set of outputs, including a paper draft and a project completion certificate [13] Section 5: Technical Requirements - Students are expected to have a foundational understanding of deep learning, basic programming skills in Python, and familiarity with PyTorch [11] - Hardware requirements include access to high-performance machines, preferably with multiple GPUs [11] Section 6: Service and Support - The program includes dedicated supervisors to track student progress and provide assistance with academic and non-academic issues [17] - The course will be conducted via Tencent Meeting and recorded for later access [18]

Core Viewpoint - End-to-end (E2E) autonomous driving is currently the core algorithm for mass production in intelligent driving, with significant advancements in the VLA (Vision-Language Architecture) and VLM (Vision-Language Model) systems, leading to high demand for related positions in the industry [2][4]. Summary by Sections Section 1: Background Knowledge - The course aims to provide a comprehensive understanding of end-to-end autonomous driving, including its historical development and the transition from modular to end-to-end approaches [21]. - Key technical stacks such as VLA, diffusion models, and reinforcement learning are essential for understanding the current landscape of autonomous driving technology [22]. Section 2: Job Market Insights - Positions related to VLA/VLM algorithms offer lucrative salaries, with 3-5 years of experience earning between 40K to 70K monthly, and top talents in the field can earn up to 1 million annually [10]. - The demand for VLA-related roles is increasing, indicating a shift in the industry towards advanced model architectures [9]. Section 3: Course Structure - The course is structured into five chapters, covering topics from basic concepts of end-to-end algorithms to advanced applications in VLA and reinforcement learning [19][30]. - Practical components are included to bridge the gap between theory and application, ensuring participants can implement learned concepts in real-world scenarios [18]. Section 4: Technical Innovations - Various approaches within end-to-end frameworks are explored, including two-stage and one-stage methods, with notable models like PLUTO and UniAD leading the way [4][23]. - The introduction of diffusion models has revolutionized trajectory prediction, allowing for better adaptability in uncertain driving environments [24]. Section 5: Learning Outcomes - Participants are expected to achieve a level of proficiency equivalent to one year of experience as an end-to-end autonomous driving algorithm engineer, mastering key technologies and frameworks [32]. - The course emphasizes the importance of understanding BEV perception, multimodal models, and reinforcement learning to stay competitive in the evolving job market [32].

Core Viewpoint - End-to-End Autonomous Driving (E2E) is identified as the core algorithm for intelligent driving mass production, marking a significant shift in the industry towards more integrated and efficient systems [2][4]. Group 1: Technology Overview - E2E can be categorized into single-stage and two-stage approaches, with the latter gaining traction following the recognition of UniAD at CVPR [2]. - The E2E system directly models the relationship between sensor inputs and vehicle control information, minimizing errors associated with modular approaches [2]. - The introduction of BEV perception has bridged gaps between modular methods, leading to a technological leap in the field [2]. Group 2: Challenges in Learning - The rapid development of E2E technology has made previous educational resources outdated, creating a need for updated learning materials [5]. - The fragmented nature of knowledge across various domains complicates the learning process for newcomers, often leading to abandonment before mastery [5]. - A lack of high-quality documentation in E2E research increases the difficulty of entry into the field [5]. Group 3: Course Development - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address the challenges faced by learners [6]. - The course aims to provide a quick entry into core technologies using accessible language and examples, facilitating easier expansion into specific knowledge areas [6]. - It focuses on building a framework for understanding E2E research and enhancing research capabilities by categorizing papers and extracting innovative points [7]. Group 4: Course Structure - The course is structured into several chapters, covering topics from the history and evolution of E2E algorithms to practical applications and advanced techniques [11][12][20]. - Key areas of focus include the introduction of E2E algorithms, background knowledge on relevant technologies, and detailed explorations of both single-stage and two-stage methods [11][12][20]. - Practical components are integrated into the curriculum to ensure a comprehensive understanding of theoretical concepts [8]. Group 5: Expected Outcomes - Participants are expected to achieve a level of proficiency equivalent to one year of experience as an E2E autonomous driving algorithm engineer [27]. - The course will cover a wide range of methodologies, including single-stage, two-stage, world models, and diffusion models, providing a holistic view of the E2E landscape [27]. - A deeper understanding of key technologies such as BEV perception, multimodal large models, and reinforcement learning will be developed [27].

Core Viewpoint - The article discusses the differences between VLA (Vision-Language-Action) and end-to-end models in the context of autonomous driving, emphasizing the importance of large models and their applications in the industry [2]. Group 1: Job Descriptions and Requirements - Positions related to large model development, including VLA and end-to-end roles, are highlighted, with a focus on skills in fine-tuning, lightweight models, and deployment [2]. - The job of an end-to-end/VLA engineer involves developing and implementing driving systems, optimizing model structures, and constructing high-quality training datasets [6]. - The VLA/VLM algorithm position requires a master's degree in computer science or AI, with 3-5 years of experience in autonomous driving or AI algorithms, and proficiency in VLA/VLM architectures [8][10]. Group 2: Technical Skills and Experience - Candidates are expected to have experience with multimodal large language models, fine-tuning existing models for specific business scenarios, and familiarity with Transformer and multimodal technologies [5]. - Experience in computer vision, trajectory prediction, and decision planning is essential, along with a strong foundation in mainstream technologies and frameworks like PyTorch [9]. - The article emphasizes the need for candidates to have published papers in top conferences or achieved notable results in international competitions [9][11].