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 ...