Core Insights - The automatic driving industry is experiencing a paradox where job openings are abundant, yet companies struggle to find suitable talent. This is attributed to a shift in market expectations and a focus on sustainable business models rather than rapid expansion [2][3]. Industry Overview - Companies in the automatic driving sector are now more cautious with their spending, prioritizing survival and the establishment of viable business models over aggressive hiring and expansion strategies. This shift is expected to lead to significant industry adjustments within the next 1-3 years [2][3]. Talent Demand - There is an unprecedented demand for "top talent" and "highly compatible talent" in the automatic driving field. Companies are not necessarily unwilling to hire, but they are looking for candidates with exceptional skills and relevant experience [4][3]. Community and Resources - The "Automatic Driving Heart Knowledge Planet" is the largest community focused on automatic driving technology in China, established to provide resources and networking opportunities for professionals in the field. It has nearly 4000 members and over 100 industry experts contributing to discussions and knowledge sharing [9][10]. Learning and Development - The community offers comprehensive learning pathways covering various subfields of automatic driving technology, including perception, mapping, and AI model deployment. This initiative aims to support both newcomers and experienced professionals in enhancing their skills [9][12][13]. Job Placement Support - The community has established a direct referral mechanism with numerous automatic driving companies, facilitating job placements for members. This service aims to streamline the hiring process and connect qualified candidates with potential employers [10][9].

Core Viewpoint - The article emphasizes the importance of staying updated with cutting-edge technologies in the fields of autonomous driving and embodied intelligence, highlighting the need for strong technical skills and knowledge in advanced areas such as large models, reinforcement learning, and 3D graphics [4][5]. Group 1: Industry Trends - There is a growing demand for talent in the fields of robotics and embodied intelligence, with many startups receiving significant funding and showing rapid growth potential [4][5]. - Major companies are shifting their focus towards more advanced technologies, moving from traditional methods to end-to-end solutions and large models, indicating a technological evolution in the industry [4][5]. - The community aims to build a comprehensive ecosystem that connects academia, products, and recruitment, fostering a collaborative environment for knowledge sharing and job opportunities [6]. Group 2: Technical Directions - The article outlines four key technical directions in the industry: visual large language models, world models, diffusion models, and end-to-end autonomous driving [9]. - It provides resources and summaries of various research papers and datasets related to these technologies, indicating a strong emphasis on research and development [10][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][35][36][38]. Group 3: Community and Learning Resources - The community offers a variety of learning materials, including video courses, hardware, and coding resources, aimed at equipping individuals with the necessary skills for the evolving job market [6]. - There is a focus on creating a supportive environment for discussions on the latest industry trends, technical challenges, and job opportunities, which is crucial for professionals looking to advance their careers [6].

Core Viewpoint - The smart driving industry is experiencing significant growth, with companies willing to invest heavily in research and talent acquisition, indicating a robust job market and opportunities for new entrants [2][3]. Group 1: Industry Trends - The smart driving sector continues to attract substantial funding for research and development, with companies offering competitive salaries to attract talent [2]. - There is a noticeable trend of shorter technology iteration cycles in the autonomous driving field, with a focus on advanced technologies such as visual large language models (VLA) and end-to-end systems [7][11]. Group 2: Community and Learning Resources - The "Autonomous Driving Heart Knowledge Planet" aims to create a comprehensive community for knowledge sharing, focusing on academic and engineering challenges in the autonomous driving industry [3][11]. - The community has established a structured learning path covering various aspects of autonomous driving technology, including perception, planning, and control [13][15]. Group 3: Educational Offerings - The community offers a range of educational resources, including video courses, hardware tutorials, and live sessions with industry experts, aimed at both newcomers and experienced professionals [3][15]. - There are dedicated modules for job preparation, including resume sharing and interview experiences, to help members navigate the job market effectively [5][12]. Group 4: Technical Focus Areas - Key technical areas of focus include visual language models, world models, and end-to-end autonomous driving systems, with ongoing discussions about their integration and application in real-world scenarios [11][36]. - The community emphasizes the importance of understanding the latest advancements in algorithms and models, such as diffusion models and generative techniques, for future developments in autonomous driving [16][36].

Core Insights - The autonomous driving industry is experiencing significant changes, with many professionals transitioning to related fields like embodied intelligence, while others remain committed to the sector due to strong funding and high salaries for new graduates [2][6] - The article emphasizes the importance of networking and community engagement for knowledge acquisition and job preparation in the autonomous driving field [3][4] Group 1: Industry Trends - The autonomous driving sector continues to attract substantial investment, with companies willing to offer competitive salaries to attract talent [2] - The technology iteration cycle in autonomous driving is becoming shorter, indicating rapid advancements and a focus on cutting-edge technologies such as visual large language models (VLM) and end-to-end systems [8][12] Group 2: Community and Learning Resources - The "Autonomous Driving Heart Knowledge Planet" is highlighted as a leading community for professionals and students in the autonomous driving field, offering resources such as video courses, technical discussions, and job opportunities [4][14] - The community provides a structured learning path covering various aspects of autonomous driving technology, including perception, planning, and machine learning [19][21] Group 3: Technical Focus Areas - Key technical areas identified for 2025 include VLM, end-to-end systems, and world models, which are crucial for the future evolution of autonomous driving technology [8][43] - The community emphasizes the integration of advanced algorithms and models, such as diffusion models and 3D generative simulations, to enhance autonomous driving capabilities [15][22]

Core Viewpoint - The article discusses a new convolution-based diffusion model called DiC (Diffusion CNN) developed by researchers from Peking University, Beijing University of Posts and Telecommunications, and Huawei, which outperforms the popular Diffusion Transformer (DiT) in both performance and inference speed [1][5][24]. Group 1: Introduction and Background - The AI-generated content (AIGC) field has predominantly adopted transformer-based diffusion models, which, while powerful, come with significant computational costs and slow inference speeds [4]. - The researchers challenge the notion that transformer architectures are the only viable path for generative models by reverting to the classic 3x3 convolution [5][9]. Group 2: Technical Innovations - The choice of 3x3 convolution is justified by its excellent hardware support and optimization, making it a key operator for achieving high throughput [8]. - DiC employs a U-Net Hourglass architecture, which is found to be more effective than the traditional transformer stacking architecture, allowing for broader coverage of the original image area [13]. - A series of optimizations, including stage-specific embeddings, optimal injection points for conditional information, and conditional gating mechanisms, enhance the model's ability to utilize conditional information effectively [14][15]. Group 3: Experimental Results - DiC demonstrates superior performance metrics compared to DiT, achieving a FID score of 13.11 and an IS score of 100.15, significantly better than DiT-XL/2's FID score of 20.05 and IS score of 66.74 [17][18]. - The throughput of DiC-XL reaches 313.7, nearly five times that of DiT-XL/2, showcasing its efficiency in inference speed [18]. - DiC's convergence speed is ten times faster than DiT under the same conditions, indicating its potential for rapid training [18][19]. Group 4: Conclusion and Future Outlook - The emergence of DiC challenges the prevailing belief that generative models must rely on self-attention mechanisms, demonstrating that simple and efficient convolutional networks can still build powerful generative models [24].

Core Viewpoint - The article discusses the current trends and opportunities in the fields of autonomous driving and embodied intelligence, emphasizing the need for strong technical skills and knowledge in cutting-edge technologies for job seekers in these areas [3][4]. Group 1: Job Market Insights - The job market for autonomous driving and embodied intelligence is competitive, with a high demand for candidates with strong backgrounds and technical skills [2][3]. - Companies are increasingly looking for expertise in advanced areas such as end-to-end models, visual language models (VLM), and reinforcement learning [3][4]. - There is a saturation of talent in traditional robotics, but many startups in the robotics sector are rapidly growing and attracting significant funding [3][4]. Group 2: Learning and Development - The article encourages individuals to enhance their technical skills, particularly in areas like SLAM (Simultaneous Localization and Mapping) and ROS (Robot Operating System), which are relevant to robotics and embodied intelligence [3][4]. - A community platform is mentioned that offers resources such as video courses, hardware learning materials, and job information, aiming to build a large network of professionals in intelligent driving and embodied intelligence [5]. Group 3: Technical Trends - The article highlights four major technical directions in the industry: visual language models, world models, diffusion models, and end-to-end autonomous driving [8]. - It provides links to various resources and papers related to these technologies, indicating a focus on the latest advancements and applications in the field [9][10].

Core Viewpoint - The article emphasizes the importance of advanced skills and knowledge in the fields of autonomous driving and embodied intelligence, highlighting the need for candidates with strong backgrounds to meet industry demands. Group 1: Industry Trends - The demand for talent in autonomous driving and embodied intelligence is increasing, with a focus on cutting-edge technologies such as SLAM, ROS, and large models [3][4]. - Many companies are transitioning from traditional methods to more advanced techniques, indicating a shift in the required skill sets for job seekers [3][4]. - The article notes that while there is a saturation of talent in certain areas, the growth of startups in robotics presents new opportunities for learning and development [3][4]. Group 2: Learning and Development - The article encourages individuals to enhance their technical skills, particularly in areas related to robotics and embodied intelligence, which are seen as the forefront of technology [3][4]. - It mentions the availability of resources and community support for learning, including access to courses, hardware, and job information through platforms like Knowledge Planet [5][6]. - The community aims to create a comprehensive ecosystem for knowledge sharing and recruitment in the fields of intelligent driving and embodied intelligence [5][6]. Group 3: Technical Directions - The article outlines four major technical directions in the industry: visual large language models, world models, diffusion models, and end-to-end autonomous driving [7]. - It highlights the importance of staying updated with the latest research and developments in these areas, providing links to various resources and papers for further exploration [8][9].

Core Viewpoint - The article emphasizes the growing importance of autonomous driving technology and highlights the availability of over 100 high-quality datasets for developers and researchers in the field. It introduces five key datasets that cover various tasks from perception to visual odometry, providing valuable resources for both beginners and experienced engineers [2]. Dataset Summaries 1. KITTI Dataset - The KITTI dataset is one of the most classic and widely used benchmark datasets in the autonomous driving field. It was collected in Karlsruhe, Germany, using high-precision sensors such as stereo color/gray cameras, Velodyne 3D LiDAR, and GPS/IMU. The dataset includes annotations for various perception tasks, including stereo vision, optical flow, visual odometry, and 3D object detection and tracking, making it a standard for evaluating vehicle vision algorithms [3]. 2. nuScenes Dataset - nuScenes is a large-scale multi-sensor dataset released by Motional, covering 1,000 continuous driving scenes in Boston and Singapore, totaling approximately 15 hours of data. It includes a full suite of sensors: six cameras, five millimeter-wave radars, one top-mounted LiDAR, and IMU/GPS. The dataset provides around 1.4 million high-resolution camera images and 390,000 LiDAR scans, annotated with 3D bounding boxes for 23 object categories, making it suitable for research on complex urban road scenarios [5][7]. 3. Waymo Open Dataset - The Waymo Open Dataset, released by Google Waymo, is one of the largest open data resources for autonomous driving. It consists of two main parts: a perception dataset with 2,030 scenes of high-resolution camera and LiDAR data, and a motion dataset with 103,354 vehicle trajectories and corresponding 3D map information. This extensive multi-sensor dataset covers various times, weather conditions, and urban environments, serving as a benchmark for target detection, tracking, and trajectory prediction research [10][12]. 4. PathTrack Dataset - PathTrack is a dataset focused on person tracking, containing over 15,000 trajectories across 720 sequences. It utilizes a re-trained existing person matching network, significantly reducing the classification error rate. The dataset is suitable for 2D/3D object detection, tracking, and trajectory prediction tasks [13][14][15]. 5. ApolloScape Dataset - ApolloScape, released by Baidu Apollo, is a massive autonomous driving dataset characterized by its large volume and high annotation accuracy. It reportedly exceeds similar datasets in size by over ten times, containing hundreds of thousands of high-resolution images with pixel-level semantic segmentation annotations. ApolloScape defines 26 different semantic categories and includes complex road scenarios, making it applicable for perception, map construction, and simulation training [17][19].

Core Viewpoint - The article discusses the revolutionary advancements in video generation through the introduction of the Frame-aware Video Diffusion Model (FVDM) and its practical application in the Pusa project, which significantly reduces training costs and enhances video generation capabilities [2][3][37]. Group 1: FVDM and Pusa Project - FVDM introduces a vectorized timestep variable (VTV) that allows each frame to have an independent temporal evolution path, addressing the limitations of traditional scalar timesteps in video generation [2][18]. - The Pusa project, developed in collaboration with Huawei's Hong Kong Research Institute, serves as a direct application and validation of FVDM, exploring a low-cost method for fine-tuning large-scale pre-trained video models [3][37]. - Pusa achieves superior results compared to the official Wan I2V model while reducing training costs by over 200 times (from at least $100,000 to $500) and data requirements by over 2500 times [5][37]. Group 2: Technical Innovations - The Pusa project utilizes non-destructive fine-tuning on pre-trained models like Wan-T2V 14B, allowing for effective video generation without compromising the original model's capabilities [5][29]. - The introduction of a probabilistic timestep sampling training strategy (PTSS) in FVDM enhances convergence speed and improves performance compared to the original model [30][31]. - Pusa's VTV mechanism enables diverse video generation tasks by allowing different frames to have distinct noise perturbation controls, thus facilitating more nuanced video generation [35][36]. Group 3: Community Engagement and Future Prospects - The complete codebase, training datasets, and training code for Pusa have been open-sourced to encourage community contributions and collaboration, aiming to enhance performance and explore new possibilities in video generation [17][37]. - The article emphasizes the potential of Pusa to lead the video generation field into a new era characterized by low costs and high flexibility [36][37].

Group 1 - The article discusses the potential of Diffusion LLMs, particularly Gemini Diffusion, as a significant breakthrough in AI, challenging traditional autoregressive models [3][4][5] - Gemini Diffusion demonstrates high generation efficiency, achieving an average sampling speed of 1479 TPS and up to 2000 TPS in encoding tasks, outperforming Gemini 2.0 Flash-Lite by 4-5 times [4][6] - The parallel generation mechanism of the diffusion architecture allows for efficient processing, which could lead to reduced computational costs compared to autoregressive models [6][7] Group 2 - Mary Meeker emphasizes that the speed of AI development surpasses that of the internet era, highlighting the cost disparity between AI model training and inference [1][2] - The article suggests that the rise of open-source models in China may impact the global supply chain, indicating a shift in competitive dynamics within the industry [1][2] - The balance between computational investment and commercial returns is crucial for enterprises as AI inference costs decline [1][2]