Core Viewpoint - The article emphasizes the evolving landscape of autonomous driving, highlighting the shift from traditional planning and control methods to end-to-end approaches, which are increasingly favored in the industry [2][29]. Summary by Sections Course Offerings - The company has designed a specialized course on end-to-end planning and control in autonomous driving, aimed at addressing real-world challenges and enhancing employability [6][12]. - The course will cover essential algorithms and frameworks used in the industry, focusing on practical applications and integration of traditional and modern methods [6][21]. Course Structure - The course consists of six chapters, each focusing on different aspects of planning and control, including foundational algorithms, decision-making frameworks, and handling uncertainty in environments [20][24][29]. - The course will also include interview preparation, resume enhancement, and mock interviews to support participants in securing job offers [31][10]. Target Audience - The course is designed for individuals with a background in vehicle engineering, automation, computer science, and related fields, particularly those seeking to transition into autonomous driving roles [37][39]. - Participants are expected to have a basic understanding of programming and relevant mathematical concepts to fully benefit from the course [43]. Instructor Expertise - The course will be led by an experienced instructor with a strong background in autonomous driving algorithms and practical implementation, ensuring that participants receive high-quality guidance [34][10]. Additional Benefits - Participants will have access to supplementary resources, including code and development environments, to enhance their learning experience [13][15]. - The course aims to provide a comprehensive understanding of the industry, equipping participants with the skills needed to tackle complex problems in autonomous driving [6][13].

Core Insights - The article discusses the advancements in autonomous driving technology, particularly focusing on the performance of Horizon's HSD system compared to Li Auto's VLA system, highlighting the strengths and weaknesses of both [5][6]. Group 1: Technology Comparison - Horizon's HSD technology architecture utilizes visual information for trajectory output, with laser radar positioning as a safety redundancy, while the VLA system is criticized for its high computational and bandwidth requirements [5]. - During a test drive of the Horizon HSD engineering vehicle, the experience was reported to be significantly better than the current production version of Li Auto's VLA, particularly in terms of comfort and smoothness during traffic conditions [6]. - Feedback from the Horizon team indicated that the HSD system performs well in controlled environments but has limitations in extreme weather and complex scenarios, suggesting a need for further development [7]. Group 2: Community and Collaboration - The article mentions the establishment of nearly a hundred technical discussion groups related to various aspects of autonomous driving, with a community of around 4,000 members and over 300 companies and research institutions involved [8]. - The collaboration between Horizon and vehicle manufacturers is emphasized, with a focus on integrating user interface elements that respect manufacturer preferences, which can impact the overall driving experience [7]. Group 3: Future Outlook - The article suggests that while the HSD system shows promise, it is still in development and may not yet reach full autonomous driving capabilities, estimating it to be around 60% of the level of Li Auto's V13 system [7].

Core Insights - The article highlights the victory of Inspur's AI team in the Autonomous Grand Challenge 2025, where they achieved a score of 53.06 in the end-to-end autonomous driving track using their innovative framework "SimpleVSF" [2][7][13] - The framework integrates bird's-eye view perception trajectory prediction with a vision-language multimodal model, enhancing decision-making capabilities in complex traffic scenarios [2][5][8] Summary by Sections Competition Overview - The ICCV 2025 Autonomous Driving Challenge is a significant international event focusing on autonomous driving and embodied intelligence, featuring three main tracks [4] - The end-to-end driving challenge evaluates trajectory prediction and behavior planning using a data-driven simulation framework, emphasizing safety and efficiency across nine key metrics [4] Technical Challenges - End-to-end autonomous driving aims to reduce errors and information loss from traditional modular approaches, yet struggles with decision-making in complex real-world scenarios [5] - Current methods can identify basic elements but fail to understand higher-level semantics and situational awareness, leading to suboptimal decisions [5] Innovations in SimpleVSF Framework - The SimpleVSF framework bridges the gap between traditional trajectory planning and semantic understanding through a vision-language model (VLM) [7][8] - The VLM-enhanced scoring mechanism improves decision quality and scene adaptability, resulting in a 2% performance increase for single models and up to 6% in fusion decision-making [8][11] Decision-Making Mechanism - The dual fusion decision mechanism combines quantitative and qualitative assessments, ensuring optimal trajectory selection based on both numerical and semantic criteria [10][11] - The framework employs advanced models for generating diverse candidate trajectories and extracting robust environmental features, enhancing overall system performance [13] Achievements and Future Directions - The SimpleVSF framework's success in the challenge sets a new benchmark for end-to-end autonomous driving technology, supporting further advancements in the field [13] - Inspur's AI team aims to leverage their algorithmic and computational strengths to drive innovation in autonomous driving technology [13]

Core Insights - The article emphasizes the establishment of a comprehensive community called "Autonomous Driving Heart Knowledge Planet," aimed at bridging the gap between academia and industry in the field of autonomous driving [1][3][14]. Group 1: Community Development - The community has grown to over 4,000 members and aims to reach nearly 10,000 within two years, providing a platform for technical sharing and communication among beginners and advanced learners [3][14]. - The community offers various resources, including videos, articles, learning paths, Q&A sessions, and job exchange opportunities, making it a holistic hub for autonomous driving enthusiasts [1][3][5]. Group 2: Learning Resources - The community has compiled over 40 technical learning paths, covering topics such as end-to-end learning, multi-modal large models, and data annotation practices, significantly reducing the time needed for research [5][14]. - Members can access a variety of video tutorials and courses tailored for beginners, covering essential topics in autonomous driving technology [9][15]. Group 3: Industry Engagement - The community collaborates with numerous industry leaders and academic experts to discuss trends, technological advancements, and production challenges in autonomous driving [6][10][14]. - There is a mechanism for job referrals within the community, facilitating connections between members and leading companies in the autonomous driving sector [10][12]. Group 4: Technical Focus Areas - The community has organized resources on various technical areas, including 3D object detection, multi-sensor fusion, and high-precision mapping, which are crucial for the development of autonomous driving technologies [27][29][31]. - Specific focus is given to emerging technologies such as visual language models (VLM) and world models, with detailed summaries and resources available for members [37][39][45].

Core Insights - Tesla has unveiled a world simulator for generating realistic driving scenarios, which was presented by Ashok Elluswamy at the ICCV conference, emphasizing the future of intelligent driving lies in end-to-end AI [1][5][24] Group 1: World Simulator Features - The world simulator can create new challenging scenarios for autonomous driving tasks, such as vehicles suddenly changing lanes or AI navigating around pedestrians and obstacles [2] - The generated scenario videos serve dual purposes: training autonomous driving models and providing a gaming experience for human users [2][4] Group 2: End-to-End AI Approach - Elluswamy highlighted that end-to-end AI is the future of autonomous driving, utilizing data from various sensors to generate control commands for vehicles [5][8] - The end-to-end approach is contrasted with modular systems, which are easier to develop initially but lack the optimization and scalability of end-to-end systems [8][10] Group 3: Challenges and Solutions - One major challenge for end-to-end autonomous driving is evaluation, which the world simulator addresses by using a vast dataset to synthesize future states based on current conditions [11] - The complexity of real-world data, such as high frame rates and multiple sensor inputs, leads to a "curse of dimensionality," which Tesla mitigates by collecting extensive driving data to enhance model generalization [13][15] Group 4: Industry Perspectives - The industry is divided between two main approaches to end-to-end autonomous driving: VLA (Vision-Language-Action) and world models, with various companies adopting different strategies [24] - Tesla's choice of the end-to-end approach has garnered attention due to its historical success in the autonomous driving space, raising questions about the future direction of the technology [24]

Core Viewpoint - Tesla has unveiled a world simulator for autonomous driving, showcasing its potential to generate realistic driving scenarios and enhance the training of AI models for self-driving technology [1][4][12]. Group 1: World Simulator Features - The simulator can create new challenging scenarios for autonomous driving tasks, such as unexpected lane changes by other vehicles [4][5]. - It allows AI to perform driving tasks in existing scenarios, avoiding pedestrians and obstacles [7][9]. - The generated scenario videos can also serve as a gaming experience for human users [9]. Group 2: End-to-End AI Approach - Tesla's VP Ashok Elluswamy emphasized that end-to-end AI is the future of autonomous driving, applicable not only to driving but also to other intelligent scenarios like the Tesla Optimus robot [12][13][14]. - The end-to-end neural network utilizes data from various sensors to generate control commands for the vehicle, contrasting with modular systems that are easier to develop initially but less effective in the long run [17]. - The end-to-end approach allows for better optimization and handling of complex driving situations, such as navigating around obstacles [18][21]. Group 3: Challenges and Solutions - One major challenge for end-to-end autonomous driving is evaluation, which Tesla addresses with its world simulator that trains on a vast dataset [22][24]. - The simulator can also facilitate large-scale reinforcement learning, potentially surpassing human performance [24]. - Other challenges include the "curse of dimensionality," interpretability, and safety guarantees, which require processing vast amounts of data [26][27][28]. Group 4: Data Utilization - Tesla collects data equivalent to 500 years of driving every day, using a complex data engine to filter high-quality samples for training [29][30]. - This extensive data collection enhances the model's generalization capabilities to handle extreme situations [30]. Group 5: Technical Approaches in the Industry - The industry is divided between two main approaches: VLA (Vision-Language Architecture) and world models, with companies like Huawei and NIO representing the latter [38][39]. - VLA proponents argue it leverages existing internet data for better understanding, while world model advocates believe it addresses the core issues of autonomous driving [41][42]. - Tesla's approach is closely watched due to its historical success in selecting effective strategies in autonomous driving development [43][44].

Core Viewpoint - 2023 marks the year of end-to-end production, with 2024 expected to be a significant year for end-to-end production in the automotive industry, as leading new forces and manufacturers have already achieved end-to-end production [1][3]. Group 1: End-to-End Production Development - The automotive industry is witnessing rapid development in end-to-end methods, particularly the one-stage approach exemplified by UniAD, which directly models vehicle trajectories from sensor inputs [1][3]. - There are two main paradigms in the industry: one-stage and two-stage methods, with the one-stage approach gaining traction and leading to various derivatives based on perception, world models, diffusion models, and VLA [3][5]. Group 2: Course Overview - A course titled "End-to-End and VLA Autonomous Driving" has been launched, focusing on cutting-edge algorithms in both one-stage and two-stage end-to-end methods, aimed at bridging academic and industrial advancements [5][15]. - The course is structured into several chapters, covering the history and evolution of end-to-end methods, background knowledge on VLA, and detailed discussions on both one-stage and two-stage approaches [9][10][12]. Group 3: Key Technologies - The course emphasizes critical technologies such as BEV perception, visual language models (VLM), diffusion models, and reinforcement learning, which are essential for mastering the latest advancements in autonomous driving [5][11][19]. - The second chapter of the course is highlighted as containing the most frequently asked technical keywords for job interviews in the next two years [10]. Group 4: Practical Applications - The course includes practical assignments, such as RLHF fine-tuning, allowing participants to apply their knowledge in real-world scenarios and understand how to build and experiment with pre-trained and reinforcement learning modules [13][19]. - The curriculum also covers various subfields of one-stage end-to-end methods, including those based on perception, world models, diffusion models, and VLA, providing a comprehensive understanding of the current landscape in autonomous driving technology [14][19].

Core Insights - Tesla has shared insights into its architecture, emphasizing the use of a large model and extensive data, which allows for a fixed computation time and high-frequency actions in its Full Self-Driving (FSD) system [5][6]. Group 1: Reasons for End-to-End Approach - The complexity of human driving behavior makes it difficult to define a single evaluation function, leading to challenges in rule-based optimization [8]. - The interface definition between perception, prediction, and planning is problematic, resulting in information loss [8]. - An end-to-end approach is better suited for scalability and addressing long-tail problems [8]. - Fixed computation time based on neural networks reduces latency compared to traditional methods [8]. - Philosophically, reliance on computational power and data is preferred over human experience [8]. Group 2: Challenges of End-to-End Systems - The three main challenges faced by end-to-end systems include evaluation, the curse of dimensionality, and ensuring interpretability and safety [19][20]. - The curse of dimensionality leads to insufficient supervisory signals when transitioning from high-dimensional to low-dimensional spaces [21]. - Ensuring interpretability and safety is crucial, as the model must genuinely understand driving behavior rather than just fitting shortcuts [23]. Group 3: Evaluation Challenges - High-quality datasets cannot solely describe performance through loss metrics, indicating a need for more comprehensive evaluation methods [39]. - Open-loop evaluations cannot replace closed-loop assessments, highlighting the necessity for real-world testing [39]. - Driving behavior is multimodal, requiring evaluation metrics that encompass various driving actions [39]. - One proposed method involves predicting the consequences of actions, potentially using a critic to assess model performance [39]. - Balancing the evaluation dataset is essential for accurate assessments [39]. Group 4: World Model Simulator - Tesla introduced a world model simulator that generates subsequent videos based on real scenarios, indicating a high barrier to entry for this technology [41]. - The simulator allows for replaying previous issues to assess improvements, akin to two-stage simulations [44]. - This technology can also be applied to humanoid robots, enabling reinforcement training and simulation [46].

Core Insights - Tesla's FSD V14 series has shown rapid evolution with four updates in two weeks, indicating a new phase of accelerated development in autonomous driving technology [4][5] - The transition to an end-to-end architecture from version 12 has sparked industry interest in similar technologies, emphasizing the importance of a unified neural network model for driving control [7][9] Technical Advancements - The end-to-end system reduces intermediate processing steps, allowing for seamless gradient backpropagation from output to perception, enhancing overall model optimization [7] - Ashok highlighted the complexity of encoding human value judgments in autonomous driving scenarios, showcasing the system's ability to learn from human driving data to make nuanced decisions [9] - Traditional modular systems face challenges in defining interfaces for perception and decision-making, while end-to-end models minimize information loss and improve decision-making in rare scenarios [11][13] Data Utilization - Tesla's data engine collects vast amounts of driving data, generating the equivalent of 500 years of driving data daily, which is crucial for training the FSD model [18][19] - The company employs complex mechanisms to gather data from rare scenarios, ensuring the model can generalize effectively [19] Model Structure and Challenges - The ideal end-to-end model structure involves high-dimensional input data (e.g., 7 channels of 5 million pixel camera video) mapped to low-dimensional output signals, presenting significant training challenges [16] - The end-to-end system's architecture is designed to ensure interpretability and safety, avoiding the pitfalls of being a "black box" [20][22] Evaluation Framework - A robust evaluation framework is essential for end-to-end systems, focusing on closed-loop performance and the ability to assess diverse driving behaviors [32][34] - Tesla's closed-loop simulation system plays a critical role in validating the correctness of the end-to-end policy and generating adversarial samples for model testing [36][38] Future Implications - The integration of Tesla's simulation capabilities into robotics suggests potential advancements in embodied AI, enhancing the versatility of AI applications across different domains [40][42]

Core Viewpoint - The article emphasizes the establishment of a comprehensive community called "Autonomous Driving Heart Knowledge Planet," aimed at providing a platform for knowledge sharing and networking in the autonomous driving industry, addressing the challenges faced by newcomers in the field [1][3][14]. Group 1: Community Development - The community has grown to over 4,000 members and aims to reach nearly 10,000 within two years, providing a space for technical sharing and communication among beginners and advanced learners [3][14]. - The community integrates various resources including videos, articles, learning paths, Q&A, and job exchange, making it a comprehensive hub for autonomous driving enthusiasts [3][5]. Group 2: Learning Resources - The community has organized over 40 technical learning paths, covering topics such as end-to-end autonomous driving, multi-modal large models, and data annotation practices, significantly reducing the time needed for research [5][14]. - Members can access a variety of video tutorials and courses tailored for beginners, covering essential topics in autonomous driving technology [9][15]. Group 3: Industry Insights - The community regularly invites industry experts to discuss trends, technological advancements, and production challenges in autonomous driving, fostering a serious content-driven environment [6][14]. - Members are encouraged to engage with industry leaders for insights on job opportunities and career development within the autonomous driving sector [10][18]. Group 4: Networking Opportunities - The community facilitates connections between members and various autonomous driving companies, offering resume forwarding services to help members secure job placements [10][12]. - Members can freely ask questions regarding career choices and research directions, receiving guidance from experienced professionals in the field [87][89].