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 convergence of autonomous driving technology, indicating a shift from numerous diverse approaches to a more unified model, which raises the technical barriers in the industry [1] Group 1 - The industry is witnessing a trend where previously many directions requiring algorithm engineers are now consolidating into unified models such as one model, VLM, and VLA [1] - The article encourages the establishment of a large community to support individuals in the industry, highlighting the limitations of individual efforts [1] - A new job and industry-related community is being launched to facilitate discussions on industry trends, company developments, product research, and job opportunities [1]

Group 1: Li Auto - Li Auto's i8 features the VLA "driver model," marking a significant advancement in intelligent driving following the previous VLM introduction [1] - The VLA model includes a newly designed spatial encoder that utilizes language models and logical reasoning to provide driving decisions, predicting trajectories of other vehicles and pedestrians through a diffusion model [1] - The inference frame rate of the VLA is approximately 10 Hz, more than tripling the previous VLM's rate of 3 Hz [1] Group 2: XPeng Motors - XPeng G7 officially commenced deliveries on July 7, with a clear timeline for the Ultra version's VLA and VLM software updates [2] - The VLA software OTA update is scheduled for September 2025, with VLM software upgrades following in November 2025, and personalized recommendations by December 2025 [2] - The XPeng G7 Ultra version is equipped with three self-developed Turing AI chips, boasting a total computing power of 2250 TOPS, positioning it as a leader among mass-produced models [2] Group 3: Chery Automobile - Chery plans to introduce the VLA and world model technology into fuel vehicles by 2025 through its Falcon 900 intelligent driving system, aiming to set a new benchmark for "oil-electric intelligence" [3] - The Falcon 900 system utilizes a self-developed VLA model that integrates visual perception, language understanding, and action execution [3] - The model has been trained on 20 million kilometers of real-world data, capable of understanding over 5000 traffic scenarios, achieving a 92% accuracy rate in recognizing non-standard traffic signals in complex urban conditions, a 37% improvement over traditional systems [3] Group 4: Geely Automobile - Geely is actively developing VLA technology, integrating it with world models to create a comprehensive world model system [4] - The Qianli Haohan system features a "dual end-to-end model" design, enabling a multi-modal VLA general scene model and an end-to-end model to back each other up [4] - This system is powered by dual NVIDIA Thor chips, with a total computing power of 1400 TOPS and over 40 perception units capable of detecting objects 0.75 meters in size from 300 meters away [4] Group 5: Yuanrong Qihang - Yuanrong Qihang is also investing in the VLA model, with five models expected to feature it by the third quarter of this year [5] - The company was among the earliest to publicly announce its VLA development in June of last year [5] - The VLA model focuses on defensive driving with four core functions: spatial semantic understanding, recognition of irregular obstacles, comprehension of text-based guide signs, and voice control of the vehicle, which will be gradually released with mass production [5]

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].

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 - 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].

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The report highlights the evolution of advanced driver assistance systems (ADAS) in China, focusing on the expansion of operational design domains (ODD), technological equity, safety concerns, and supportive policies [4][21][23] - It emphasizes the need for algorithm, data, and computing power upgrades to address safety shortcomings in high-level ADAS technologies [23][66] - The report discusses the transition from modular to end-to-end architectures in vehicle algorithms, aiming for human-like driving capabilities [66][68] Summary by Sections 1. Market Background - The expansion of high-level ADAS ODD is noted, with a focus on technological inclusivity and addressing accident anxiety through safety redundancies [4][21] - Policy support is highlighted as crucial for rational promotion of ADAS technologies [4][21] 2. Technology Insights - The report decodes the underlying logic of data, algorithms, and computing power in high-level ADAS [4][28] - It discusses the computing power landscape, noting the shift towards higher TOPS (trillions of operations per second) capabilities in vehicle and cloud computing [42][44] - Data challenges, including collection and positioning technologies, are identified as critical areas for development [4][28] 3. Competitive Analysis - The competitive landscape is analyzed, detailing the tiered structure of companies and their development strategies [29][30] - The report outlines various collaboration models among automotive manufacturers and technology providers, emphasizing the balance between self-research and external sourcing [83] 4. Trend Insights - The report notes the commercialization progress of passenger vehicle L3 systems, indicating a growing market for advanced ADAS [31][32] - It highlights the importance of continuous upgrades and iterations in ADAS functionalities to meet evolving consumer expectations and safety standards [82][83]

Core Viewpoint - The article discusses Li Auto's strategic focus on artificial intelligence (AI) and its evolution from a vehicle-centric AI assistant to a multi-platform intelligent application, emphasizing the importance of AI in future competitiveness [4][5][6]. Group 1: Strategic Meetings and AI Prioritization - Li Auto holds biannual closed-door strategy meetings to discuss future directions, with significant participation from top executives and industry leaders [3]. - Following a strategic meeting, Li Auto adjusted its AI-related business priorities, emphasizing the strategic importance of intelligent driving over other AI applications [4][5]. - The company aims to become a global leader in AI by 2030, with a clear focus on enhancing its AI capabilities and applications [5][6]. Group 2: Development of AI Capabilities - Li Auto has transitioned its AI assistant, "Li Xiang," from a vehicle-only application to a multi-platform tool, including mobile and web applications [7]. - The company has invested in self-developed algorithms, achieving a full switch to in-house technology for its AI functionalities by March 2023 [7][8]. - The introduction of the multi-modal cognitive model, Mind GPT 1.0, marks a significant advancement in Li Auto's AI capabilities [7]. Group 3: Intelligent Driving and Technological Advancements - Li Auto's intelligent driving system, AD Max, was launched to address product shortcomings and enhance competitive positioning in the market [10][11]. - The company has initiated a large-scale recruitment drive for its intelligent driving team, reflecting its commitment to advancing this technology [10]. - The shift towards an "end-to-end" model for intelligent driving aims to streamline processes and improve system performance through better data utilization [10][11]. Group 4: Organizational Changes and AI Integration - Li Auto established an AI Technical Committee to integrate AI capabilities across various business lines, enhancing collaboration and execution [15][16]. - The committee includes leaders from key departments, ensuring that AI is a core focus in strategic decision-making [16][17]. - The company aims to develop a foundational model that serves as a core capability for all AI projects, positioning itself as a leader in the automotive AI landscape [17].

Core Viewpoint - The article discusses the advancements in the field of autonomous driving, particularly focusing on the Diffusion model and its application in generating driving trajectories, highlighting the differences between VLM and VLA systems [1][4]. Group 1: Diffusion Model Explanation - Diffusion is a generative model that learns data distribution through a process of adding noise (Forward Process) and removing noise (Reverse Process), akin to a reverse puzzle [4]. - The model's denoising process involves training a neural network to predict and remove noise, ultimately generating target data [4]. - Diffusion not only generates the vehicle's trajectory but also predicts the trajectories of other vehicles and pedestrians, enhancing decision-making in complex traffic environments [5]. Group 2: VLM and VLA Systems - VLM consists of two systems: System 1 mimics learning to output trajectories without semantic understanding, while System 2 has semantic understanding but only provides suggestions [2]. - VLA is a single system with both fast and slow thinking capabilities, inherently possessing semantic reasoning [2]. - The output of VLA is action tokens that encode the vehicle's driving behavior and surrounding environment, which are then decoded into driving trajectories using the Diffusion model [4][5]. Group 3: TransDiffuser Architecture - TransDiffuser is an end-to-end trajectory generation model that integrates multi-modal perception information to produce high-quality, diverse trajectories [6][7]. - The architecture includes a Scene Encoder for processing multi-modal data and a Denoising Decoder that utilizes the DDPM framework for trajectory generation [7][9]. - The model employs a multi-head cross-attention mechanism to fuse scene and motion features during the denoising process [9]. Group 4: Performance and Innovations - The model achieves a Predictive Driver Model Score (PDMS) of 94.85, outperforming existing methods [11]. - Key innovations include anchor-free trajectory generation and a multi-modal representation decorrelation optimization mechanism to enhance trajectory diversity and reduce redundancy [11][12]. Group 5: Limitations and Future Directions - The authors note challenges in fine-tuning the model, particularly the perception encoder [13]. - Future directions involve integrating reinforcement learning and referencing models like OpenVLA for further advancements [13].