Core Viewpoint - The article emphasizes the importance of understanding end-to-end (E2E) algorithms and Visual Language Models (VLA) in the context of autonomous driving, highlighting the rapid development and complexity of the technology stack involved [2][32]. Summary by Sections Introduction to End-to-End and VLA - The article discusses the evolution of large language models over the past five years, indicating a significant technological advancement in the field [2]. Technical Foundations - The Transformer architecture is introduced as a fundamental component for understanding large models, with a focus on attention mechanisms and multi-head attention [8][12]. - Tokenization methods such as BPE (Byte Pair Encoding) and positional encoding are explained as essential for processing sequences in models [13][9]. Course Overview - A new course titled "End-to-End and VLA Autonomous Driving" is launched, aimed at providing a comprehensive understanding of the technology stack and practical applications in autonomous driving [21][33]. - The course is structured into five chapters, covering topics from basic E2E algorithms to advanced VLA methods, including practical assignments [36][48]. Key Learning Objectives - The course aims to equip participants with the ability to classify research papers, extract innovative points, and develop their own research frameworks [34]. - Emphasis is placed on the integration of theory and practice, ensuring that learners can apply their knowledge effectively [35]. Industry Demand and Career Opportunities - The demand for VLA/VLM algorithm experts is highlighted, with salary ranges between 40K to 70K for positions requiring 3-5 years of experience [29]. - The course is positioned as a pathway for individuals looking to transition into roles focused on autonomous driving algorithms, particularly in the context of emerging technologies [28].

Core Viewpoint - The article discusses the recent criticisms of the DiT (Diffusion Transformers) model, which is considered a cornerstone in the diffusion model field, highlighting the importance of scientific scrutiny and empirical validation in research [3][10]. Group 1: Criticism of DiT - A user has raised multiple concerns about DiT, claiming it is flawed both mathematically and in its structure, even questioning the presence of Transformer elements in DiT [4][12]. - The criticisms are based on a paper titled "TREAD: Token Routing for Efficient Architecture-agnostic Diffusion Training," which introduces a strategy that allows early-layer tokens to be passed to deeper layers without modifying the architecture or adding parameters [12][14]. - The critic argues that the rapid decrease in FID (Fréchet Inception Distance) during training indicates that DiT's architecture has inherent properties that allow it to easily learn the dataset [15]. - The Tread model reportedly trains 14 times faster than DiT after 400,000 iterations and 37 times faster at its best performance after 7 million iterations, suggesting that significant performance improvements may undermine previous methods [16][17]. - The critic also suggests that if parts of the network are disabled during training, it could render the network ineffective [19]. - It is noted that the more network units in DiT that are replaced with identity mappings during training, the better the model evaluation results [20]. - The architecture of DiT is said to require logarithmic scaling to represent the signal-to-noise ratio differences during the diffusion process, indicating potential issues with output dynamics [23]. - Concerns are raised regarding the Adaptive Layer Normalization method, suggesting that DiT processes conditional inputs through a standard MLP (Multi-Layer Perceptron) without clear Transformer characteristics [25][26]. Group 2: Response from Xie Saining - Xie Saining, the author of DiT, responded to the criticisms, asserting that the Tread model's findings do not invalidate DiT [27]. - He acknowledges the Tread model's contributions but emphasizes that its effectiveness is due to regularization enhancing feature robustness, not because DiT is incorrect [28]. - Saining highlights that Lightning DiT, an upgraded version of DiT, remains a powerful option and should be prioritized when conditions allow [29]. - He also states that there is no evidence to suggest that the post-layer normalization in DiT causes issues [30]. - Saining summarizes improvements made over the past year, focusing on internal representation learning and various methods for enhancing model training [32]. - He mentions that the sd-vae (stochastic depth variational autoencoder) is a significant concern for DiT, particularly regarding its high computational cost for processing images at 256×256 resolution [34].

Core Viewpoint - The article discusses criticisms of the DiT model, highlighting potential architectural flaws and the introduction of a new method called TREAD that significantly improves training efficiency and image generation quality compared to DiT [1][4][6]. Group 1 - A recent post on X claims that DiT has architectural defects, sparking significant discussion [1]. - The TREAD method achieves a training speed improvement of 14/37 times on the FID metric when applied to the DiT backbone network, indicating better generation quality [2][6]. - The post argues that DiT's FID stabilizes too early during training, suggesting it may have "latent architectural defects" that prevent further learning from data [4]. Group 2 - TREAD employs a "token routing" mechanism to enhance training efficiency without altering the model architecture, using a partial token set to save information and reduce computational costs [6]. - The author of the original DiT paper, Sseining, acknowledges the criticisms and emphasizes the importance of experimental validation over theoretical assertions [28][33]. - Sseining also points out that DiT's architecture has some inherent flaws, particularly in its use of post-layer normalization, which is known to be unstable for tasks with significant numerical range variations [13][36]. Group 3 - The article mentions that DiT's design relies on a simple MLP network for processing critical conditional data, which limits its expressive power [16]. - Sseining highlights that the real issue with DiT lies in its sd-vae component, which is inefficient and has been overlooked for a long time [36]. - The ongoing debate around DiT reflects the iterative nature of algorithmic progress, where existing models are continuously questioned and improved [38].

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 emphasizes the importance of trajectory prediction in the context of autonomous driving and highlights the ongoing relevance of traditional two-stage and modular methods despite the rise of end-to-end approaches. It discusses the integration of trajectory prediction models with perception models as a form of end-to-end training, indicating a significant area of research and application in the industry [1][2]. Group 1: Trajectory Prediction Methods - The article introduces the concept of multi-agent trajectory prediction, which aims to forecast future movements based on the historical trajectories of multiple interacting agents. This is crucial for applications in autonomous driving, intelligent monitoring, and robotic navigation [1]. - It discusses the challenges of predicting human behavior due to its uncertainty and multimodality, noting that traditional methods often rely on recurrent neural networks, convolutional networks, or graph neural networks for social interaction modeling [1]. - The article highlights the advancements in diffusion models for trajectory prediction, showcasing models like Leapfrog Diffusion Model (LED) and Mixed Gaussian Flow (MGF) that have significantly improved accuracy and efficiency in various datasets [2]. Group 2: Course Objectives and Structure - The course aims to provide a systematic understanding of trajectory prediction and diffusion models, helping participants to integrate theoretical knowledge with practical coding skills, ultimately leading to the development of new models and research papers [6][8]. - It is designed for individuals at various academic levels who are interested in trajectory prediction and autonomous driving, offering insights into cutting-edge research and algorithm design [8]. - Participants will gain access to classic and cutting-edge papers, coding implementations, and methodologies for writing and submitting research papers [8][9]. Group 3: Course Highlights and Requirements - The course features a "2+1" teaching model with experienced instructors and dedicated support staff to enhance the learning experience [16][17]. - It requires participants to have a foundational understanding of deep learning and proficiency in Python and PyTorch, ensuring they can engage with the course material effectively [10]. - The course structure includes a comprehensive curriculum covering data sets, baseline codes, and essential research papers, facilitating a thorough understanding of trajectory prediction techniques [20][21][23].

Core Viewpoint - The article emphasizes the ongoing relevance and importance of trajectory prediction in autonomous driving, despite the rise of VLA (Vehicle Localization and Awareness) technologies. It highlights that trajectory prediction remains a critical module for ensuring safety and efficiency in driving systems [1][2]. Group 1: Trajectory Prediction Importance - Trajectory prediction is essential for autonomous driving systems as it helps in identifying potential hazards and planning optimal driving routes, thereby enhancing safety and efficiency [1]. - The quality of trajectory prediction directly impacts the planning and control of autonomous vehicles, making it a fundamental component of intelligent driving systems [1]. Group 2: Research and Development in Trajectory Prediction - Academic research in trajectory prediction is thriving, with significant focus on joint prediction, multi-agent prediction, and diffusion-based approaches, which are gaining traction in major conferences [1]. - The introduction of diffusion models has shown promise in improving multi-modal modeling capabilities for trajectory prediction, addressing the challenges posed by human behavior's uncertainty and multi-modality [2][3]. Group 3: Course Offering and Objectives - A new course on trajectory prediction using diffusion models is being offered, aimed at teaching research methods and paper publication strategies, particularly for multi-agent trajectory prediction [2][9]. - The course will cover various aspects, including classic and cutting-edge papers, baseline models, datasets, and writing methodologies, to help students develop a comprehensive understanding of the field [7][9]. Group 4: Course Structure and Content - The course spans 12 weeks of online group research followed by 2 weeks of paper guidance, with a focus on empirical validation using public datasets like ETH, UCY, and SDD [12][24]. - Key topics include the introduction of diffusion models, traditional trajectory prediction methods, and advanced techniques for integrating social interaction modeling and conditional control mechanisms [28][29].

Core Viewpoint - The article emphasizes the significant role of diffusion models in enhancing the capabilities of autonomous driving systems, particularly in data diversity, perception robustness, and decision-making under uncertainty [2][3]. Group 1: Applications of Diffusion Models - Diffusion models improve 3D occupancy prediction, outperforming traditional methods, especially in occluded or low-visibility areas, thus aiding downstream planning tasks [5]. - Conditional diffusion models are utilized for precise image translation in driving scenarios, enhancing system understanding of various road environments [5]. - Stable diffusion models efficiently predict vehicle trajectories, significantly boosting the predictive capabilities of autonomous driving systems [5]. - The DiffusionDrive framework innovatively applies diffusion models to multimodal action distribution, addressing uncertainties in driving decisions [5]. Group 2: Data Generation and Quality - Diffusion models effectively tackle the challenges of insufficient diversity and authenticity in natural driving datasets, providing high-quality synthetic data for autonomous driving validation [5]. - Future explorations will include video generation to further enhance data quality, particularly in 3D data annotation [5]. Group 3: Recent Research Developments - The dual-conditioned temporal diffusion model (DcTDM) generates realistic long-duration driving videos, outperforming existing models by over 25% in consistency and frame quality [7]. - LD-Scene integrates large language models with latent diffusion models for user-controllable adversarial scenario generation, achieving state-of-the-art performance in generating high adversariality and diversity [11]. - DualDiff enhances multi-view driving scene generation through a dual-branch conditional diffusion model, achieving state-of-the-art performance in various downstream tasks [14][34]. Group 4: Traffic Simulation and Scenario Generation - DriveGen introduces a novel traffic simulation framework that generates diverse traffic scenarios, supporting customized designs and improving downstream algorithm performance [26]. - Scenario Dreamer utilizes a vectorized latent diffusion model for generating driving simulation environments, demonstrating superior performance in realism and efficiency [28][31]. - AdvDiffuser generates adversarial safety-critical driving scenarios, enhancing transferability across different systems while maintaining high realism and diversity [68]. Group 5: Safety and Robustness - AVD2 enhances understanding of accident scenarios through the generation of accident videos aligned with natural language descriptions, significantly advancing accident analysis and prevention [39]. - Causal Composition Diffusion Model (CCDiff) improves the generation of closed-loop traffic scenarios by incorporating causal structures, demonstrating enhanced realism and user preference alignment [44].

Core Viewpoint - The article discusses the ongoing relevance of trajectory prediction in the context of end-to-end models, highlighting that many companies still utilize layered approaches where trajectory prediction remains a key algorithmic focus. This includes both joint trajectory prediction and target trajectory prediction, which continue to be active research areas with significant output in conferences and journals [1]. Group 1: Trajectory Prediction Research - The article emphasizes the importance of multi-agent trajectory prediction, which aims to forecast future movements based on historical trajectories of multiple interacting entities, crucial for applications in autonomous driving, intelligent monitoring, and robotic navigation [1]. - Traditional methods for trajectory prediction often rely on recurrent neural networks, convolutional networks, or graph neural networks, while generative models like GANs and CVAEs, although capable of simulating multimodal distributions, are noted for their inefficiency [1]. Group 2: Diffusion Models - Diffusion models have emerged as a new class of models that generate complex distributions through a stepwise denoising process, achieving significant breakthroughs in image generation and showing promise in trajectory prediction by enhancing multimodal modeling capabilities [2]. - Specific models such as the Leapfrog Diffusion Model (LED) and Mixed Gaussian Flow (MGF) have demonstrated substantial improvements in accuracy and efficiency, with LED achieving real-time predictions and MGF enhancing diversity in trajectory predictions [2]. Group 3: Course Objectives and Structure - The course aims to provide a systematic understanding of trajectory prediction and diffusion models, helping participants integrate theoretical knowledge with practical coding skills, and develop their own research ideas [6]. - Participants will gain insights into writing and submitting academic papers, with a focus on accumulating a methodology for writing and receiving guidance on revisions and submissions [6]. Group 4: Target Audience and Outcomes - The course is designed for graduate students and professionals in trajectory prediction and autonomous driving, aiming to enhance their resumes and research capabilities [8]. - Expected outcomes include a comprehensive understanding of classic and cutting-edge papers, coding implementations, and the development of a research paper draft [8][9]. Group 5: Course Highlights and Requirements - The course features a "2+1" teaching model with experienced instructors and a structured learning experience, ensuring comprehensive support throughout the research process [16][17]. - Participants are required to have a foundational understanding of deep learning and proficiency in Python and PyTorch, with recommendations for hardware specifications to facilitate learning [10][12].

Core Viewpoint - The article emphasizes the establishment of a comprehensive community focused on autonomous driving, aiming to bridge the gap between academia and industry while providing valuable resources for learning and career opportunities in the field [2][16]. Group 1: Community and Resources - The community has created a closed-loop system covering various fields such as industry, academia, job seeking, and Q&A exchanges, enhancing the learning experience for participants [2][3]. - The platform offers cutting-edge academic content, industry roundtables, open-source code solutions, and timely job information, significantly reducing the time needed for research [3][16]. - Members can access nearly 40 technical routes, including industry applications, VLA benchmarks, and entry-level learning paths, catering to both beginners and advanced researchers [3][16]. Group 2: Learning and Development - The community provides a well-structured learning path for beginners, including foundational knowledge in mathematics, computer vision, deep learning, and programming [10][12]. - For those already engaged in research, valuable industry frameworks and project proposals are available to further their understanding and application of autonomous driving technologies [12][14]. - Continuous job sharing and career opportunities are promoted within the community, fostering a complete ecosystem for autonomous driving [14][16]. Group 3: Technical Focus Areas - The community has compiled extensive resources on various technical aspects of autonomous driving, including perception, simulation, planning, and control [16][17]. - Specific learning routes are available for topics such as end-to-end learning, 3DGS principles, and multi-modal large models, ensuring comprehensive coverage of the field [16][17]. - The platform also features a collection of open-source projects and datasets relevant to autonomous driving, facilitating hands-on experience and practical application [32][34].