规划算法工程师（工作地点：北京房山），薪资面议 投递邮箱： hongyan.zhi@uisee.com 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 驭势科技： 1. 熟悉常用的路径规划方法，如混合A*、Lattice、QP、MPC等，并有相关的项目经验； 2. 了解车辆运动学、动力学、建模相关知识； 3. 熟知基本算法和算法优化的方法和思路； 4. 具有丰富的Linux系统下C/C++语言编程经验和良好的编程规范； 1. 学历背景优秀，或有智能驾驶相关行业大厂背景； 2. 在优化问题建模及优化问题求解方面有研究经历； 3. 有机器人或无人驾驶规划相关项目经验； 4. 扎实的数学基础和数学建模能力； 5. 发表过高质量学术论文。 或者联系柱哥简历直推： 工作职责： 1. 研发满足复杂场景和任务要求的无人驾驶轨迹规划算法，保证无人车驾驶的安全、平顺行驶。 任职条件： 5. 良好的团队合作精神； 优先考虑： 更多自动驾驶的技术进展、行业动态、求职内推，欢迎加入自动驾驶之心知识星球！ o 7 19:00 阅读人数 353 写在小鹏 VLA2.0之后,关于 VL ...

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 驭势科技： 工作职责： 1. 研发满足复杂场景和任务要求的无人驾驶轨迹规划算法，保证无人车驾驶的安全、平顺行驶。 任职条件： 5. 良好的团队合作精神； 优先考虑： 1. 熟悉常用的路径规划方法，如混合A*、Lattice、QP、MPC等，并有相关的项目经验； 2. 了解车辆运动学、动力学、建模相关知识； 3. 熟知基本算法和算法优化的方法和思路； 4. 具有丰富的Linux系统下C/C++语言编程经验和良好的编程规范； 1. 学历背景优秀，或有智能驾驶相关行业大厂背景； 2. 在优化问题建模及优化问题求解方面有研究经历； 3. 有机器人或无人驾驶规划相关项目经验； 4. 扎实的数学基础和数学建模能力； 5. 发表过高质量学术论文。 规划算法工程师（工作地点：北京房山），薪资面议 投递邮箱： hongyan.zhi@uisee.com 更多自动驾驶的技术进展、行业动态、求职内推，欢迎加入自动驾驶之心知识星球！ 近期更新内容一览： 自驾圆桌 | 2025.11.17 FSD v14里面藏了VLA吗? 谁在定义自 ...

江岸青 ：早稻田大学博士，博世中央研究院高级算法科学家，vla/闭环算法 研究team leader 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 >>直播和内容获取转到 → 自动驾驶之心知识星球 超五百人预约！赶紧加入 这一个月，大家对智能驾驶的讨论前所未有的高涨。 而这些讨论的背后都有着一个主题： 自动驾驶的下一代方案应该是什么？ 今天自动驾驶之心将为大家带来一场重量级的智驾圆桌，汇聚学术界和工业界的多元观点。这一场圆桌将围绕VLA、世界模型展开极其深入 而全面的讨论，包括世界模型和VLA的各种形态，在产业界落地的进展和结合二者的可能性。会谈及近期特斯拉，理想在ICCV发表的技术报 告，DriveVLA-W0和世界模型的技术讨论等等。敬请期待这场深度与前瞻性兼具的思想盛宴。 主讲嘉宾 许凌云 ：中国科学院博士，卡内基梅隆机器人研究所博士后。共发表12篇机器人领域顶级期刊或会议文章，获取过DARPA SUBT无人车挑战 赛2019年世界冠军。研究成果主要集中在目标检测、跟踪，从2019年到2024年专注于智能驾驶算法的开发，主导过多个行车和泊车量产项目 ...

Core Insights - The article discusses advancements in autonomous driving technology, particularly focusing on the development of the Visual-Language-Action (VLA) framework and world models, highlighting the contributions of various experts in the field [1][2][3][4][5]. Group 1: Key Contributors - Jian Kun, a senior director at Li Auto, has built the autonomous driving technology stack from scratch since joining in 2021, achieving milestones such as Highway NoA in 2022 and City NoA in 2023 [1]. - Xu Lingyun, a PhD from the Chinese Academy of Sciences, leads the parking team at Changan Automobile, focusing on autonomous driving perception and end-to-end system research [2]. - Jiang Anqing, a senior algorithm scientist at Bosch, leads research on VLA and closed-loop algorithms [3]. Group 2: Technological Developments - The discussion includes the potential integration of world models and VLA, questioning whether a unified approach is feasible [8]. - The high demand for data and computing power is making it increasingly difficult for academia to participate in intelligent driving advancements, raising questions about future opportunities in the academic sector [8]. Group 3: Event Highlights - A live discussion on the future of autonomous driving technologies, including insights on Tesla's FSD v14 and its implications for domestic technology [4][5]. - The event featured a deep dive into the reliability of VLM in autonomous driving, with expert opinions on data closed-loop engineering [12].

Core Insights - The article discusses advancements in autonomous driving technology, particularly focusing on the development of the Visual-Language-Action (VLA) framework and world models, highlighting the contributions of various experts in the field [1][2][3][4][5]. Group 1: Key Contributors - Jian Kun, a senior director at Li Auto, has built the autonomous driving technology stack from scratch since 2021, achieving milestones such as Highway NoA in 2022 and City NoA in 2023 [1]. - Xu Lingyun, a PhD from the Chinese Academy of Sciences, leads the parking team at Changan Automobile, focusing on autonomous driving perception and end-to-end system research [2]. - Jiang Anqing, a senior algorithm scientist at Bosch, leads research on VLA and closed-loop algorithms [3]. Group 2: Technological Focus - The discussion includes the potential integration of world models and VLA, questioning whether a unified approach is feasible [8]. - The high demand for data and computing power is making it increasingly difficult for academia to participate in intelligent driving, raising questions about future opportunities in the academic sector [8]. Group 3: Event Highlights - A live discussion on the future of autonomous driving technologies, including insights on Tesla's FSD v14 and its implications for domestic technology [4][5]. - The event featured a deep dive into the reliability of VLM in autonomous driving, with expert opinions on data closed-loop engineering [12].

Core Insights - CITIC Securities maintains a "Buy" rating for XPeng Motors (XPEV.US, 09868) following the company's AI Day on November 5, 2023, where significant technological breakthroughs were shared in four key areas: VLA, robotics, Robotaxi, and flying cars [1] Group 1 - The introduction of female robots and their advanced humanoid gait has garnered significant attention [1] - The company is expected to transition from being perceived as a "new force in the automotive industry" to a "technology company exploring the frontiers of physical AI" by 2026, as mass production of VLA and robotics is realized [1] - The continuous exploration of unknown technological fields is identified as XPeng's core competitive advantage [1]

Core Viewpoint - Citic Securities maintains a "Buy" rating for XPeng Motors (XPEV.US, 09868) following the company's AI Day on November 5, 2025, where significant technological breakthroughs were shared in four key areas: VLA, robotics, Robotaxi, and flying cars [1] Group 1: Technological Advancements - XPeng showcased a female robot with highly realistic humanoid gait, which garnered significant attention [1] - The company is expected to transition from being perceived as a "new force in the automotive industry" to a "technology company exploring the frontiers of physical AI" by 2026 as it achieves mass production of VLA and robotics [1] Group 2: Competitive Advantage - The continuous exploration of unknown technological fields is identified as XPeng's core competitive advantage [1]

Core Insights - The article discusses the evolution and current state of the autonomous driving industry, highlighting the experiences and lessons learned from industry experts [4][7][11] - It emphasizes the importance of strategic execution and the need for companies to avoid weaknesses in their operations to succeed in the competitive landscape of autonomous driving [7][11] Group 1: Industry Evolution - The autonomous driving industry has undergone significant changes over the past decade, with early optimism giving way to more realistic approaches focused on Level 2 (L2) automation and safety [5][6] - Experts reflect on the initial hype surrounding RoboTaxi and the subsequent shift towards practical applications and L2 production, marking a more commercially viable direction for the industry [6][7] Group 2: Key Challenges and Lessons - The industry has faced three major challenges: the abandonment of RoboTaxi, ensuring the safety of L2 systems, and transitioning to mass production [7] - Successful companies in the autonomous driving sector must possess strong strategic execution and avoid operational weaknesses, as the delivery chain for autonomous products is complex and lengthy [7][11] Group 3: Technological Perspectives - The discussion includes insights on VLA (Vision-Language-Action) and world models, highlighting their complementary nature in addressing challenges in autonomous driving [8][10] - Experts agree that advancements in AI and the integration of new technologies will continue to shape the future of autonomous driving, with a focus on balancing innovation and safety [10][11] Group 4: Future Opportunities - There is a consensus among experts that the autonomous driving industry still has significant growth potential, particularly in areas like urban navigation and the integration of academic research into practical applications [11] - The ongoing development of AI coding is seen as a tool that can enhance focus on core algorithmic challenges rather than detracting from the industry's competitive edge [11]

Core Insights - The automatic driving industry is experiencing a battle for narrative control over next-generation technologies, with companies like Li Auto and XPeng betting on VLA (Visual Language Action) as the future architecture, while Huawei criticizes it as a shortcut and promotes its own WA (World Behavior Architecture) [1][2][3] - The rapid emergence of jargon in the industry reflects the struggle for technological branding, as hardware becomes increasingly homogeneous and intelligent driving capabilities become the key differentiator [1][2][3] Group 1: Evolution of Terminology - Before 2022, the automatic driving industry's technical evolution was primarily defined by Tesla and Waymo, with terms being objective descriptions of specific functions [3] - Tesla's AI Day events in 2021 and 2022 significantly influenced the industry, introducing the BEV+Transformer architecture, which improved perception capabilities by integrating multiple camera inputs into a unified 3D view [3][4] - The transition to an "end-to-end" paradigm began in 2022, breaking down the barriers between perception and planning, with Tesla's FSD Beta V12 showcasing a large neural network that processes both simultaneously [5][6] Group 2: Technological Developments - Chinese automakers quickly adopted Tesla's advancements, with companies like XPeng and NIO implementing their own versions of the BEV+Transformer architecture for mass production [4][6] - The industry is moving towards a more integrated approach, with XPeng and Huawei adopting multi-stage end-to-end systems, while NIO is restructuring to focus on end-to-end development [7][8] - The introduction of VLA and world models into the automatic driving sector reflects a shift towards more sophisticated AI models that can understand and respond to complex driving scenarios [9][10][13] Group 3: Competitive Landscape - The competition in computing power is intensifying, with XPeng and Li Auto investing heavily in both vehicle and cloud computing capabilities, aiming to develop larger parameter models for their systems [11][12][36] - The VLA model, initially developed for robotics, is being adapted for automatic driving, with companies like Yuanrong Qixing leading the charge in applying this technology [10][31] - NIO and Huawei are taking a more aggressive approach by deploying world models directly in vehicles for real-time control, although the technology is still in the experimental stage [14][15] Group 4: Future Directions - The evolution of automatic driving terminology indicates a broader exploration of technology, with each new term representing a step in the industry's journey [16] - The ultimate success in the automatic driving sector may hinge on the ability to translate technological promises into tangible user experiences, rather than merely introducing new concepts [16]

Core Insights - The automatic driving industry is experiencing a battle for narrative control over next-generation technologies, with companies like Li Auto and XPeng betting on VLA (Visual Language Action) as the future architecture, while Huawei promotes its self-developed WA (World Behavior Architecture) [1][2][20] - The rapid emergence of jargon in the industry reflects the struggle for technological branding and user perception, as hardware and configurations become increasingly homogeneous [1][2][27] Group 1: Evolution of Technology - Before 2022, the evolution of automatic driving technology was primarily defined by Tesla and Waymo, with terminology focused on objective descriptions of specific functions [3] - Tesla's introduction of the BEV+Transformer architecture in 2021 marked a significant shift from rule-based systems to AI-driven approaches, enhancing perception capabilities [4][5][6] - The transition to an end-to-end paradigm was catalyzed by Tesla's AI DAY in 2022, which integrated perception and planning into a single neural network, significantly improving obstacle recognition [9][10] Group 2: Adoption of New Models - Chinese automakers quickly adopted Tesla's technology, with companies like XPeng and NIO implementing their own versions of the BEV+Transformer model for mass production [8][10] - The industry is moving towards end-to-end systems, with XPeng and Huawei initially adopting a multi-stage approach for safety reasons, before transitioning to fully integrated models [10][12] - The introduction of VLA and world models into automatic driving systems represents a new frontier, with companies like Yuanrong Qixing and NIO leading the charge in applying these concepts [17][20] Group 3: Competitive Landscape - The competition among companies is not only about technology but also about computational power, with XPeng and Li Auto investing heavily in cloud computing capabilities, boasting figures of 10 EFlops and over 13 EFlops respectively [18][19][55] - The race for computational resources extends to both vehicle and cloud platforms, with Tesla's Dojo and other companies ramping up their AI training capabilities [18][57] - The rapid evolution of VLA and world models is indicative of a broader trend where companies are leveraging advanced AI techniques to enhance their automatic driving systems [20][46] Group 4: Future Directions - The world model concept, initially used for simulation, is now being applied in real-time vehicle control by companies like NIO and Huawei, aiming for more predictive and human-like driving experiences [20][24][25] - The emergence of terms like VLA and world models highlights the industry's shift towards integrating language understanding and real-time decision-making into automatic driving systems [46][59] - The ultimate success in this competitive landscape may hinge on a company's ability to translate technological promises into tangible user experiences, rather than merely marketing jargon [30][29]