Core Viewpoint - The article introduces a new course titled "World Models and Autonomous Driving Small Class," focusing on advanced algorithms in the field of autonomous driving, including general world models, video generation, and OCC generation [1][3]. Course Overview - The course is developed in collaboration with industry leaders and follows the success of a previous course on end-to-end and VLA autonomous driving [1]. - The course aims to enhance understanding and practical skills in world models, which are crucial for the advancement of autonomous driving technology [11]. Course Structure Chapter 1: Introduction to World Models - This chapter covers the relationship between world models and end-to-end autonomous driving, the history of world models, and current application cases [6]. - It discusses various types of world models, including pure simulation, simulation plus planning, and generating sensor inputs and perception results [6]. Chapter 2: Background Knowledge of World Models - The second chapter focuses on foundational knowledge related to world models, including scene representation, Transformer technology, and BEV perception [6][12]. - It highlights key technical terms frequently encountered in job interviews related to world models [7]. Chapter 3: Discussion on General World Models - This chapter addresses popular general world models and recent trends in autonomous driving jobs, including models from Li Feifei's team and DeepMind [7]. - It provides insights into the core technologies and design philosophies behind these models [7]. Chapter 4: Video Generation-Based World Models - The fourth chapter focuses on video generation algorithms, showcasing significant works such as GAIA-1 & GAIA-2 and recent advancements from various institutions [8]. - It includes practical applications using open-source projects like OpenDWM [8]. Chapter 5: OCC-Based World Models - This chapter explores OCC generation algorithms, discussing three major papers and a practical project that extends to vehicle trajectory planning [9]. Chapter 6: World Model Job Topics - The final chapter shares practical experiences from the instructor's career, addressing industry applications, pain points, and interview preparation for related positions [10]. Target Audience and Learning Outcomes - The course is designed for individuals aiming to deepen their understanding of end-to-end autonomous driving and world models [11]. - Upon completion, participants are expected to achieve a level equivalent to one year of experience as a world model autonomous driving algorithm engineer, mastering key technologies and being able to apply learned concepts in projects [14].

Core Insights - The article discusses the evolving recruitment landscape in the autonomous driving sector, highlighting a shift in demand from perception roles to end-to-end, VLA, and world model positions [2] - A new advanced course focused on end-to-end production in autonomous driving has been designed, emphasizing practical applications and real-world experience [2][4] Course Overview - The course is structured to cover various core algorithms, including one-stage and two-stage end-to-end methods, navigation information applications, reinforcement learning, and trajectory optimization [2] - The course aims to provide in-depth knowledge and practical skills necessary for production in autonomous driving, with a focus on real-world applications and challenges [2][4] Chapter Summaries - **Chapter 1: Overview of End-to-End Tasks** Discusses the integration of perception tasks and the learning-based design of control algorithms, which are essential skills for companies in the end-to-end era [7] - **Chapter 2: Two-Stage End-to-End Algorithm Framework** Introduces the modeling methods of two-stage frameworks and the information transfer between perception and planning, including practical examples [8] - **Chapter 3: One-Stage End-to-End Algorithm** Focuses on one-stage frameworks that allow for lossless information transfer, presenting various methods and practical learning experiences [9] - **Chapter 4: Production Application of Navigation Information** Covers the critical role of navigation information in autonomous driving, detailing mainstream navigation map formats and their integration into models [10] - **Chapter 5: Introduction to RL Algorithms in Autonomous Driving** Explains the necessity of reinforcement learning in conjunction with imitation learning to enhance the model's ability to generalize [11] - **Chapter 6: Trajectory Output Optimization** Engages participants in practical projects focusing on algorithms based on imitation learning and reinforcement learning [12] - **Chapter 7: Safety Net Solutions - Spatiotemporal Joint Planning** Discusses post-processing logic to ensure model accuracy and stability in trajectory outputs, introducing common smoothing algorithms [13] - **Chapter 8: Experience Sharing on End-to-End Production** Provides insights on practical experiences in production, addressing data, models, scenarios, and strategies for system capability enhancement [14] Target Audience - The course is aimed at advanced learners with a foundational understanding of autonomous driving algorithms, reinforcement learning, and programming skills [15][17]

Core Insights - The article discusses the evolving recruitment landscape in the autonomous driving industry, highlighting a shift in demand from perception roles to end-to-end, VLA, and world model positions [2] - A new course titled "End-to-End Practical Class for Mass Production" has been designed to address the skills gap in the industry, focusing on practical applications and mass production experiences [2][4] Course Overview - The course aims to cover core algorithms such as one-stage and two-stage end-to-end methods, navigation information applications, reinforcement learning, and trajectory optimization [2] - It is structured into eight chapters, each focusing on different aspects of end-to-end autonomous driving systems, including task overview, algorithm frameworks, navigation applications, and production experiences [5][7][8][9][10][11][12][13][14] Target Audience - The course is designed for advanced learners with a background in autonomous driving perception, reinforcement learning, and programming languages like Python and PyTorch [15][16] - It emphasizes practical skills and aims to prepare participants for real-world applications in the autonomous driving sector [2][15] Course Schedule - The course will commence on November 30, with a duration of approximately three months, featuring offline video lectures and online Q&A sessions [15][17]

Investment Rating - The report maintains an "Outperform" rating for the industry [1] Core Insights - The development of intelligent driving is an inevitable trend supported by national strategies and policies, leading to multi-dimensional improvements in society and industry [2] - Tesla and Huawei are leading the breakthrough in L4 autonomous driving with their end-to-end algorithms, significantly enhancing performance and capabilities [2] - The Robotaxi global market is projected to reach nearly 10 trillion, with ongoing commercialization efforts [2] Summary by Sections 01 Intelligent Driving Regulations: Gradual Policy Implementation - Domestic and international policies are progressively supporting the automation of driving applications, with various local governments exploring intelligent driving scenarios [6][7] 02 High-End Intelligent Driving: Tesla and Huawei's End-to-End Technology - Tesla's FSD V12 and Huawei's ADS 3.0 are leading advancements in L4 capabilities, with significant improvements in algorithm performance and urban coverage [2][20] 03 Intelligent Driving Equality: 2025 Penetration Rate Inflection Point - The penetration rates for highway NOA are expected to grow from 11.3% in 2024 to 39.0% in 2025, while urban NOA is projected to increase from 6.1% to 9.6% [41] - The high-end intelligent driving market is anticipated to reach 23,866 billion by 2025, doubling from 2024 due to increased penetration and market expansion [41] 04 Industry Chain and Component Manufacturer Analysis - BYD's "Tian Shen Zhi Yan" system is set to penetrate the mid-range market, with plans to offer intelligent driving features in vehicles priced below 100,000 [25][29] 05 Robotaxi: The Best Commercialization Scenario for Intelligent Driving - Companies like Waymo and Apollo are leading in the Robotaxi sector, with PONY AI achieving operational cost balance and WeRide aiming for a fleet of 100,000 by 2030 [2]

Core Viewpoint - The article discusses the evolving recruitment demands in the autonomous driving sector, highlighting a shift from perception roles to end-to-end, VLA, and world model positions, indicating a broader technical skill requirement for candidates [1][2]. Group 1: Course Overview - The course titled "End-to-End Practical Class for Mass Production" focuses on practical applications in autonomous driving, covering various algorithms and real-world production experiences [2][3]. - The course is designed for a limited number of participants, with only 25 spots available, emphasizing a targeted approach to training [2][3]. Group 2: Course Structure - Chapter 1 introduces the overview of end-to-end tasks, discussing the integration of perception tasks and the learning-based control algorithms that are becoming mainstream [6]. - Chapter 2 covers the two-stage end-to-end algorithm framework, explaining the modeling methods and the information transfer between perception and planning [7]. - Chapter 3 focuses on the one-stage end-to-end algorithm framework, highlighting its advantages in information transmission and introducing various one-stage framework solutions [8]. - Chapter 4 discusses the application of navigation information in autonomous driving, detailing the formats and encoding methods of navigation maps [9]. - Chapter 5 introduces reinforcement learning algorithms, emphasizing the need for these methods to complement imitation learning in autonomous driving [10]. - Chapter 6 involves practical projects on trajectory output optimization, combining imitation learning and reinforcement learning techniques [11]. - Chapter 7 presents fallback solutions through spatiotemporal planning, focusing on trajectory smoothing algorithms to enhance output reliability [12]. - Chapter 8 shares mass production experiences, analyzing how to effectively use tools and strategies to improve system capabilities [13]. Group 3: Target Audience and Requirements - The course is aimed at advanced learners with a foundational understanding of autonomous driving algorithms, though those with weaker backgrounds can still participate [14][15]. - Participants are required to have access to a GPU with recommended specifications and familiarity with various algorithms and programming languages [15].

Core Viewpoint - The article emphasizes the importance of end-to-end production in the automotive industry, highlighting the scarcity of qualified talent and the need for comprehensive training programs to address various challenges in this field [1][3]. Group 1: Course Overview - The course is designed to cover essential algorithms related to end-to-end production, including one-stage and two-stage frameworks, reinforcement learning applications, and trajectory optimization [3][9]. - It aims to provide practical experience and insights into production challenges, focusing on real-world applications and expert guidance [3][6]. Group 2: Course Structure - The course consists of eight chapters, each addressing different aspects of end-to-end production, such as task overview, algorithm frameworks, navigation information applications, and trajectory output optimization [9][10][11][12][13][14][15][16]. - The final chapter will share production experiences from various perspectives, including data, models, and strategies for system enhancement [16]. Group 3: Target Audience and Requirements - The course is aimed at advanced learners with a background in autonomous driving, reinforcement learning, and programming, although those with weaker foundations can still participate [17][18]. - Participants are required to have access to a GPU with recommended specifications and familiarity with relevant algorithms and programming languages [18].

Core Insights - The article emphasizes the importance of end-to-end production in the automotive industry, highlighting the scarcity of qualified talent in this area [1][3] - A newly designed advanced course on end-to-end production has been developed to address the industry's needs, focusing on practical applications and real-world scenarios [3][5] Course Overview - The course covers essential algorithms such as one-stage and two-stage end-to-end frameworks, reinforcement learning applications, and trajectory optimization techniques [5][10] - It aims to provide hands-on experience and insights into production challenges, making it suitable for individuals looking to advance or transition in their careers [5][18] Course Structure - Chapter 1 introduces the overview of end-to-end tasks, focusing on the integration of perception and control algorithms [10] - Chapter 2 discusses the two-stage end-to-end algorithm framework, including its modeling and information transfer methods [11] - Chapter 3 covers the one-stage end-to-end algorithm framework, emphasizing its advantages in information transmission [12] - Chapter 4 focuses on the application of navigation information in autonomous driving, detailing map formats and encoding methods [13] - Chapter 5 introduces reinforcement learning algorithms, highlighting their necessity alongside imitation learning [14] - Chapter 6 provides practical experience in trajectory output optimization, combining imitation and reinforcement learning [15] - Chapter 7 discusses fallback strategies for trajectory smoothing and reliability in production [16] - Chapter 8 shares production experiences from various perspectives, including data and model optimization [17] Target Audience - The course is designed for advanced learners with a foundational understanding of autonomous driving algorithms, reinforcement learning, and programming skills [18][19] Course Logistics - The course starts on November 30 and spans three months, featuring offline video lectures and online Q&A sessions [20]

Summary of Key Points from the Conference Call Industry Overview - The conference call discusses the autonomous driving (AD) industry, focusing on various companies and their technological advancements in the sector. Key Companies and Market Share - **Momenta** holds a leading position in the third-party autonomous driving market with a market share of 55%, while **Huawei** has a 25% share [1][3]. - **DJI** excels in low-computing power chip solutions but is shifting towards mid-to-high computing power solutions due to market demand [1][5]. - **Horizon Robotics** has developed self-researched hardware-software integrated solutions, currently in mass production with Chery's models, but faces challenges in NPU computing power and algorithm upgrades [1][6]. Technological Routes and Developments - The AD industry is divided into three main technological routes: 1. **End-to-End Algorithms**: Gaining traction since Tesla's AI Day in 2021, with companies like Momenta and Tesla implementing these algorithms in production vehicles [2]. 2. **Vision Language Action (VLA) Models**: Used by companies like Li Auto and XPeng, requiring high computing power (minimum 500 TOPS) and significant resources for training [2]. 3. **World Models**: Developed by companies like Huawei and Momenta, capable of understanding and predicting environmental changes [2]. Performance and Capabilities of Key Players - **Momenta** offers two product lines: a cost-effective single Orin X solution and a high-end dual Orin X solution, showcasing strong engineering capabilities [3]. - **DJI** has strong engineering capabilities but relatively weaker algorithm capabilities, allowing it to effectively implement complex algorithms in practical scenarios [3]. - **Horizon Robotics** is in the second tier of the industry, with its HSD and G6P series solutions providing decent user experience but needing more vehicle validation [6]. Market Trends and Shifts - The market is shifting from low-computing power chips to mid-to-high computing power solutions, prompting companies like DJI to develop new chip solutions [4][5]. - The demand for **fusion perception** routes combining Lidar and other sensors is expected to grow due to regulatory requirements and the need for handling complex scenarios [12]. Challenges and Future Outlook - The differences in autonomous driving capabilities among companies are primarily determined by data, computing power, and algorithms [8][9]. - Long-term, the accumulation of data will be crucial for competitive advantage, with a critical mass of road testing data needed to trigger significant improvements [10]. - The **Robot Taxi** market is seen as a positive growth area, with profitability dependent on vehicle efficiency, cost management, and competitive pricing [18][19]. Conclusion - Companies transitioning from L2+ to L4 levels of autonomous driving have a natural advantage due to lower resource investment and existing experience in mass production [20].

Group 1 - The article discusses the importance of the autumn recruitment season, highlighting a student's experience of receiving an offer from a tier 1 company but feeling unfulfilled due to a desire to transition to a more advanced algorithm position [1] - The article encourages perseverance and self-challenge, emphasizing that pushing oneself can reveal personal limits and potential [2] Group 2 - A significant learning package is introduced, including a 299 yuan discount card for a year of courses at a 30% discount, various course benefits, and hardware discounts [4][6] - The focus is on cutting-edge autonomous driving technologies for 2025, particularly end-to-end (E2E) and VLA autonomous driving systems, which are becoming central to the industry [7][8] Group 3 - The article outlines the development of end-to-end autonomous driving algorithms, emphasizing the need for knowledge in multimodal large models, BEV perception, reinforcement learning, and more [8] - It highlights the challenges faced by beginners in synthesizing knowledge from fragmented research papers and the lack of practical guidance in transitioning from theory to practice [8] Group 4 - The introduction of a new course on automated 4D annotation algorithms is aimed at addressing the increasing complexity of training data requirements for autonomous driving systems [11][12] - The course is designed to help students navigate the challenges of data annotation and improve the efficiency of data loops in autonomous driving [12] Group 5 - The article discusses the emergence of multimodal large models in autonomous driving, noting the rapid growth of job opportunities in this area and the need for a structured learning platform [14] - It emphasizes the importance of practical experience and project involvement for job seekers in the autonomous driving sector [21] Group 6 - The article mentions various specialized courses available, including those focused on perception, model deployment, planning control, and simulation in autonomous driving [16][18][20] - It highlights the importance of community engagement and support through dedicated VIP groups for course participants [26]

Core Viewpoint - The article emphasizes the importance of active safety technology in smart electric vehicles, highlighting Xiaopeng's advancements in this area to regain a competitive edge in the market [2][3][14]. Group 1: Active Safety Technology Developments - Xiaopeng has demonstrated its AEB (Automatic Emergency Braking) capabilities at speeds of up to 130 km/h in various challenging conditions, including night and wet roads [2][4]. - The company has redefined its active safety architecture and software, with daily updates to enhance performance and address market competition [2][3]. - Xiaopeng's AEB system is designed to operate effectively in a full speed range of 0-150 km/h, with a focus on real-world scenarios [4][5]. Group 2: Technical Innovations - Xiaopeng employs a "two-stage braking" strategy to enhance user comfort during emergency braking, initially applying a moderate deceleration before engaging full braking if necessary [5][6]. - The introduction of the AES (Automatic Emergency Steering) system allows vehicles to navigate around obstacles on slippery surfaces, utilizing a unique "single-side braking" technique [8][9]. - The company aims to tackle complex driving conditions, such as icy roads, to ensure stability and effective obstacle avoidance [9][10]. Group 3: Strategic Focus and Team Dynamics - Xiaopeng has established dedicated teams and "war rooms" to enhance collaboration and expedite the development of active safety features [15][16]. - The company has shifted its focus to prioritize active safety, responding to consumer demand for improved vehicle safety [14][18]. - The development process has been streamlined to ensure rapid iteration and effective communication among team members [16][17]. Group 4: Market Position and Future Goals - Xiaopeng's advancements in active safety are part of a broader strategy to maintain its leadership in the smart driving sector amid increasing competition [14][18]. - The ultimate goal of the active safety technology is to achieve "zero collisions" by expanding the coverage of AEB and AES systems [22][23]. - Future efforts will focus on enhancing scene coverage based on real-world collision data to prioritize high-frequency and high-severity scenarios [23][24].