Group 1 - The article highlights the importance of continuous learning in the field of autonomous driving, emphasizing the need for professionals to stay updated with the latest technologies and trends [6] - It mentions a variety of advanced topics and learning routes available, including VLA, world models, closed-loop simulation, and diffusion models, indicating a comprehensive curriculum for learners [6] - The platform offers opportunities for direct interaction with industry leaders and academic experts, facilitating knowledge exchange and networking [6] Group 2 - The article outlines various promotional offers for new users, including discounts on courses and membership renewals, aimed at attracting more participants to the learning community [4][3] - It lists seven premium courses available, covering essential topics such as trajectory prediction, camera calibration, and 3D point cloud detection, catering to both beginners and advanced learners [6] - The content emphasizes the significance of face-to-face discussions with top authors and experts in the field, enhancing the learning experience through direct engagement [6]

Core Viewpoint - The article discusses the development and features of the PLUTO model within the end-to-end autonomous driving domain, emphasizing its unique two-stage architecture and its direct encoding of structured perception outputs for downstream control tasks [1][2]. Summary by Sections Overview of PLUTO - PLUTO is characterized by its three main losses: regression loss, classification loss, and imitation learning loss, which collectively contribute to the model's performance [7]. - Additional auxiliary losses are incorporated to aid model convergence [9]. Course Introduction - The article introduces a new course titled "End-to-End and VLA Autonomous Driving," developed in collaboration with top algorithm experts from domestic leading manufacturers, aimed at addressing the challenges faced by learners in this rapidly evolving field [12][15]. Learning Challenges - The course addresses the difficulties learners face due to the fast-paced development of technology and the fragmented nature of knowledge across various domains, making it hard for beginners to grasp the necessary concepts [13]. Course Features - The course is designed to provide quick entry into the field, build a framework for research capabilities, and combine theory with practical applications [15][16][17]. Course Outline - The course consists of several chapters covering topics such as the history and evolution of end-to-end algorithms, background knowledge on various technologies, and detailed discussions on both one-stage and two-stage end-to-end methods [20][21][22][29]. Practical Application - The course includes practical assignments, such as RLHF fine-tuning, allowing students to apply their theoretical knowledge in real-world scenarios [31]. Instructor Background - The instructor, Jason, has a strong academic and practical background in cutting-edge algorithms related to end-to-end and large models, contributing to the course's credibility [32]. Target Audience and Expected Outcomes - The course is aimed at individuals with a foundational understanding of autonomous driving and related technologies, with the goal of elevating their skills to the level of an end-to-end autonomous driving algorithm engineer within a year [36].

Core Viewpoint - The article discusses various end-to-end models in autonomous driving, focusing on their architectures and functionalities, particularly the UniAD framework and its modular components for perception, prediction, and planning [4][13]. Group 1: End-to-End Models - End-to-end models are categorized into two types: completely black-box models like OneNet, which optimize the planner directly, and modular end-to-end models that reduce error accumulation through interactions between perception, prediction, and planning modules [3]. - The UniAD framework consists of four main parts: multi-view camera input, backbone for BEV feature extraction, perception for scene-level understanding, and prediction for multi-mode trajectory forecasting [4]. Group 2: Specific Model Architectures - TrackFormer utilizes three types of queries: detection, tracking, and ego queries, with a dynamic length for the tracking query set based on object disappearance [6]. - MotionFormer operates similarly to RNN structures, processing sequential blocks to predict future states based on previous outputs, focusing on agent-level knowledge [9]. - MapFormer employs Panoptic Segformer for environment segmentation, distinguishing between countable instances and uncountable elements [10]. Group 3: Advanced Techniques - PARA-Drive modifies the UniAD framework by adjusting the connections between perception, prediction, and planning modules, allowing for parallel training and improved inference speed [13]. - Symmetric sparse perception is divided into two parallel parts for agent detection and map perception, utilizing a DETR paradigm for both tasks [20]. - The planning transformer integrates various tokens to output action probabilities, selecting the most probable action based on human trajectory data [23]. Group 4: Community and Learning Resources - The article highlights the establishment of numerous technical discussion groups related to autonomous driving, covering over 30 learning paths and involving nearly 300 companies and research institutions [27][28].

Core Viewpoint - The article discusses the rapid evolution and challenges in the field of end-to-end autonomous driving technology, emphasizing the need for a comprehensive understanding of various algorithms and models to succeed in this competitive industry [2][4][6]. Group 1: Industry Trends - The shift from modular approaches to end-to-end systems in autonomous driving aims to eliminate cumulative errors between modules, marking a significant technological leap [2]. - The emergence of various algorithms and models, such as UniAD and BEV perception, indicates a growing focus on integrating multiple tasks into a unified framework [4][9]. - The demand for knowledge in multi-modal large models, reinforcement learning, and diffusion models is increasing, reflecting the industry's need for versatile skill sets [5][20]. Group 2: Learning Challenges - New entrants face difficulties due to the fragmented nature of knowledge and the overwhelming volume of research papers in the field, often leading to early abandonment of learning [5][6]. - The lack of high-quality documentation and practical guidance further complicates the transition from theory to practice in end-to-end autonomous driving research [5][6]. Group 3: Course Offerings - A new course titled "End-to-End and VLA Autonomous Driving" has been developed to address the learning challenges, focusing on practical applications and theoretical foundations [6][24]. - The course is structured to provide a comprehensive understanding of end-to-end algorithms, including their historical development and current trends [11][12]. - Practical components, such as real-world projects and assignments, are included to ensure that participants can apply their knowledge effectively [8][21]. Group 4: Course Content Overview - The course covers various topics, including the introduction to end-to-end algorithms, background knowledge on relevant technologies, and detailed explorations of both one-stage and two-stage end-to-end methods [11][12][13]. - Specific chapters focus on advanced topics like world models and diffusion models, which are crucial for understanding the latest advancements in autonomous driving [15][17][20]. - The final project involves practical applications of reinforcement learning from human feedback (RLHF), allowing participants to gain hands-on experience [21].

Core Viewpoint - The article discusses the development and significance of end-to-end (E2E) algorithms in autonomous driving, emphasizing the integration of various advanced technologies such as large language models (LLMs), diffusion models, and reinforcement learning (RL) in enhancing the capabilities of autonomous systems [21][31]. Summary by Sections Section 1: Overview of End-to-End Autonomous Driving - The first chapter provides a comprehensive overview of the evolution of end-to-end algorithms, explaining the transition from modular approaches to end-to-end solutions, and discussing the advantages and challenges of different paradigms [40]. Section 2: Background Knowledge - The second chapter focuses on the technical stack associated with end-to-end systems, detailing the importance of LLMs, diffusion models, and reinforcement learning, which are crucial for understanding the future job market in this field [41][42]. Section 3: Two-Stage End-to-End Systems - The third chapter delves into two-stage end-to-end systems, exploring their emergence, advantages, and disadvantages, while also reviewing notable works in the field such as PLUTO and CarPlanner [42][43]. Section 4: One-Stage End-to-End and VLA - The fourth chapter highlights one-stage end-to-end systems, discussing various subfields including perception-based methods and the latest advancements in VLA (Vision-Language Alignment), which are pivotal for achieving the ultimate goals of autonomous driving [44][50]. Section 5: Practical Application and RLHF Fine-Tuning - The fifth chapter includes a major project focused on RLHF (Reinforcement Learning from Human Feedback) fine-tuning, providing practical insights into building pre-training and reinforcement learning modules, which are applicable to VLA-related algorithms [52]. Course Structure and Learning Outcomes - The course aims to equip participants with a solid understanding of end-to-end autonomous driving technologies, covering essential frameworks and methodologies, and preparing them for roles in the industry [56][57].

Core Viewpoint - The article announces the launch of the "Heart of Autonomous Driving" project and paper guidance, aimed at assisting students facing challenges in their research and development efforts in the field of autonomous driving [1]. Group 1: Project and Guidance Overview - The project aims to provide support for students who encounter difficulties in their research, such as environmental configuration issues and debugging challenges [1]. - Last year's outcomes were positive, with several students successfully publishing papers in top conferences like CVPR and ICRA [1]. Group 2: Guidance Directions - **Direction 1**: Focus on multi-modal perception and computer vision, end-to-end autonomous driving, large models, and BEV perception. The guiding teacher has published over 30 papers in top AI conferences with a citation count exceeding 6000 [3]. - **Direction 2**: Emphasis on 3D Object Detection, Semantic Segmentation, Occupancy Prediction, and multi-task learning based on images or point clouds. The guiding teacher is a top-tier PhD with multiple publications in ECCV and CVPR [5]. - **Direction 3**: Concentration on end-to-end autonomous driving, OCC, BEV, and world model directions. The guiding teacher is also a top-tier PhD with contributions to several mainstream perception solutions [6]. - **Direction 4**: Focus on NeRF / 3D GS neural rendering and 3D reconstruction. The guiding teacher has published four CCF-A class papers, including two in CVPR and two in IEEE Transactions [7].

Core Viewpoint - End-to-End Autonomous Driving is a key algorithm for intelligent driving mass production, with significant salary potential for related positions, and it has evolved into various technical directions since the introduction of UniAD [2][4]. Group 1: Technical Directions - End-to-End Autonomous Driving can be categorized into one-stage and two-stage approaches, with various subfields emerging under each category [2][4]. - The core advantage of end-to-end systems is the direct modeling from sensor input to vehicle planning/control information, avoiding error accumulation seen in modular methods [2]. - Notable algorithms include PLUTO for two-stage end-to-end, UniAD for perception-based one-stage, OccWorld for world model-based one-stage, and DiffusionDrive for diffusion model-based one-stage [4]. Group 2: Industry Trends - The demand for VLA/VLM algorithm experts is increasing, with salary ranges for positions requiring 3-5 years of experience being between 40K-70K [9]. - The industry is witnessing a shift towards large model algorithms, with companies focusing on VLA as the next generation of autonomous driving solutions [8][9]. Group 3: Course Offerings - A new course titled "End-to-End and VLA Autonomous Driving" is being offered to help individuals understand the complexities of end-to-end algorithms and their applications [15][28]. - The course covers various topics, including background knowledge, two-stage end-to-end, one-stage end-to-end, and practical applications of reinforcement learning [20][22][24]. - The course aims to provide a comprehensive understanding of the end-to-end framework, including key technologies like BEV perception, multi-modal large models, and diffusion models [31].

Core Viewpoint - End-to-End Autonomous Driving is a key algorithm for intelligent driving mass production, with significant salary potential for related positions, and it has evolved into various technical branches since the introduction of UniAD [2] Group 1: Overview of End-to-End Autonomous Driving - End-to-End Autonomous Driving can be categorized into one-stage and two-stage approaches, with the core advantage being direct modeling from sensor input to vehicle planning/control, 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 academic and industrial focus on End-to-End technology has raised questions about whether UniAD is the ultimate solution, indicating ongoing developments in various algorithms [2] Group 2: Challenges in Learning - The rapid development of End-to-End technology has made previous solutions inadequate, necessitating knowledge in multimodal large models, BEV perception, reinforcement learning, visual transformers, and diffusion models [4] - Beginners often struggle with the fragmented nature of knowledge and the overwhelming number of papers, leading to challenges in extracting frameworks and understanding industry trends [4] Group 3: Course Features - The newly developed course on End-to-End and VLA Autonomous Driving aims to address learning challenges by providing a structured approach to mastering core technologies [5] - The course emphasizes Just-in-Time Learning, helping students quickly grasp key concepts and expand their knowledge in specific areas [5] - It aims to build a framework for research capabilities, enabling students to categorize papers and extract innovative points [6] Group 4: Course Outline - The course includes chapters on the introduction to End-to-End algorithms, background knowledge, two-stage End-to-End methods, one-stage End-to-End methods, and practical applications [11][12][13] - Key topics include the evolution of End-to-End methods, the significance of BEV perception, and the latest advancements in VLA [9][14] Group 5: Target Audience and Expected Outcomes - The course is designed for individuals aiming to enter the autonomous driving industry, providing a comprehensive understanding of End-to-End technologies [19] - Upon completion, participants are expected to achieve a level equivalent to one year of experience as an End-to-End Autonomous Driving algorithm engineer, mastering various methodologies and key technologies [22]

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]