Core Viewpoint - The article emphasizes the establishment of a comprehensive community for autonomous driving, aiming to gather industry professionals and facilitate rapid responses to challenges, with a target of building a community of 10,000 members within three years [2]. Group 1: Community Development - The community aims to integrate academic research, product development, and recruitment, creating a closed-loop system for education and technical discussions [2][5]. - It has already attracted notable figures from the industry, including talents from Huawei and leading researchers in autonomous driving [2]. - The community will provide resources such as video courses, hardware, and practical coding experiences related to autonomous driving [2][3]. Group 2: Learning Resources - A structured learning roadmap is available, covering essential topics for newcomers, including how to ask questions and access weekly Q&A sessions [3][4]. - The community offers a variety of courses on foundational topics like deep learning, computer vision, and advanced algorithms in autonomous driving [4][21]. - Members can access exclusive content, including over 5,000 resources and discounts on paid courses [19][21]. Group 3: Industry Engagement - The community collaborates with numerous companies in the autonomous driving sector, providing direct recruitment channels and job postings [5][6]. - It aims to connect students and professionals with industry leaders, enhancing networking opportunities and knowledge sharing [5][6]. - The community is positioned as a hub for both academic and industrial advancements in autonomous driving technology [12][14]. Group 4: Technological Focus - The article highlights the rapid evolution of technology in autonomous driving, with a focus on end-to-end systems and the integration of large models [7][24]. - Key areas of interest include visual language models, world models, and closed-loop simulations, which are critical for the future of autonomous driving [7][24]. - The community plans to host live sessions with experts from top conferences to discuss practical applications and research advancements [23][24].

Core Viewpoint - GeoScan S1 is presented as a highly cost-effective handheld 3D laser scanner, designed for various operational fields with features such as lightweight design, one-button operation, and centimeter-level precision in real-time 3D scene reconstruction [1][4]. Group 1: Product Features - The GeoScan S1 can generate point clouds at a rate of 200,000 points per second, with a maximum measurement distance of 70 meters and 360° coverage, supporting large scenes over 200,000 square meters [1][23]. - It integrates multiple sensors, including RTK, 3D laser radar, and dual wide-angle cameras, allowing for high-precision mapping and real-time data processing [7][28]. - The device operates on a handheld Ubuntu system and features a built-in power supply for various sensors, enhancing its usability [2][3]. Group 2: Performance and Efficiency - The scanner is designed for ease of use, with a simple one-button start for scanning tasks and immediate usability of the exported results without complex deployment [3][4]. - It boasts high efficiency and accuracy in mapping, with relative accuracy better than 3 cm and absolute accuracy better than 5 cm [16][21]. - The device supports real-time modeling and detailed restoration through multi-sensor fusion and microsecond-level data synchronization [21][28]. Group 3: Market Position and Pricing - GeoScan S1 is marketed as the most affordable option in the industry, with a starting price of 19,800 yuan for the basic version, and various configurations available for different needs [4][51]. - The product has been validated through numerous projects and collaborations with academic institutions, ensuring its reliability and effectiveness in practical applications [4][32]. Group 4: Application Scenarios - The scanner is suitable for a wide range of environments, including office buildings, parking lots, industrial parks, tunnels, forests, and mining sites, demonstrating its versatility in 3D scene mapping [32][40]. - It can be integrated with various platforms such as drones, unmanned vehicles, and robots, facilitating unmanned operations [38][40]. Group 5: Technical Specifications - The GeoScan S1 features a compact design with dimensions of 14.2 cm x 9.5 cm x 45 cm and weighs 1.3 kg without the battery [16]. - It has a battery capacity of 88.8 Wh, providing approximately 3 to 4 hours of operational time [16]. - The device supports various data export formats, including PCD, LAS, and PLV, ensuring compatibility with different software [16].

Core Viewpoint - The article discusses the evolution and differentiation between Chat and Agent in the context of artificial intelligence, emphasizing the shift from mere conversational capabilities to actionable intelligence that can perform tasks autonomously [1][2][3]. Group 1: Chat vs. Agent - Chat refers to systems focused on information processing and language communication, exemplified by ChatGPT, which provides coherent responses but does not execute tasks [1]. - Agent represents a more advanced form of AI that can think, make decisions, and perform specific tasks, thus emphasizing action over mere conversation [2][3]. Group 2: Evolution of AI Applications - The development of smart speakers, starting from basic functionalities to becoming central hubs in smart home ecosystems, illustrates the potential for AI to expand its capabilities and influence daily life [4][5]. - The transition from simple AI assistants to AI digital employees that can both converse and execute tasks marks a significant evolution in AI technology [5][6]. Group 3: AI Agent Development Paradigm - The emergence of AI Agents signifies a profound change in software development, where traditional programming paradigms are challenged by the need for AI to learn and adapt autonomously [7]. - AI Agents are structured around four key modules: Memory, Tools, Planning, and Action, which facilitate their operational capabilities [7]. Group 4: Learning Paths for AI Agents - Current learning paths for AI Agents are primarily divided into two routes: one based on OpenAI technology and the other on open-source technology, encouraging developers to explore both avenues [9]. - The rapid development of AI Agents post the explosion of large models has led to a surge in various projects and applications [9]. Group 5: Notable AI Agent Projects - AutoGPT allows users to break down goals into tasks and execute them through various methods, showcasing the practical application of AI Agents [12]. - JARVIS is a model selection agent that decomposes user requests into subtasks and utilizes expert models to execute them, demonstrating multi-modal task execution capabilities [13][15]. - MetaGPT mimics traditional software company structures, assigning roles to agents for collaborative task execution, thus enhancing the development process [16]. Group 6: Community and Learning Resources - A community of nearly 4,000 members and over 300 companies in the autonomous driving sector provides a platform for knowledge sharing and collaboration on various AI technologies [19]. - The article highlights numerous learning paths and resources available for individuals interested in autonomous driving technologies and AI applications [21].

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 Viewpoint - The article emphasizes the significance of high-fidelity simulation technology in overcoming the challenges of testing autonomous driving algorithms, particularly through the introduction of New View Synthesis (NVS) technology, which allows for the creation of closed-loop driving simulation environments based on real-world data [1][2]. Group 1: Challenges and Tasks - The workshop addresses two main challenges in the application of NVS technology, focusing on the need for improved rendering quality in extrapolated views and the evaluation of driving algorithms in closed-loop simulation environments [2][3]. - The first track, "Extrapolated View New View Synthesis," aims to enhance rendering quality under sparse input views, which is crucial for evaluating autonomous driving algorithms in various scenarios [3][4]. - The second track, "Closed-Loop Simulation Evaluation," highlights the importance of creating high-fidelity simulation environments that bridge the gap between real-world data and interactive assessments, overcoming the limitations of traditional static datasets [5][6]. Group 2: Competition Details - Each track of the workshop offers awards, including a Creative Award of $9,000, and the competition is set to commence on June 30, 2025, with submissions due by August 31, 2025 [8][9]. - The workshop encourages global participation to advance autonomous driving technology, providing a platform for challenging and valuable research [10][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 - The article emphasizes the evolving landscape of autonomous driving, particularly the integration of traditional planning and control (PnC) with end-to-end systems, highlighting the necessity for professionals to adapt to these changes in order to remain competitive in the job market [2][4][29]. Group 1: Industry Trends - The shift towards end-to-end and VLA (Vision-Language Alignment) systems is impacting traditional PnC roles, which are now required to incorporate more advanced algorithms and frameworks [2][4]. - As of 2025, end-to-end systems are expected to become more prevalent, yet traditional PnC methods will still play a crucial role, especially in safety-critical applications like Level 4 autonomous driving [4][29]. - The article discusses the importance of understanding both traditional and modern approaches to planning and control, as they are increasingly being integrated in practical applications [4][29]. Group 2: Educational Offerings - The company has launched specialized courses aimed at bridging the gap between theoretical knowledge and practical application in the field of autonomous driving, focusing on real-world challenges and interview preparation [5][7]. - The courses are designed to provide hands-on experience with current industry practices, including classic and innovative solutions in PnC, and are tailored for individuals with some background in the field [8][12]. - The curriculum includes modules on foundational algorithms, decision-making frameworks, and advanced topics such as contingency planning and interactive planning, which are critical for modern autonomous driving systems [20][21][24][26][29]. Group 3: Career Development - The courses not only focus on technical skills but also offer support in job application processes, including resume reviews and mock interviews, to enhance employability [9][10][31]. - Previous participants have successfully secured positions at major companies in the autonomous driving sector, indicating the effectiveness of the training provided [10][12]. - The program aims to equip participants with the skills necessary to construct decision-making systems and address real-world challenges in autonomous driving, thereby enhancing their career prospects [13][29].

Core Viewpoint - The article emphasizes the importance of learning from experienced mentors in the field of research, particularly in LLM/MLLM, to accelerate the research process and achieve results more efficiently [1]. Group 1: Course Offerings - The program offers a 1v6 elite small class format, allowing for personalized guidance from a mentor throughout the research process [5]. - The course covers everything from model theory to practical coding, helping participants build their own knowledge systems and understand algorithm design and innovation in LLM/MLLM [1][10]. - Participants will receive tailored ideas from the mentor to kickstart their research, even if they lack a clear direction initially [7]. Group 2: Instructor Background - The instructor has a strong academic background, having graduated from a prestigious computer science university and worked as an algorithm researcher in various companies [2]. - The instructor's research includes computer vision, efficient model compression algorithms, and multimodal large language models, with a focus on lightweight models and efficient fine-tuning techniques [2][3]. Group 3: Target Audience - The program is suitable for graduate students and professionals in the fields of autonomous driving, AI, and those looking to enhance their algorithmic knowledge and research skills [11]. - It caters to individuals who need to publish papers for academic recognition or those who want to systematically master model compression and multimodal reasoning [11]. Group 4: Course Structure and Requirements - The course is designed to accommodate students with varying levels of foundational knowledge, with adjustments made to the depth of instruction based on participants' backgrounds [14]. - Participants are expected to have a basic understanding of deep learning and machine learning, familiarity with Python and PyTorch, and a willingness to engage actively in the learning process [16][19].

Core Viewpoint - The article discusses the significance of remote operation (遥操作) in the context of embodied intelligence, emphasizing its historical roots and contemporary relevance in robotics and data collection [3][15][17]. Group 1: Understanding Remote Operation - Remote operation is not a new concept; it has been around for decades, primarily in military and aerospace applications [8][10]. - Examples of remote operation include surgical robots and remote-controlled excavators, showcasing its practical applications [8][10]. - The ideal remote operation involves spatial separation, allowing operators to control robots from a distance, thus creating value through this separation [10][15]. Group 2: Remote Operation Experience - Various types of remote operation experiences were shared, with a focus on the comfort level of different methods [19][20]. - The most comfortable method identified is pure visual inverse kinematics (IK), which allows for greater freedom of movement compared to rigid control systems [30][28]. Group 3: Future of Remote Operation - The discussion includes visions for future remote operation systems, highlighting the need for a complete control loop involving both human-to-machine and machine-to-human interactions [33][34]. - The potential for pure virtual and pure physical solutions was explored, suggesting that future systems may integrate both approaches for optimal user experience [37][39]. Group 4: Data Collection and Its Importance - Remote operation is crucial for data collection, which is essential for training robots to mimic human actions [55][64]. - The concept of "borrowing to repair the truth" was introduced, indicating that advancements in remote operation are driven by the need for better data collection in robotics [64][65]. Group 5: Implications for Robotics - The emergence of the "robot cockpit" concept indicates a trend towards more intuitive control systems for robots, integrating various functionalities into a cohesive interface [67][70]. - The challenges of controlling multiple joints in robots were discussed, emphasizing the need for innovative hardware and interaction designs to manage complex operations [68][70]. Group 6: Motion Capture and Its Challenges - Motion capture systems are essential for remote operation, but they face challenges such as precision and the need for complex setups [93][95]. - The discussion highlighted the importance of human adaptability in using motion capture systems, suggesting that users can adjust to various input methods effectively [80][81]. Group 7: ALOHA System Innovations - The ALOHA system represents a significant innovation in remote operation, focusing on minimal hardware configurations and end-to-end algorithm frameworks [102][104]. - This system has prompted the industry to rethink robot design and operational paradigms, indicating its potential long-term impact [103][104].

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]