作者 | 蔡道清 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/1932480841222723066 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 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 作者阵容挺强的，代码也开源了，值得follow。 Motivation 自动驾驶世界模型需同时满足长时程高分辨率场景生成与实时精准轨迹规划，但现有方法存在明显局限： 因而，本文提出一个 既能生成长时高分辨率视频，又能端到端输出连续轨迹 的统一框架。 Contribution Paper link ： https://arxiv.org/pdf/2506.24113 Code link ： https://github.com/Kevin-thu/Epona 扩散模型（如Vista）：固定长度视频生成（≤15秒），无法支持灵活长时预测（>2分钟）和多模态轨迹控制； GPT式自回归模型（如GAIA-1）：可无限延伸，却需把图像 离散成 token ，导致视觉质量下降，且缺乏连 ...

Group 1 - The article discusses recent advancements in autonomous driving technologies, highlighting several innovative frameworks and models [3][9][21][33][45] - GS-Occ3D achieves state-of-the-art (SOTA) geometric accuracy with a 0.56 corner distance (CD) on the Waymo dataset, demonstrating superior performance over LiDAR-based methods [3][5] - BEV-LLM introduces a lightweight multimodal scene description model that outperforms existing models by 5% in BLEU-4 score, showcasing the integration of LiDAR and multi-view images [9][10] - CoopTrack presents an end-to-end cooperative perception framework that sets new SOTA performance on the V2X-Seq dataset with 39.0% mAP and 32.8% AMOTA [21][22] - The Diffusion-FS model achieves a 0.7767 IoU in free-space prediction, marking a significant improvement in multimodal driving channel prediction [45][48] Group 2 - GS-Occ3D's contributions include a scalable visual occupancy label generation pipeline that eliminates reliance on LiDAR annotations, enhancing the training efficiency for downstream models [5][6] - BEV-LLM utilizes BEVFusion to combine 360-degree panoramic images with LiDAR point clouds, improving the accuracy of scene descriptions [10][12] - CoopTrack's innovative instance-level end-to-end framework integrates cooperative tracking and perception, enhancing the learning capabilities across agents [22][26] - The ContourDiff model introduces a novel self-supervised method for generating free-space samples, reducing dependency on dense annotated data [48][49]

Core Viewpoint - The article discusses the principles and applications of Diffusion Models and Variational Autoencoders (VAE) in the context of machine learning, particularly focusing on their mathematical foundations and training methodologies. Group 1: Diffusion Models - The training objective of the network is to fit the mean and variance of two Gaussian distributions during the denoising process [7] - The KL divergence term is crucial for fitting the theoretical values and the network's predicted values in the denoising process [9] - The process of transforming the uncertain variable \(x_0\) into the uncertain noise \(\epsilon\) is iteratively predicted [15] Group 2: Variational Autoencoders (VAE) - VAE assumes that the latent distribution follows a Gaussian distribution, which is essential for its generative capabilities [19] - The training of VAE is transformed into a combination of reconstruction loss and KL divergence constraint loss to prevent the latent space from degenerating into a sharp distribution [26] - Minimizing the KL loss corresponds to maximizing the Evidence Lower Bound (ELBO) [27] Group 3: Reinforcement Learning (RL) - The Markov Decision Process (MDP) framework is utilized, which includes states and actions in a sequential manner [35] - The semantic representation aims to approach a pulse distribution, while the generated representation is expected to follow a Gaussian distribution [36] - Policy gradient methods are employed to enable the network to learn the optimal action given a state [42]

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].