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. The article emphasizes the significance of multi-agent trajectory prediction methods based on diffusion models, which are gaining traction in various applications such as autonomous driving and intelligent monitoring [1][2]. Group 1: Trajectory Prediction Research - Despite the rise of end-to-end models, trajectory prediction continues to be a hot research area, with significant output in conferences and journals [1]. - Multi-agent trajectory prediction aims to forecast future movements based on historical trajectories of multiple interacting agents, which is crucial in fields like autonomous driving and robotics [1]. - Traditional methods often struggle with the uncertainty and multimodality of human behavior, while generative models like GANs and CVAEs, although capable of simulating multimodal distributions, lack efficiency [1]. Group 2: Diffusion Models - Diffusion models have emerged as a new class of models that achieve complex distribution generation through gradual denoising, showing significant breakthroughs in image generation and other fields [2]. - The Leapfrog Diffusion Model (LED) enhances real-time prediction by reducing denoising steps, achieving a 19-30 times speedup while improving accuracy on various datasets [2]. - Mixed Gaussian Flow (MGF) and Pattern Memory-based Diffusion Model (MPMNet) are also highlighted for their advanced performance in trajectory prediction by better matching multimodal distributions and utilizing human motion patterns, respectively [2]. Group 3: Course Objectives and Structure - The course aims to provide a systematic understanding of trajectory prediction and diffusion models, helping students integrate theoretical knowledge with practical coding skills [6]. - It addresses common challenges faced by students, such as lack of direction and difficulties in reproducing research papers, by offering a structured approach to model development and academic writing [6]. - The course includes a comprehensive curriculum that covers classic and cutting-edge papers, coding implementations, and writing methodologies, ultimately guiding students to produce a draft of a research paper [6][9]. Group 4: Target Audience and Requirements - The course is designed for graduate students and professionals in trajectory prediction and autonomous driving, aiming to enhance their research capabilities and resume value [8]. - Participants are expected to have a foundational understanding of deep learning and familiarity with Python and PyTorch [10]. - The course emphasizes the importance of academic integrity and active participation, with specific requirements for attendance and assignment completion [15]. Group 5: Course Highlights and Outcomes - The program features a "2+1" teaching model with experienced instructors providing comprehensive support throughout the learning process [16][17]. - Students will gain access to datasets, baseline codes, and essential papers, facilitating a deeper understanding of the subject matter [20][21]. - Upon completion, students will have produced a research paper draft, a project completion certificate, and potentially a recommendation letter based on their performance [19].

1. 自身实力不够过硬 2. 导师精力资源没有倾斜给到 话又说回来，大导精力有限，对所带学生很难平均照顾，只能深入指导他最看重的几个学生。 于是 问题又绕回了自身实力这一块，如何打破循环，快速发一篇高质量论文？ 跟随大佬套路，一年两篇论文不是问题！ 自动驾驶之心服务大家的论文辅导正式推出了，联手全球 QS排名前100的老师，严格要求交付过程，不盲目招生，以诚信服务学生为主，近3年辅导学员超过 400+名，中稿率高达96%。 辅导全流程 明确需求与方向 → 精准选题与文献综述 → 创新方法设计与实验规划 → 严谨实验与深度分析 → 规范 写作与结构优化 → 多轮修改与反馈迭代 → 投稿选则与意见回复。 签订正规协议，保障你的研究想法、论文内容及个人隐私！ 好消息: 2025年国内高校硕博扩招继续推进，自驾与人工智能等工科招生增幅普遍超过30%, 许多同学 成功在这个时候申上了硕/博士。 坏消息： 大厂面试人手2篇A会，未来就业不确定，毕业时间不确定，论文发表不确定，导师意见不 确定，实验结果不确定 ，竞争压力与就业压力与日俱增，时代的黑利也算是吃上了... 以上所有问题的根源说到底就是： 我们能帮你什么？ 一直 ...

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 大家好，我们是自动驾驶之心/具身智能/大模型之心Tech团队。非常高兴在这里和你相遇，如果你也认同技 术内容可以改变世界，那你可能就是我们在找的人！ 我们在做什么？ 我们希望通过技术内容连接学术界和工业界，成为企业和学校沟通的桥梁，更乃至数十万的AI开发者和创 业者。我们致力于为大家带来全网最新最权威的技术信息，团队聚焦在自动驾驶、具身智能、大模型等AI 最前沿的技术领域，涵盖学术论文解读、业内量产方案分析、大模型评测、商业动态、行业招聘、开源项 目等，并通过公众号、社群、视频号、知乎、小红书、B站等平台进行内容分享、粉丝交流及企业联系。 有技术背景，独立解读学术论文，运行部署开源项目和撰写代码demo； 1. 负责大模型/自动驾驶/具身智能等方向学术论文选题、解读和汇总； 2. 负责大模型/自动驾驶/具身智能方向知识星球的搭建； 3. 负责大模型/自动驾驶/具身智能的原创视频制作； 4. 负责原创稿件的撰写策划； 5. 推后管理和数据复盘； 2. 对技术相关的前沿进展和事件有极高的研究热情和分享欲； 3. ...

Core Insights - The article discusses the potential and understanding of AI, particularly focusing on the concept of "predicting the next token" and its implications for AI capabilities and consciousness [2][3][18]. Group 1: Understanding AI and Token Prediction - Different interpretations of "predicting the next token" reflect varying understandings of the potential and essence of LLM (Large Language Models) and AI [2]. - Those who view "predicting the next token" as more than just a statistical distribution are more likely to recognize the significant potential of LLMs and AI [2][18]. - The article argues that the contributions of companies like 理想 (Li Auto) in AI development are often underestimated due to a lack of deep understanding of AI's capabilities [2][19]. Group 2: Ilya's Contributions and Perspectives - Ilya, a prominent figure in AI, has been instrumental in several key advancements in the field, including deep learning and reinforcement learning [4][5][6]. - His views on "predicting the next token" challenge the notion that it cannot surpass human performance, suggesting that a sufficiently advanced neural network could extrapolate behaviors of hypothetical individuals with superior capabilities [8][9][18]. Group 3: Li Auto's VLA and AI Integration - 理想's VLA (Vehicle Learning Architecture) operates by continuously predicting the next action token based on sensor inputs, which is a more profound understanding of the physical world rather than mere statistical analysis [19][20]. - The reasoning process of 理想's VLA is likened to consciousness, differing from traditional chatbots, as it operates in real-time and ceases when the system is turned off [21][22]. - The article posits that the integration of AI software and hardware in 理想's approach is at a high level, which is often overlooked by those in the industry [29]. Group 4: Reinforcement Learning in AI Applications - The article asserts that assisted driving is more suitable for reinforcement learning compared to chatbots, as the reward functions in driving are clearer and more defined [24][26]. - The differences in the underlying capabilities required for AI software and hardware development are significant, with software allowing for rapid iteration and testing, unlike hardware [28].

Core Viewpoint - The article discusses the challenges and methodologies related to the automation of occupancy network (OCC) data labeling in the context of autonomous driving, emphasizing the need for high-quality training data to improve model generalization and safety. Group 1: OCC Data Labeling Challenges - The need for high-quality training data is highlighted due to incidents caused by undetected obstacles, such as fallen tree branches during adverse weather conditions [2]. - The OCC network is essential for modeling irregular obstacles and background elements, which increases the demand for accurate data labeling [5]. - The automation of OCC data labeling is being pursued by many companies to enhance model performance and reduce costs associated with manual labeling [2][10]. Group 2: Automation Techniques - The common process for generating OCC training ground truth involves three main methods: 2D-3D object detection consistency, comparison with edge models, and manual intervention for quality control [9]. - High-quality automated labeling data can be used for both vehicle model training and cloud model optimization, facilitating continuous iteration [10]. Group 3: 4D Automated Labeling Course - A course is introduced that covers the entire process of 4D automated labeling, including dynamic and static object detection, and the challenges faced in real-world applications [10][12]. - The course aims to address the difficulties in learning and advancing in the field of automated driving data labeling, providing a comprehensive understanding of core algorithms and practical applications [10][11]. Group 4: Key Learning Outcomes - Participants will gain knowledge of the entire 4D automated labeling process, including dynamic obstacle detection, SLAM reconstruction, and the generation of end-to-end ground truth [12][20]. - The course also focuses on the practical implementation of algorithms and the resolution of common issues encountered in the industry [15][22]. Group 5: Target Audience - The course is designed for various groups, including researchers, students, and professionals looking to transition into the field of data closure in autonomous driving [26][31].

Core Insights - The article discusses the development of DistillDrive, an end-to-end autonomous driving model that significantly reduces collision rates by 50% and improves closed-loop performance by 3 percentage points compared to baseline models [2][7]. Group 1: Model Overview - DistillDrive utilizes a knowledge distillation framework to enhance multi-modal motion feature learning, addressing the limitations of existing models that overly focus on ego-vehicle status [2][6]. - The model incorporates a structured scene representation as a teacher model, leveraging diverse planning instances for multi-objective learning [2][6]. - Reinforcement learning is introduced to optimize the mapping from states to decisions, while generative modeling is used to construct planning-oriented instances [2][6]. Group 2: Experimental Validation - The model was validated on the nuScenes and NAVSIM datasets, demonstrating a 50% reduction in collision rates and a 3-point improvement in performance metrics [7][37]. - The nuScenes dataset consists of 1,000 driving scenes, while the NAVSIM dataset enhances perception capabilities with high-quality annotations and complex scenarios [33][36]. Group 3: Performance Metrics - DistillDrive outperformed existing models, achieving lower collision rates and reduced L2 error compared to SparseDrive, indicating the effectiveness of diversified imitation learning [37][38]. - The teacher model exhibited superior performance, confirming the effectiveness of reinforcement learning in optimizing state space [37][39]. Group 4: Future Directions - Future work aims to integrate world models with language models to further enhance planning performance and employ more effective reinforcement learning methods [54][55].

Core Insights - Embodied intelligence is a hot topic this year, transitioning from previous years' silence to last year's frenzy, and now gradually cooling down as the industry realizes that embodied robots are far from being productive [1] Group 1: Industry Trends - The demand for multi-sensor fusion and positioning in robotics is significant, with a focus on SLAM and ROS technologies [3] - Many robotics companies are rapidly developing and have secured considerable funding, indicating a promising future for the sector [3] - Traditional robotics remains the main product line, despite the excitement around embodied intelligence [3] Group 2: Community and Resources - The community has established a closed loop across various fields including industry, academia, and job seeking, aiming to create a valuable exchange platform [4][6] - The community offers access to over 40 technical routes and invites industry leaders for discussions, enhancing learning and networking opportunities [6][20] - Members can freely ask questions regarding job choices or research directions, receiving guidance from experienced professionals [83] Group 3: Educational Content - Comprehensive resources for beginners and advanced learners are available, including technical stacks and learning roadmaps for autonomous driving and robotics [13][16] - The community has compiled a list of notable domestic and international research labs and companies in the autonomous driving and robotics sectors, aiding members in their academic and career pursuits [27][29]

Core Viewpoint - The 2025 World Robot Conference (WRC) in Beijing highlights 3D perception technology as a key focus, showcasing advancements in spatial memory modules and multi-modal sensors that enhance robotic capabilities in various industries [2][4]. Group 1: 3D Reconstruction Technology - The ultimate goal of 3D reconstruction technology is to enable robots to understand, navigate, and operate in any environment [4]. - The latest handheld laser scanner, D-H100, achieves centimeter-level precision scanning at a distance of 120 meters, significantly improving efficiency by 300% in complex environments [4]. - The integration of laser scanning capabilities with robots can facilitate real-time mapping of disaster areas and enhance operational efficiency in industrial settings [4][5]. Group 2: GeoScan S1 Laser Scanner - GeoScan S1 is presented as the most cost-effective handheld 3D laser scanner in China, featuring a lightweight design and easy one-button operation for efficient 3D solutions [7][12]. - The device supports real-time reconstruction of 3D scenes with centimeter-level accuracy and can cover areas exceeding 200,000 square meters [7][25]. - It integrates multiple sensors and offers high bandwidth connectivity, making it suitable for various research and industrial applications [7][9]. Group 3: Technical Specifications and Features - GeoScan S1 operates on Ubuntu 20.04 and supports various data export formats, including PCD, LAS, and PLY, with relative accuracy better than 3 cm and absolute accuracy better than 5 cm [25][28]. - The scanner features a compact design with dimensions of 14.2 cm x 9.5 cm x 45 cm and weighs 1.3 kg without the battery, providing a battery life of approximately 3 to 4 hours [25][27]. - It includes advanced synchronization technology for multi-sensor data, ensuring precise mapping in complex indoor and outdoor environments [33][34]. Group 4: Market Position and Pricing - The GeoScan S1 is available in multiple versions, with prices starting at 19,800 yuan for the basic model and going up to 67,800 yuan for the offline version [60]. - The product is backed by extensive research and validation from teams at Tongji University and Northwestern Polytechnical University, ensuring reliability and performance [14][18]. - The scanner is designed for cross-platform integration, making it compatible with drones, unmanned vehicles, and humanoid robots for automated operations [45][48].

Group 1 - The core focus of the research is on "multi-agent trajectory prediction methods based on diffusion models," which is crucial for applications in autonomous driving, intelligent monitoring, and robot navigation [1][2] - Traditional methods for trajectory prediction often rely on recurrent neural networks, convolutional networks, or graph neural networks, while diffusion models have shown significant improvements in multimodal modeling capabilities [1] - The Leapfrog Diffusion Model (LED) has demonstrated a 19-30 times acceleration in real-time prediction accuracy on datasets such as NBA, NFL, SDD, and ETHUCY [1] Group 2 - The research aims to integrate diffusion generation mechanisms to model trajectory uncertainty while incorporating social interaction modeling and conditional control mechanisms [2] - The expected outcomes include an algorithm framework, quantitative and visual displays, and high-level papers with broad application prospects in autonomous driving, intelligent monitoring, and service robots [2] Group 3 - The course is designed to help students systematically master key theoretical knowledge in trajectory prediction and related fields, addressing gaps in understanding and practical skills [5] - It targets students at various academic levels (bachelor's, master's, PhD) who are interested in trajectory prediction and autonomous driving, aiming to enhance their research capabilities and resume value [7] Group 4 - The course will provide access to public datasets such as ETH, UCY, and SDD, along with baseline code for diffusion model trajectory prediction [19][20] - Students will engage with classic and cutting-edge papers, learning about innovative points, baseline methods, datasets, and writing techniques [5][8]

Core Viewpoint - The article emphasizes the importance of individual approaches and methodologies in the field of large language models (LLMs), particularly in the context of fine-tuning and data quality, suggesting that the technical depth of work in this area is highly dependent on personal engagement and practices [5][16]. Data Work - Method 1 involves inheriting training data from colleagues without checking data quality, which may lead to suboptimal results [7]. - Method 2 suggests downloading open-source data to create a "system + query + answer" dataset [8]. - Method 3 focuses on generating data using GPT-4, emphasizing the diversity of prompts and the importance of data quality checks [8]. - Method 4 advocates using user interaction logs to drive data construction, analyzing user feedback to improve answer quality [9]. - Method 5 recommends breaking down complex tasks at the data level to enhance model performance [9]. Training Code - Method 1 involves inheriting training code and making minimal modifications [11]. - Method 2 encourages a thorough understanding of training code parameters and their implications [11]. - Method 3 promotes questioning and improving training code, such as optimizing speed and framework choices [12]. Experimental Analysis - Method 1 suggests running prepared evaluation sets and addressing data quality issues based on results [14]. - Method 2 involves analyzing bad cases from models to identify underlying issues and designing experiments to validate findings [14]. - Method 3 emphasizes the relationship between model results, data quality, and training methods, advocating for a comprehensive analysis of training logs and evaluation results [15]. Community and Collaboration - The article highlights the establishment of a large community focused on various aspects of autonomous driving technology, including large models and multi-sensor fusion, with nearly 4,000 members and over 300 companies and research institutions involved [18].