理想VLA司机大模型已经上车了！从发布会上看，VLA 能力的提升集中体现在三点：更懂语义 （多模态输入）、更擅长推理（思维链）、更接近人类驾驶直觉（轨迹规划）。并且具备四个核 心能力：空间理解能力、思维能力、沟通与记忆能力以及行为能力。 其中思维能力、沟通与记忆能力是语言模型赋予的能力，其中记忆能力还用到了RAG。下面是理 想VLA司机大模型思维链输出的demo：结合了动态目标、静态元素、导航地图、空间理解等等元 素。毫无疑问，VLA已经是自动驾驶学术界和工业界最为关注的方向。 而VLA是从VLM+E2E一路发展过来的，涵盖了端到端、轨迹预测、视觉语言模型、强化学习等多 个前沿技术栈。。而传统的BEV感知、车道线、Occupancy等工作相对较少出现在顶会了，最近也 有很多同学陆续来咨询柱哥，传统的感知、规划这块还能继续发论文吗？感觉工作都已经被做的 七七八八了，审稿人会打高分吗？ 说到传统的感知、规划等任务，工业界都还在继续优化方案！但学术界基本都慢慢转向大模型与 VLA了，这个领域还有很多工作可以做的子领域... 之前我们已经开展了第一期VLA论文指导班，反响很不错，很多同学联系我们第二期什么时候开 班， ...

理想VLA司机大模型已经上车了！从发布会上看，VLA 能力的提升集中体现在三点：更懂语义 （多模态输入）、更擅长推理（思维链）、更接近人类驾驶直觉（轨迹规划）。发布会上展示了 四个核心能力：空间理解能力、思维能力、沟通与记忆能力以及行为能力。 ⼀、VLA科研论文辅导课题来啦⭐ 其中思维能力、沟通与记忆能力是语言模型赋予的能力，其中记忆能力还用到了RAG。下面是理 想VLA司机大模型思维链输出的demo：结合了动态目标、静态元素、导航地图、空间理解等等元 素。毫无疑问，VLA已经是自动驾驶学术界和工业界最为关注的方向。 而VLA是从VLM+E2E一路发展过来的，涵盖了端到端、轨迹预测、视觉语言模型、强化学习等多 个前沿技术栈。。而传统的BEV感知、车道线、Occupancy等工作相对较少出现在顶会了，最近也 有很多同学陆续来咨询柱哥，传统的感知、规划这块还能继续发论文吗？感觉工作都已经被做的 七七八八了，审稿人会打高分吗？ 说到传统的感知、规划等任务，工业界都还在继续优化方案！但学术界基本都慢慢转向大模型与 VLA了，这个领域还有很多工作可以做的子领域... 之前我们已经开展了第一期VLA论文指导班，反响很不错 ...

Core Viewpoint - The article discusses the advancements in the VLA (Vision-Language Action) driver model by Li Auto, highlighting its four core capabilities: spatial understanding, reasoning, communication and memory, and behavioral capabilities. It emphasizes the significance of VLA in the field of autonomous driving, indicating a shift in focus from traditional perception and planning tasks to large models and VLA technologies [2][4]. Summary by Sections VLA Model Capabilities - The VLA model integrates dynamic targets, static elements, navigation maps, and spatial understanding, showcasing a more human-like reasoning ability. This positions VLA as a leading focus in both academia and industry for autonomous driving [2]. Shift in Research Focus - Traditional perception and planning tasks are becoming less prominent in top conferences, with academia increasingly shifting towards large models and VLA. Despite this, the industry continues to optimize traditional methods, indicating ongoing opportunities in both areas [4]. Educational Program - An educational program is introduced to help students systematically grasp key theoretical knowledge in VLA, enhance practical coding skills, and develop their own research ideas. The program includes a structured 12-week online group research course followed by 2 weeks of paper guidance and a 10-week maintenance period [5][34]. Course Structure - The course spans 14 weeks, covering topics from introductory lessons to advanced VLA models and paper writing methodologies. Each week focuses on different aspects of VLA and autonomous driving, culminating in a final project report and submission guidance [8][10][35]. Target Audience - The program is designed for master's and doctoral students in VLA and autonomous driving, individuals seeking to enhance their resumes for further studies abroad, and professionals in the AI and autonomous driving sectors looking to deepen their algorithmic knowledge [14][24]. Course Requirements - Participants are expected to have a foundational understanding of deep learning, basic programming skills in Python, and familiarity with PyTorch. Access to high-performance computing resources is recommended for optimal learning [20][21]. Course Highlights - The program features a "2+1" teaching model with experienced instructors, ensuring comprehensive support throughout the learning process. It emphasizes academic integrity and provides a structured evaluation system to enhance the learning experience [22][23].

Core Viewpoint - The article discusses the challenges and advancements in the deployment of Visual-Language-Action (VLA) models in autonomous driving, emphasizing the integration of 3D spatial understanding with global semantic comprehension. Group 1: Challenges in VLA Deployment - The difficulties in deploying VLA models include multi-modal alignment, data training, and single-chip deployment, but advancements in new chip technologies may alleviate these challenges [2][3][5]. - The alignment issue between Visual-Language Models (VLM) and VLA is gradually being resolved with the release of advanced models like GPT-5, indicating that the alignment is not insurmountable [2][3]. Group 2: Technical Innovations - The VLA model incorporates a unique architecture that combines 3D local spatial understanding with 2D global comprehension, enhancing its ability to interpret complex environments [3][7]. - The integration of diffusion models into VLA is a significant innovation, allowing for improved trajectory generation and decision-making processes [5][6]. Group 3: Comparison with Competitors - The gradual transition from Level 2 (L2) to Level 4 (L4) autonomous driving is highlighted as a strategic approach, contrasting with competitors who may focus solely on L4 from the outset [9][10]. - The article draws parallels between the strategies of different companies in the autonomous driving space, particularly comparing the approaches of Tesla and Waymo [9][10]. Group 4: Future Developments - Future iterations of the VLA model are expected to scale in size and performance, with potential increases in parameters from 4 billion to 10 billion, while maintaining efficiency in deployment [16][18]. - The company is focused on enhancing the model's reasoning capabilities through reinforcement learning, which will play a crucial role in its development [13][51]. Group 5: User Experience and Functionality - The article emphasizes the importance of user experience, particularly in features like voice control and memory functions, which are essential for a seamless interaction between users and autonomous vehicles [18][25]. - The need for a robust understanding of various driving scenarios, including complex urban environments and highway conditions, is crucial for the model's success [22][23]. Group 6: Data and Training - The transition from VLM to VLA necessitates a complete overhaul of data labeling processes, as the requirements for training data have evolved significantly [32][34]. - The use of synthetic data is acknowledged, but the majority of the training data is derived from real-world scenarios to ensure the model's effectiveness [54]. Group 7: Regulatory Considerations - The company is actively engaging with regulatory bodies to ensure that its capabilities align with legal requirements, indicating a proactive approach to compliance [35][36]. - The relationship between technological advancements and regulatory frameworks is highlighted as a critical factor in the deployment of autonomous driving technologies [35][36].

Core Viewpoint - The article discusses the advancements of the Li Auto VLA driver model, highlighting its enhanced capabilities in understanding semantics, reasoning, and trajectory planning, which are crucial for autonomous driving [1][3]. Summary by Sections VLA Model Capabilities - The VLA model has improved in three main areas: better semantic understanding through multimodal input, enhanced reasoning abilities via thinking chains, and closer alignment with human driving intuition through trajectory planning [1]. - Four core capabilities of the VLA model are showcased: spatial understanding, reasoning, communication and memory, and behavioral capabilities [1][3]. Development and Research Trends - The VLA model has evolved from VLM+E2E, incorporating various cutting-edge technologies such as end-to-end learning, trajectory prediction, visual language models, and reinforcement learning [5]. - While traditional perception and planning tasks are still being optimized in the industry, the academic community is increasingly shifting focus towards large models and VLA, indicating a wealth of subfields still open for research [5]. VLA Research Guidance Program - A VLA research paper guidance program has been initiated, receiving positive feedback, with many students eager for a second session. The program aims to help participants systematically grasp key theoretical knowledge and develop their own research ideas [6]. - The program includes a structured curriculum over 14 weeks, covering topics from traditional end-to-end autonomous driving to writing methodologies for research papers [9][11]. Enrollment and Course Structure - The program is limited to 6-8 participants per session, targeting students at various academic levels interested in VLA and autonomous driving [12]. - Participants will gain insights into classic and cutting-edge papers, coding implementations, and methods for selecting research topics and writing papers [13][14]. Course Highlights - The course emphasizes a comprehensive learning experience with a "2+1" teaching model, involving main instructors and experienced research assistants to support students throughout the program [22]. - Students will receive guidance on coding, research ideas, and writing methodologies, culminating in the production of a research paper draft [31][32]. Required Skills and Resources - Participants are expected to have a foundational understanding of deep learning, basic programming skills in Python, and familiarity with PyTorch [19]. - The program encourages the use of high-performance computing resources, ideally with multiple GPUs, to facilitate research and experimentation [19]. Conclusion - The VLA model represents a significant advancement in autonomous driving technology, with ongoing research and educational initiatives aimed at fostering innovation in this field [1][5][31].

Core Insights - The article discusses the recent advancements in the Li Auto VLA driver model, highlighting its improved capabilities in understanding semantics, reasoning, and trajectory planning, which are crucial for autonomous driving [1][3]. Group 1: VLA Model Capabilities - The VLA model's enhancements focus on four core abilities: spatial understanding, reasoning, communication and memory, and behavioral capabilities [1]. - The reasoning and communication abilities are derived from language models, with memory capabilities utilizing RAG [3]. Group 2: Research and Development Trends - The VLA model has evolved from VLM+E2E, incorporating various cutting-edge technologies such as end-to-end learning, trajectory prediction, visual language models, and reinforcement learning [5]. - While traditional perception and planning tasks are still being optimized in the industry, the academic community is increasingly shifting towards large models and VLA, indicating a wealth of subfields still open for research [5]. Group 3: VLA Research Guidance Program - A VLA research paper guidance program has been initiated, aimed at helping participants systematically grasp key theoretical knowledge and develop their own research ideas [6]. - The program includes a structured 12-week online group research course followed by 2 weeks of paper guidance and a 10-week maintenance period for paper development [14][34]. Group 4: Course Structure and Content - The course covers various topics over 14 weeks, including traditional end-to-end autonomous driving, VLA end-to-end models, and writing methodologies for research papers [9][11][35]. - Participants will gain insights into classic and cutting-edge papers, coding skills, and methods for writing and submitting research papers [20][34]. Group 5: Enrollment and Requirements - The program is limited to 6-8 participants per session, targeting individuals with a background in deep learning and basic knowledge of autonomous driving algorithms [12][15]. - Participants are expected to have a foundational understanding of Python and PyTorch, with access to high-performance computing resources recommended [21].

Core Insights - The article discusses the advancements of the Li Auto VLA driver model, highlighting its improved capabilities in understanding semantics, reasoning, and trajectory planning, which are crucial for autonomous driving [1][3][5] Group 1: VLA Model Capabilities - The VLA model demonstrates enhanced semantic understanding through multimodal input, improved reasoning via thinking chains, and a closer approximation to human driving intuition through trajectory planning [1] - Four core abilities of the VLA model are showcased: spatial understanding, reasoning ability, communication and memory capability, and behavioral ability [1][3] Group 2: Research and Development Trends - The VLA model has evolved from VLM+E2E, integrating various cutting-edge technologies such as end-to-end learning, trajectory prediction, visual language models, and reinforcement learning [5] - While traditional perception and planning tasks are still being optimized in the industry, the academic community is increasingly shifting focus towards large models and VLA, indicating a wealth of subfields still open for exploration [5] Group 3: VLA Research Guidance Program - A second session of the VLA research paper guidance program is being launched, aimed at helping participants systematically grasp key theoretical knowledge and develop their own research ideas [6][31] - The program includes a structured curriculum over 12 weeks of online group research, followed by 2 weeks of paper guidance and a 10-week maintenance period for paper development [14][31] Group 4: Course Structure and Requirements - The course is designed for a maximum of 8 participants, focusing on those pursuing master's or doctoral degrees in VLA and autonomous driving, as well as professionals in the AI field seeking to enhance their algorithmic knowledge [12][13] - Participants are expected to have a foundational understanding of deep learning, basic programming skills in Python, and familiarity with PyTorch [19][20] Group 5: Course Outcomes - Participants will gain insights into classic and cutting-edge papers, coding implementations, and methodologies for selecting research topics, conducting experiments, and writing papers [14][31] - The program aims to produce a draft of a research paper, enhancing participants' academic profiles for further studies or employment opportunities [14][31]

Core Viewpoint - The article discusses the potential of the VLA (Vision-Language-Action) architecture in autonomous driving, emphasizing its long-term viability and alignment with human cognitive processes [2][12]. Summary by Sections VLA Architecture and Technical Potential - VLA has strong technical potential, transitioning from manual to AI-driven autonomous driving, and is expected to support urban driving scenarios [2]. - The architecture is inspired by robotics and embodied intelligence, suggesting it will remain relevant even after the proliferation of robots [2]. Performance Metrics and Chip Capabilities - The Thor-U chip currently operates at 10Hz, with potential upgrades to 20Hz or 30Hz through optimizations [2]. - The VLA model is designed to be platform-agnostic, ensuring consistent performance across different hardware [2]. Language Integration and Cognitive Abilities - Language understanding is crucial for advanced autonomous driving capabilities, enhancing the model's ability to handle complex scenarios [2]. - VLA's ability to generalize and learn from experiences is likened to human learning, allowing it to adapt to new situations without repeated failures [2]. Model Upgrade and Iteration - The 3.2B MoE vehicle model has a structured upgrade cycle, focusing on both pre-training and post-training updates to enhance various capabilities [3]. User Experience and Trust - The article highlights the importance of user trust and experience, noting that different user groups will gradually accept the technology [2]. - Future iterations aim to improve driving speed and responsiveness, addressing current limitations in specific scenarios [5][12]. Competitive Landscape and Differentiation - The company is closely monitoring competitors like Tesla, aiming to differentiate its approach through gradual iterations and a focus on full-scene autonomous driving [12]. - VLA's architecture is designed to support unique product experiences, setting it apart from competitors [13]. Safety Mechanisms - The AEB (Automatic Emergency Braking) function is emphasized as a critical safety feature, ensuring high frame rates for emergency scenarios [14].

Core Insights - The core focus of the article is the launch of the Li Auto i8, a six-seat pure electric SUV, and the introduction of its new driver model, "Xiao Li Shifu" [1][2]. Group 1: Product Launch - Li Auto officially launched the i8 model, which is a family-oriented six-seat pure electric SUV [2]. - The i8 features the new Li VLA driver model, which is designed to assist with driving tasks [2]. Group 2: Technology and Features - The new driver model, "Xiao Li Shifu," is distinct from the existing "Li Xiang" assistant, with the former focusing on driving assistance and the latter on lifestyle tasks [2]. - An industry expert raised concerns about the complexity of having two separate voice assistants, suggesting it complicates user interaction unnecessarily [2].

Core Viewpoint - The article emphasizes the advancements in Li Auto's VLA (Vehicle Language Assistant) technology, highlighting its capabilities in intelligent driving and AI security, as well as the company's leadership role in the newly established Automotive AI Standardization Promotion Center. Group 1: VLA Technology Advancements - Li Auto's VLA can understand and execute voice commands for driving tasks, enhancing user experience by integrating visual, linguistic, and behavioral capabilities into a single chip [4][6]. - The VLA system employs a newly designed architecture that combines end-to-end models with logical reasoning, optimizing driving decisions and trajectory predictions to improve vehicle performance in complex environments [6][7]. - The VLA incorporates user interaction features, allowing drivers to communicate their needs easily, such as finding locations or adjusting driving speed [7][9]. Group 2: AI Security Measures - Li Auto has established a comprehensive security framework that covers vehicles, cloud services, apps, and charging networks, creating a robust defense system against potential threats [9][12]. - The article discusses the complexity of AI-targeted attacks, which can manipulate AI decision-making through subtle inputs, necessitating advanced defensive strategies [9][11]. - Li Auto is focusing on building an AI security capability system that addresses adversarial attacks, safety alignment, and behavioral constraints through continuous innovation and validation [11][12]. Group 3: Standardization Initiatives - The Automotive AI Standardization Promotion Center has been established to focus on AI safety management, risk governance, and the development of international standards for automotive AI [13][14]. - Li Auto has taken a leadership role in two research groups within the center, aiming to integrate research outcomes into products to enhance safety and intelligence in driving experiences [16][12]. - The SAFER AI initiative has been launched to create a comprehensive framework for AI safety assessment, engineering research, and standard development in the automotive sector [14][16].