Core Viewpoint - The article discusses the unique characteristics of the AI academic community compared to other disciplines, highlighting the rapid growth and the implications for the quality and significance of research papers submitted to top conferences [5][7][8]. Group 1: Characteristics of AI Academic Community - AI conferences are more important than journals due to the fast-paced development of AI, which makes the lengthy journal review process inadequate [5]. - The number of submissions and acceptances to top AI conferences has significantly increased over the past decade, with acceptance rates declining, indicating a surge in competition [5][7]. - The rapid increase in submissions has led to a shortage of qualified reviewers, resulting in a decline in the quality of accepted papers [8]. Group 2: Implications for Research Quality - The increase in accepted papers does not guarantee high-quality research, as many accepted papers may lack substantial contributions [8]. - The job market for AI researchers is becoming increasingly competitive, with the demand for high-quality publications rising faster than the availability of quality positions [8]. Group 3: Company-Specific Insights - Li Auto's recent achievement of having multiple papers accepted at ICCV is used as a promotional tool to showcase its advancements in assisted driving technology [9]. - The original innovation level of Li Auto's VLA is compared to DeepSeek's MoE level, indicating that few Chinese companies can achieve such a high level of innovation [11][12]. - Li Auto's approach to autonomous driving has evolved from following Tesla to developing its unique systems, particularly in the integration of fast and slow systems in its VLM [12][13].

Core Viewpoint - The article emphasizes the rapid evolution and commercialization of autonomous driving technologies, highlighting the importance of community engagement and knowledge sharing in this field [9][14][19]. Group 1: Job Opportunities and Community Engagement - Huawei is actively recruiting for various positions in its autonomous driving division, including roles focused on end-to-end model algorithms, perception models, and efficiency optimization [1][2]. - The "Autonomous Driving Heart Knowledge Planet" serves as a platform for technical exchange, targeting students and professionals in the autonomous driving and AI sectors, and has established connections with numerous industry companies for job referrals [7][14][15]. Group 2: Technological Trends and Future Directions - The article outlines that by 2025, the focus will be on advanced technologies such as visual large language models (VLM), end-to-end trajectory prediction, and 3D generative simulations, indicating a shift towards more integrated and intelligent systems in autonomous driving [9][22]. - The community has developed over 30 learning pathways covering various subfields of autonomous driving, including perception, mapping, and AI model deployment, which are crucial for industry professionals [19][21]. Group 3: Educational Resources and Content - The knowledge platform offers exclusive rights to members, including access to academic advancements, professional Q&A sessions, and discounts on courses, fostering a comprehensive learning environment [17][19]. - Regular webinars featuring experts from top conferences and companies are organized to discuss practical applications and research in autonomous driving, enhancing the learning experience for participants [21][22].

Core Viewpoint - The article discusses the limitations of existing end-to-end methods in autonomous driving, particularly the computational intensity of BEV paradigms and the inefficiency of sequential prediction and planning approaches. It proposes a new Sparse paradigm that allows for parallel processing of prediction and planning tasks [2][5]. Group 1: SparseDrive Methodology - SparseDrive adopts the core ideas from the previous Horizon Sparse series, focusing on sparse scene representation for autonomous driving [3]. - The proposed method modifies the similarities between motion prediction and planning, introducing a hierarchical planning selection strategy [5]. - The architecture includes features such as symmetric sparse perception and a parallel motion planner [5]. Group 2: Training and Performance - The training loss function for SparseDrive is defined as a combination of detection, mapping, motion, planning, and depth losses [9]. - Performance comparisons show that SparseDrive-S achieves a mean Average Precision (mAP) of 0.418, while SparseDrive-B reaches 0.496, outperforming other methods like UniAD [11]. - In motion prediction and planning, SparseDrive-S and SparseDrive-B demonstrate significant improvements in metrics such as minADE and minFDE compared to traditional methods [18]. Group 3: Efficiency Comparison - SparseDrive exhibits superior training and inference efficiency, requiring only 15.2 GB of GPU memory and achieving 9.0 FPS during inference, compared to UniAD's 50.0 GB and 1.8 FPS [20]. - The method's reduced computational requirements make it more accessible for real-time applications in autonomous driving [20]. Group 4: Course and Learning Opportunities - The article promotes a course focused on end-to-end autonomous driving algorithms, covering foundational knowledge, practical implementations, and various algorithmic approaches [29][41]. - The course aims to equip participants with the skills necessary to understand and implement end-to-end solutions in the autonomous driving industry [54][56].

Core Insights - The article discusses the rapid advancements in end-to-end autonomous driving, particularly focusing on Vision-Language-Action (VLA) models and their applications in the industry [2][3]. Group 1: VLA Model Developments - The introduction of AutoVLA, a new VLA model that integrates reasoning and action generation for end-to-end autonomous driving, shows promising results in semantic reasoning and trajectory planning [3][4]. - ReCogDrive, another VLA model, addresses performance issues in rare and long-tail scenarios by utilizing a three-stage training framework that combines visual language models with diffusion planners [7][9]. - Impromptu VLA introduces a dataset aimed at improving VLA models' performance in unstructured extreme conditions, demonstrating significant performance improvements in established benchmarks [14][24]. Group 2: Experimental Results - AutoVLA achieved competitive performance metrics in various scenarios, with the best-of-N method reaching a PDMS score of 92.12, indicating its effectiveness in planning and execution [5]. - ReCogDrive set a new state-of-the-art PDMS score of 89.6 on the NAVSIM benchmark, showcasing its robustness and safety in driving trajectories [9][10]. - The OpenDriveVLA model demonstrated superior results in open-loop trajectory planning and driving-related question-answering tasks, outperforming previous methods on the nuScenes dataset [28][32]. Group 3: Industry Trends - The article highlights a trend among major automotive manufacturers, such as Li Auto, Xiaomi, and XPeng, to invest heavily in VLA model research and development, indicating a competitive landscape in autonomous driving technology [2][3]. - The integration of large language models (LLMs) with VLA frameworks is becoming a focal point for enhancing decision-making capabilities in autonomous vehicles, as seen in models like ORION and VLM-RL [33][39].

Core Viewpoint - The article discusses the challenges and opportunities faced by SenseTime's autonomous driving division, particularly focusing on the competitive landscape and the importance of technological advancements in the industry. Group 1: Company Developments - The head of the world model development for SenseTime's autonomous driving division has left the company, which raises concerns about the future of their cloud technology system and the R-UniAD generative driving solution [2][3]. - SenseTime's autonomous driving division has successfully delivered a mid-tier solution based on the J6M model to GAC Trumpchi, but the mid-tier market is expected to undergo significant upgrades this year [4]. Group 2: Market Dynamics - The mid-tier market will see a shift from highway-based NOA (Navigation on Autopilot) to full urban NOA, which represents a major change in the competitive landscape [4]. - Leading companies are introducing lightweight urban NOA solutions based on high-tier algorithms, targeting chips with around 100 TOPS computing power, which are already being demonstrated to OEM clients [4]. Group 3: High-Tier Strategy - The key focus for SenseTime this year is the one-stage end-to-end solution, which has shown impressive performance and is a requirement for high-tier project tenders from OEMs [5]. - Collaborations with Dongfeng Motor aim for mass production and delivery of the UniAD one-stage end-to-end solution by Q4 2025, marking a critical opportunity for SenseTime to establish a foothold in the high-tier market [5][6]. Group 4: Competitive Landscape - SenseTime's ability to deliver a benchmark project in the high-tier segment is crucial for gaining credibility with OEMs and securing additional projects [6][7]. - The current window of opportunity for SenseTime in the high-tier market is limited, as many models capable of supporting high-tier software and hardware costs are being released this year [6][8].

今天自动驾驶之心为大家分享 英伟达、复旦大学 最新的工作！ GTRS：可泛化的 多模式端到端轨迹规划! 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 >>点击进入→ 自动驾驶之心 『端到端自动驾驶』技术交流群 论文作者 | Zhenxin Li等 编辑 | 自动驾驶之心 论文链接：https://arxiv.org/abs/2506.06664 Github：https://github.com/NVlabs/GTRS NVIDIA技术博客：https://blogs.nvidia.com/blog/auto-research-cvpr-2025/?ncid=so-nvsh-677066 CVPR 2025 Autonomous Grand Challenge: https://opendrivelab.com/legacy/challenge2025/index.html 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 端到端自动驾驶挑战赛背景 NAVSIM v2 ...

Core Viewpoint - The article discusses the top 10 recommended papers in the field of end-to-end autonomous driving, highlighting the increasing presence of Li Auto in the discourse surrounding autonomous driving technology and research [2][20][22]. Group 1: Overview of Recommended Papers - The article presents a list of 10 highly recommended papers in the end-to-end autonomous driving domain, compiled from interviews with leading researchers [22][26]. - The papers cover various innovative approaches, including reinforcement learning, vision-language models, and multimodal frameworks [27][29][35][40]. Group 2: Key Innovations and Technologies - The paper "TransDiffuser" introduces an encoder-decoder model for trajectory generation, utilizing multimodal perception information to create diverse and high-quality trajectories [10][42]. - The diffusion model is highlighted for its ability to generate trajectories by learning from noise, significantly improving the model's performance in complex traffic environments [6][7][13][16]. - The architecture of TransDiffuser includes a scene encoder for processing multimodal data and a denoising decoder for trajectory generation [11][12][14]. Group 3: Performance Metrics and Results - TransDiffuser achieved a Predictive Driver Model Score (PDMS) of 94.85 on the NAVSIM benchmark, outperforming existing methods [15][42]. - The model's efficiency is enhanced through the use of ordinary differential equations (ODE) sampling, allowing for rapid trajectory generation [7][13]. Group 4: Future Directions and Challenges - The authors of the papers acknowledge challenges in fine-tuning models and suggest future work could involve integrating reinforcement learning and exploring models like OpenVLA [17][18]. - The article emphasizes the ongoing evolution in the field, with a shift towards more integrated and robust approaches to autonomous driving [70].

Core Insights - The article discusses the evolution of SenseAuto, a subsidiary of SenseTime, focusing on its advancements in end-to-end autonomous driving technology and the challenges faced in the automotive industry [2][3][4]. Group 1: Company Background and Innovations - Wang Xiaogang, CEO of SenseAuto, was among the first to propose the "end-to-end" approach in computer vision, aiming to reduce errors in intermediate module transmissions [2][3]. - SenseAuto launched its first product, the SenseDrive DMS driver monitoring system, in 2018, and secured partnerships with major Tier 1 suppliers and over 10 OEMs [4][5]. - The company introduced the SenseAuto Pilot-P solution in 2021, achieving L2+ level advanced driver assistance functions [4][5]. Group 2: Market Position and Competition - SenseAuto's entry into the automotive sector was marked by a focus on intelligent cockpit solutions, while the autonomous driving sector was still in a chaotic phase with no consensus on the future direction [3][4]. - The emergence of Tesla and its successful implementation of end-to-end autonomous driving models in 2022 shifted industry dynamics, prompting other companies like Xiaopeng and Li Auto to adopt similar strategies [5][6]. Group 3: Strategic Development and Challenges - Wang Xiaogang emphasized the need for cost reduction and efficiency improvement to compete effectively in mass production, which poses a significant challenge for SenseAuto [6][7]. - The company is focusing on talent acquisition and platformization to address the challenges of adapting to various hardware platforms and software [7][8]. Group 4: Future Outlook and Business Strategy - SenseAuto aims to expand its delivery range in the mid-to-low-end market by 2025, with plans to collaborate with new partners like GAC Aion and FAW Hongqi [11][12]. - The company is also developing a multi-modal large model, DriveAGI, to enhance its autonomous driving technology, which is expected to exceed human capabilities [11][12]. - SenseAuto positions itself as an AI platform company in the automotive sector, focusing on building AI infrastructure and data pipelines for enterprises [11][12].

"我们牵头为端到端自动驾驶系统编写了一本'教科书'。"近日，东风汽车宣布了这一成 果。这家拥有56年造车积淀的国有车企正向外界展示其在智能化领域的创新实力，发布了目 前行业最大规模的端到端自动驾驶开源数据集。 什么是端到端自动驾驶？这是一种智能计算系统，以原始传感器数据（如相机图像、激 光雷达数据等）为输入，实现深度学习，像人一样，从"看到路"到"控制车"一步到位，全程 自己搞定。 这份"教科书"涵盖了125万组数据，包含超6000个场景片段，涵盖不同时段、天气及多 种复杂驾驶场景的数据，给自动驾驶汽车提供了"实战"教学。例如"下雨天避开积水""晚上 会车时保持适当距离""应对出现的行人"等每一个具体场景都经过精确标注。"换句话说，这 些数据让自动驾驶技术更加'聪明'，从而在复杂的路况中更加精准地做出判断和反应，为下 一代智能汽车的发展奠定基础。"东风汽车研发总院智能化技术总工程师李红林解释道。 目前，我国智能驾驶行业面临数据体量不足、数据孤岛化现象严重等挑战，行业急需打 造开放统一的数据生态。"有了这个开源的数据集，大家基于同样的高质量数据作为研究基 础，就能推动产业协同，让自动驾驶技术更快进步。"李红林表 ...