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].

Core Insights - The article provides a comprehensive overview of various surveys and research papers related to embodied intelligence, focusing on areas such as vision-language-action models, reinforcement learning, and robotics applications [1][2][3][4][5][6][7][8][9] Group 1: Vision-Language-Action Models - A survey on Vision-Language-Action (VLA) models highlights their significance in autonomous driving and human motor learning, discussing progress, challenges, and future trends [2][3][8] - The exploration of VLA models emphasizes their applications in embodied AI, showcasing various datasets and methodologies [8][9] Group 2: Robotics and Reinforcement Learning - Research on foundation models in robotics addresses applications, challenges, and future directions, indicating a growing interest in integrating AI with robotic systems [3][4] - Deep reinforcement learning is identified as a key area with real-world successes, suggesting its potential for enhancing robotic capabilities [3] Group 3: Multimodal and Generative Approaches - The article discusses multimodal fusion and vision-language models, which are crucial for improving robot vision and interaction with the environment [6] - Generative artificial intelligence in robotic manipulation is highlighted as an emerging field, indicating a shift towards more sophisticated AI-driven robotic systems [6] Group 4: Datasets and Community Engagement - The article encourages engagement with a community focused on embodied intelligence, offering access to a wealth of resources, including datasets and collaborative projects [9]

Core Viewpoint - End-to-End Autonomous Driving (E2E) is the core algorithm for intelligent driving mass production, marking a new phase in the industry with significant advancements and competition following the recognition of UniAD at CVPR [2] Group 1: E2E Autonomous Driving Overview - E2E can be categorized into single-stage and two-stage approaches, directly modeling from sensor data to vehicle control information, thus avoiding error accumulation seen in modular methods [2] - The emergence of BEV perception has bridged gaps between modular methods, leading to a significant technological leap [2] - The rapid development of E2E has led to a surge in demand for VLM/VLA expertise, with potential salaries reaching millions annually [2] Group 2: Learning Challenges - The fast-paced evolution of E2E technology has made previous learning materials outdated, necessitating a comprehensive understanding of multi-modal large models, BEV perception, reinforcement learning, and more [3] - Beginners face challenges in synthesizing knowledge from numerous fragmented papers and transitioning from theory to practice due to a lack of high-quality documentation [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 Just-in-Time Learning to help students quickly grasp core technologies [4] - The course aims to build a framework for research capabilities, enabling students to categorize papers and extract innovative points [5] - Practical applications are integrated into the course to ensure a complete learning loop from theory to practice [6] Group 4: Course Structure - The course consists of multiple chapters covering the history and evolution of E2E algorithms, background knowledge, two-stage and one-stage E2E methods, and the latest advancements in VLA [8][9][10] - Key topics include the introduction of E2E algorithms, background knowledge on VLA, and practical applications of diffusion models and reinforcement learning [11][12] Group 5: Target Audience and Outcomes - The course is designed for individuals with a foundational understanding of autonomous driving and aims to elevate participants to a level comparable to one year of experience as an E2E algorithm engineer [19] - Participants will gain a deep understanding of key technologies such as BEV perception, multi-modal large models, and reinforcement learning, enabling them to apply learned concepts to real-world projects [19]

Core Insights - The article highlights the key research directions in computer vision and autonomous driving as presented at major conferences CVPR and ICCV, focusing on four main areas: general computer vision, autonomous driving, embodied intelligence, and 3D vision [2][3]. Group 1: Research Directions - In the field of computer vision and image processing, the main research topics include diffusion models, image quality assessment, semi-supervised learning, zero-shot learning, and open-world detection [3]. - Autonomous driving research is concentrated on end-to-end systems, closed-loop simulation, 3D ground segmentation (3DGS), multimodal large models, diffusion models, world models, and trajectory prediction [3]. - Embodied intelligence focuses on visual language navigation (VLA), zero-shot learning, robotic manipulation, end-to-end systems, sim-to-real transfer, and dexterous grasping [3]. - The 3D vision domain emphasizes point cloud completion, single-view reconstruction, 3D ground segmentation (3DGS), 3D matching, video compression, and Neural Radiance Fields (NeRF) [3]. Group 2: Research Support and Collaboration - The article offers support for various research needs in autonomous driving, including large models, VLA, end-to-end autonomous driving, 3DGS, BEV perception, target tracking, and multi-sensor fusion [4]. - In the embodied intelligence area, support is provided for VLA, visual language navigation, end-to-end systems, reinforcement learning, diffusion policy, sim-to-real, embodied interaction, and robotic decision-making [4]. - For 3D vision, the focus is on point cloud processing, 3DGS, and SLAM [4]. - General computer vision support includes diffusion models, image quality assessment, semi-supervised learning, and zero-shot learning [4].

Core Insights - The embodied intelligence sector is experiencing unprecedented investment and interest, with discussions on whether there is a bubble and which applications will mature first [1][3] Investment Landscape - The current investment scale in embodied intelligence is significantly lower than that in the smart automotive sector, indicating potential for growth once scalable commercial applications are identified [3][4] - Companies believe that more capital is needed to bridge the financing gap between domestic and international players, with domestic leading companies operating at a scale of tens of billions of RMB compared to tens of billions of USD for their US counterparts [3][4] Market Applications - B-end applications are seen as the most suitable for initial deployment, particularly in areas like logistics, quality inspection, and manufacturing processes [6][7] - The industry is exploring various strategies, including the replacement of human labor in hard-to-fill positions, with a gradual expansion into more complex scenarios over the next few years [6][7] Technological Development - The VLA (Vision, Language, Action) model is considered a key framework for the future of robotics, with ongoing improvements in data collection and model training methodologies [7][8] - The industry is moving towards a unified model paradigm, emphasizing the importance of integrating visual, linguistic, and action capabilities in robotic systems [8] Competitive Landscape - The embodied intelligence sector is expected to evolve similarly to the smartphone and automotive industries, with a diverse range of players including hardware manufacturers and AI developers [9][10] - The market is anticipated to consolidate into a limited number of major players, with a focus on maintaining technological barriers and establishing closed-loop commercial applications [10][11]