Core Viewpoint - The article emphasizes the evolving landscape of the autonomous driving industry, highlighting the need for professionals to adapt their skill sets to align with current industry demands, particularly in areas like end-to-end VLA (Vision-Language Action) models and traditional control systems [4][6]. Summary by Sections Industry Trends - The demand for talent in autonomous driving is shifting towards candidates with strong backgrounds and skills in cutting-edge technologies, such as end-to-end VLA models, while traditional control systems still have job opportunities [2][4]. - The article notes that the technology stack in autonomous driving is becoming more standardized, reducing the diversity of recruitment directions compared to previous years [3][4]. Skill Development - Professionals are encouraged to upgrade their technical skills to meet the evolving demands of the industry, with a focus on continuous learning and adaptation [4][6]. - The article suggests that anxiety about job prospects can be mitigated by actively seeking out learning resources and engaging with communities that focus on the latest advancements in autonomous driving technology [4][6]. Learning Resources - The article mentions various learning modules available in the "Autonomous Driving Heart Knowledge Planet," which includes cutting-edge topics such as world models, trajectory prediction, and large models [5][11]. - It highlights the availability of videos and materials for beginners and advanced learners, aimed at helping individuals navigate the complexities of the autonomous driving field [4][5]. Community Engagement - The "Autonomous Driving Heart Knowledge Planet" is described as a significant community for knowledge sharing, featuring nearly 4000 members and over 100 industry experts, providing a platform for discussion and problem-solving [8][11]. - The community focuses on various subfields within autonomous driving, including perception, mapping, planning, and control, offering a comprehensive approach to learning and professional development [11][13].

>>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 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15个 方向 学习 路线 今天自动驾驶之心为大家分享 华中科技大学最新的工作！ SDG-OCC：融合 LiDAR深度先验与图像语义，刷新Occ3D-nuScenes BEV特征生成SOTA！ 如果您 有相关工作需要分享，请在文末联系我们！ 自动驾驶课程学习与技术交流群事宜，也欢迎添加小助理微信AIDriver004做进一 步咨询 论文作者 | ZaiPeng Duan等 编辑 | 自动驾驶之心 背景 3D 环境的准确感知是现代自动驾驶系统和机器人技术的基础，能保障高效规划和安全控制。近年来，3D 目标检测和语义分割的进展推动了 3D 感知领域发展，但目标检测依赖精准的3D框，并且难以识别开集的 模板；语义分割在复杂场景（尤其是遮挡和重叠情况下）的细粒度分类表现不佳。 3D 语义占用预测作为更全面的环境建模方法，同时估计场景体素的几何结构和语义类别，为每个 3D 体素 分配标签，对任意形状和动态遮挡具有更强鲁棒性，因此在自动驾驶领域中展现出了巨大潜力。 然而，现有 3D 占用预测方法存 ...

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Core Viewpoint - The article discusses the current development status of end-to-end autonomous driving algorithms, comparing them with traditional algorithms and highlighting their advantages and limitations [1][3][53]. Summary by Sections Traditional vs. End-to-End Algorithms - Traditional autonomous driving algorithms follow a pipeline of perception, prediction, and planning, where each module has distinct inputs and outputs [3]. - End-to-end algorithms take raw sensor data as input and directly output path points, simplifying the process and reducing error accumulation [3][5]. - Traditional algorithms are easier to debug and have some level of interpretability, but they suffer from cumulative error issues due to the inability to ensure complete accuracy in perception and prediction modules [3][5]. Limitations of End-to-End Algorithms - End-to-end algorithms face challenges such as limited ability to handle corner cases, as they rely heavily on data-driven methods [7][8]. - The use of imitation learning in these algorithms can lead to difficulties in learning optimal ground truth and handling exceptional cases [53]. - Current end-to-end paradigms include imitation learning (behavior cloning and inverse reinforcement learning) and reinforcement learning, with evaluation methods categorized into open-loop and closed-loop [8]. Current Implementations - The ST-P3 algorithm is highlighted as an early work focusing on end-to-end autonomous driving, utilizing a framework that includes perception, prediction, and planning modules [10][11]. - Innovations in the ST-P3 algorithm include a perception module that uses a self-centered cumulative alignment technique and a prediction module that employs a dual-path prediction mechanism [11][13]. - The planning phase of ST-P3 optimizes predicted trajectories by incorporating traffic light information [14][15]. Advanced Techniques - The UniAD system employs a full Transformer framework for end-to-end autonomous driving, integrating multiple tasks to enhance performance [23][25]. - The TrackFormer framework focuses on the collaborative updating of track queries and detect queries to improve prediction accuracy [26]. - The VAD (Vectorized Autonomous Driving) method introduces vectorized representations for better structural information and faster computation in trajectory planning [32][33]. Future Directions - The article suggests that end-to-end algorithms still primarily rely on imitation learning frameworks, which have inherent limitations that need further exploration [53]. - The introduction of more constraints and multi-modal planning methods aims to address trajectory prediction instability and improve model performance [49][52].

Core Viewpoint - The article discusses the competitive landscape of the autonomous driving industry, focusing on NVIDIA's challenges in maintaining its market position against emerging Chinese companies and the shift towards self-developed chips by major automakers [5][15][50]. Group 1: NVIDIA's Market Position - NVIDIA's market capitalization has reached $4 trillion, making it the world's most valuable company, but it faces increasing competition from Chinese automakers who are trying to reduce reliance on NVIDIA's technology [5][15]. - General Motors' executives have expressed concerns about NVIDIA's autonomous driving solutions, indicating potential issues in their collaboration [7][8]. - Other automakers, such as Mercedes-Benz, have also reported that NVIDIA's autonomous driving performance is lagging behind that of Chinese startups like Momenta [10][11]. Group 2: Challenges in Chip Delivery - NVIDIA's latest Thor chip has faced multiple delays, impacting key clients like Li Auto, which has resulted in significant sales losses estimated at around 6 billion yuan due to postponed vehicle launches [18][19]. - The delays in chip delivery have prompted companies like Xiaopeng to pivot towards self-developed chips, as they can no longer rely on NVIDIA's timelines [20][24]. - The challenges faced by NVIDIA in delivering the Thor chip are attributed to design flaws and the complexity of automotive-grade chip production, which differs from consumer electronics [34][42][46]. Group 3: Shift Towards Self-Developed Chips - Major Chinese automakers are increasingly investing in self-developed chips to reduce costs and enhance compatibility with their AI technologies, with companies like NIO and Xiaopeng already making significant progress [25][35][37]. - The self-development of chips is seen as a strategic necessity for automakers to maintain competitiveness in the rapidly evolving autonomous driving market [38][39]. - The article highlights that the development of self-developed chips is a long-term commitment, with significant investments and risks involved, but it is becoming essential due to supply chain uncertainties [26][27][30]. Group 4: Competitive Landscape - The competition in the autonomous driving software space is intensifying, with Chinese companies like Momenta and Qingtou Zhihang rapidly advancing their technologies, often outpacing NVIDIA's offerings [51][53]. - NVIDIA's corporate culture and operational structure may hinder its ability to adapt quickly to the demands of the automotive industry, contrasting with the agile approaches of Chinese startups [52][54]. - The article suggests that the future of autonomous driving will likely see a shift towards more localized solutions, with Chinese companies capturing a larger share of the market as they innovate faster and align more closely with automotive needs [55].

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15个 方向 学习 路线 今天自动驾驶之心为大家分享 复旦大学ICCV2025中稿的最新工作！ BezierGS：基于贝塞尔曲线高斯泼溅的动态城市场景重建！ 如果您有相关 工作需要分享，请在文末联系我们！ 自动驾驶课程学习与技术交流群事宜，也欢迎添加小助理微信AIDriver004做进一步咨询 ICCV 2025中稿的最新工作！自动驾驶场景的真实重建对于开发闭环仿真至关重要。大多数现有方法依赖于目标的位姿标注，使用这些位姿来重建动态目标并在渲染过程 中实现动态重建。这种对高精度目标标注的依赖限制了大规模和广泛场景的重建。为了解决这一挑战，复旦大学的团队提出了Bezier curve Gaussian splatting （BezierGS），该方法使用可学习的贝塞尔曲线表示动态目标的运动轨迹。这种方法充分利用了动态目标的时间信息，并通过可学习的曲线建模自动校准位姿误差。通过 引入对动态目标渲染的额外监督和曲线间一致性约束，实现了合理且准确的场景元素分离和重建。在Waymo开放数据集和nuPlan基准上的大量实验表明，BezierGS在 ...

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 shift in academic research from traditional perception and planning tasks in autonomous driving to the exploration of Vision-Language-Action (VLA) models, suggesting that there are still many opportunities for research in this area [1][2]. Group 1: VLA Research Topics - The VLA model represents a new paradigm in autonomous driving, integrating vision, language, and action to enhance decision-making capabilities [2][3]. - The evolution of autonomous driving technology can be categorized into three phases: traditional modular architecture, pure visual end-to-end systems, and the emergence of VLA models [2][3]. - VLA models aim to improve interpretability and reliability by allowing the model to explain its decisions in natural language, thus increasing transparency and trust [3]. Group 2: Course Objectives and Structure - The course aims to help participants systematically master key theoretical knowledge in VLA and develop practical skills in model design and implementation [6][7]. - Participants will engage in a 12-week online group research followed by 2 weeks of paper guidance, culminating in a 10-week maintenance period for their research papers [6]. - The course will provide insights into classic and cutting-edge papers, coding implementations, and writing methodologies, ultimately assisting participants in producing a research paper draft [6][12]. Group 3: Enrollment and Requirements - The course is limited to 6-8 participants per session, targeting individuals with a foundational understanding of deep learning and basic programming skills [5][9]. - Participants are expected to have access to high-performance computing resources, ideally with multiple high-end GPUs, to facilitate their research [13][14]. - A preliminary assessment will be conducted to tailor the course content to the individual needs of participants, ensuring a focused learning experience [15]. Group 4: Course Highlights and Outcomes - The course features a "2+1" teaching model, providing comprehensive support from experienced instructors and research mentors [15]. - Participants will gain a thorough understanding of the research process, writing techniques, and submission strategies, enhancing their academic and professional profiles [15][20]. - The expected outcomes include a research paper draft, project completion certificates, and potential recommendation letters based on performance [15].

Core Viewpoint - The article emphasizes the importance of proactive engagement in research and the utilization of available resources to enhance academic and career prospects for students, particularly in the context of job hunting and academic publishing [1]. Group 1: Research Guidance and Support - The company offers a comprehensive research guidance program aimed at helping students produce high-quality academic papers, particularly in AI-related fields [3][11]. - A successful case is highlighted where a second-year graduate student completed an SCI paper in three months with the company's assistance [2]. - The program includes personalized mentoring from over 300 qualified instructors, with a high acceptance rate of 96% for students who have received guidance [3]. Group 2: Course Structure and Benefits - The structured course spans 12 weeks, covering topic selection, literature review, experimental design, drafting, and submission processes [5]. - Students can expect to gain practical skills, including coding and experimental design, which are essential for producing their own research papers [9][10]. - The program also offers additional benefits such as recommendations to prestigious institutions and companies for outstanding students [14]. Group 3: Target Audience and Accessibility - The services are tailored for graduate students, particularly those with limited guidance from their advisors, and those seeking to enhance their research capabilities [6][10]. - The company provides a matching system to ensure students are paired with suitable mentors based on their research interests and goals [13]. - The program is accessible to students with no prior research experience, with foundational courses available to support their learning [13].

Group 1 - The article discusses the recruitment of business partners for the "Autonomous Driving Heart" initiative, aiming to onboard 10 outstanding partners (individuals and enterprises) for various autonomous driving projects [2] - The main focus areas for potential partners include large models, multimodal models, diffusion models, and other advanced AI technologies related to autonomous driving [2] - The article outlines the requirements for applicants, emphasizing a master's degree or higher from universities ranked within QS200, with a preference for candidates with significant contributions to top conferences [2] Group 2 - The article highlights the benefits for partners, including resource sharing for job placements, PhD recommendations, and study abroad opportunities [3] - It mentions attractive cash incentives and opportunities for collaboration on entrepreneurial projects [3] - Contact information is provided for interested parties to inquire about collaboration in autonomous driving projects [3]