Core Viewpoint - The article discusses the recruitment process and job responsibilities for a perception algorithm engineer in the autonomous driving industry, emphasizing the importance of skills in computer vision, deep learning, and sensor fusion technologies [1][5][6]. Group 1: Job Responsibilities - The role involves processing large amounts of autonomous driving data, building automated ground truth labeling systems, and designing cutting-edge AI and vision technologies [6]. - Algorithms and code developed will be deployed in millions of mass-produced vehicles [6]. - Key tasks include detecting static scene elements, tracking dynamic targets, and developing calibration methods for various sensors [10]. Group 2: Job Qualifications - Candidates should have a master's degree or higher in relevant fields such as computer science, automation, or mathematics [7]. - Proficiency in programming languages like C++ or Python, along with solid knowledge of algorithms and data structures, is required [7]. - Familiarity with multi-view geometry, computer vision, deep learning, and sensor technology applications is essential [7]. Group 3: Preferred Qualifications - Experience in developing perception algorithms for autonomous driving systems or ADAS, such as lane detection and obstacle tracking, is a plus [9]. - Candidates with experience in sensor fusion involving visual, LiDAR, and millimeter-wave radar are preferred [9]. - Publications in top conferences or journals in the fields of computer vision, machine learning, or robotics are advantageous [9]. Group 4: Community and Resources - The article mentions a community platform for job seekers in autonomous driving and robotics, providing resources such as interview questions, industry reports, and salary negotiation tips [12][13]. - The community aims to assist members in preparing for job applications and understanding industry trends [12][21].

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作者 | 王小迪MLE 编辑 | 自动驾驶之心 原文链接： https://www.zhihu.com/question/279973696/answer/3535722816 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 >>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 最近end2end风头正盛，BEV已成标准范式，但planning进展仍然焦灼。究其原因，interaction modelling是planning的深水区，涉及博弈和不确定性建模，监督学习仍然 不能很好得描述此类问题。这次报告以interaction的建模和求解为切口，分析了近些年常用的框架范式，比如将ego trajectory和agent trajectory的关系加入loss function或 constraint中，ego/agent trajectory从lane routing或neural network生成等。 - (We already have) Reactive: surrounding agents influenc ...

Core Viewpoint - The intelligent driving industry is transitioning from Level 2 (L2) to Level 3 (L3), with significant technological advancements improving user experience [2]. Group 1: Industry Development - The intelligent driving sector is gaining momentum, with companies like Xiaomi achieving impressive sales, such as the YU7 model reaching over 200,000 pre-orders in just three minutes [2]. - The industry is entering a more complex phase, requiring deeper engagement and collaboration among stakeholders to tackle challenges [2]. - The company emphasizes the importance of steady progress and overcoming production challenges as it moves towards L3 capabilities [2]. Group 2: Educational Initiatives - The company is inviting industry leaders to contribute to the development of online courses and consulting services in the autonomous driving field [3]. - There is a focus on advanced topics such as large models, reinforcement learning, and 3D simulation, encouraging experts to join in creating high-quality educational content [3][4]. Group 3: Recruitment and Collaboration - The company seeks individuals with a PhD or equivalent experience, particularly those with over three years of research and development experience in the industry [4]. - It offers attractive compensation packages, including significant profit-sharing and resource sharing across the industry, with options for part-time or full-time engagement [6].

Core Viewpoint - The article emphasizes the critical role of data annotation in the development of autonomous driving systems, highlighting its impact on the performance of perception models and the overall safety of autonomous vehicles [4][14]. Group 1: Data Annotation Importance - Data annotation is essential for converting raw perception data into structured labels with semantic information, which directly influences the system's ability to recognize, understand, and make decisions in real-world environments [4][14]. - Accurate and systematic data annotation enhances the robustness and generalization capabilities of perception algorithms, making it an irreplaceable component in the autonomous driving technology ecosystem [4][14]. Group 2: Types of Data Annotation - Image data annotation focuses on identifying and locating key targets in road scenes, including vehicles, pedestrians, traffic signs, and lane markings, using methods like 2D bounding boxes, instance segmentation, and semantic segmentation [5][14]. - 3D point cloud data annotation involves higher spatial complexity, utilizing 3D bounding boxes to capture the dimensions, center points, orientations, and dynamic states of objects in three-dimensional space [7][14]. - Multi-modal data annotation is required for sensor fusion, where corresponding relationships between different modalities (e.g., images and point clouds) are established to improve recognition accuracy in complex scenarios [9][14]. Group 3: High-Precision Map Data Annotation - High-definition map data annotation involves abstracting and extracting geometric and semantic elements of road structures, such as lane boundaries and traffic signal locations, which are crucial for precise vehicle positioning and decision-making [9][14]. - The annotation process must ensure high spatial accuracy and semantic consistency with perception annotations to maintain the stability of the perception-map linkage model [9][14]. Group 4: Environmental and Behavioral Annotation - Annotation also includes describing the overall environmental state, such as road types, weather conditions, and traffic density, which aids in enhancing the model's adaptability to diverse scenarios [11][14]. - Behavioral annotation focuses on capturing the motion characteristics and intentions of dynamic traffic participants, which is vital for trajectory prediction and risk assessment [11][14]. Group 5: Quality Control in Data Annotation - Quality control is paramount in the annotation process, involving standardized guidelines, professional training for annotators, and multiple rounds of review to ensure consistency across semantic, spatial, and temporal dimensions [13][14]. - Companies often utilize self-developed annotation platforms and feedback mechanisms to create a continuous data iteration loop, enhancing the quality and relevance of the training data [13][14]. Group 6: Conclusion on Data Annotation - The core task of autonomous driving data annotation is to provide accurate, comprehensive, temporally consistent, and context-rich training samples, which are fundamental for the collaborative functioning of perception, prediction, decision-making, and control modules [14].

Core Viewpoint - The article discusses the current state and future prospects of the autonomous driving industry, highlighting the shift towards embodied intelligence and large models, while questioning whether traditional autonomous driving technologies are becoming obsolete [2][3]. Group 1: Industry Perspectives - Some professionals have transitioned away from autonomous driving, believing that the technology stack has become homogenized, with only end-to-end and large models remaining viable [2]. - Those still observing the field are reluctant to leave their current high-paying jobs and lack reliable resources in the embodied intelligence sector [3]. - Many individuals remain committed to the autonomous driving field, viewing it as the most promising path towards achieving general embodied intelligence [3]. Group 2: Industry Challenges - The current state of mass production in autonomous driving is perceived as somewhat chaotic, with existing solutions not yet fully refined before new ones are rushed to market [3]. - The article suggests that the past hype around autonomous driving may have been beneficial, allowing for a more focused approach to solidifying mass production capabilities [3]. Group 3: Future Directions - The future of mass production in autonomous driving is expected to be unified, multi-modal, and end-to-end, requiring full-stack talent who are knowledgeable in perception, planning, prediction, and large models [3]. - The community aims to bridge the gap between academia and industry, facilitating communication and collaboration to advance the field [3][6]. Group 4: Community Initiatives - The "Autonomous Driving Heart" knowledge platform has created a comprehensive ecosystem for sharing academic and industrial insights, including job opportunities and technical resources [5][12][14]. - The platform has organized various resources, including over 40 technical routes and numerous open-source projects, to assist both newcomers and experienced professionals in the field [5][15][16]. Group 5: Educational Resources - The community provides a well-structured entry-level technical stack and roadmap for beginners, as well as valuable industry frameworks and project plans for those already engaged in research [10][12]. - Continuous job postings and sharing of opportunities are part of the community's offerings, aimed at building a complete ecosystem for autonomous driving [14].

Core Viewpoint - The establishment of the VLA technology exchange group aims to facilitate discussions on various aspects of VLA, including dataset creation, one-stage VLA, hierarchical VLA, end-to-end solutions based on large models, VLM+DP-based solutions, mass production implementation, and job opportunities [1] Group 1 - The VLA technology exchange group invites interested individuals to join and share insights related to VLA [1] - The group will cover topics such as VLA dataset production and implementation strategies [1] - Participants can connect through WeChat by adding the assistant with specific identification [1]

Core Viewpoint - End-to-end (E2E) autonomous driving is currently the core algorithm for mass production in intelligent driving, with significant advancements in the VLM/VLA systems leading to high demand for related positions and salaries reaching up to 1 million annually [2][11]. Group 1: Industry Trends - The concept of E2E has evolved significantly, with various technical schools emerging, yet many still struggle to understand its workings and distinctions between single-stage and two-stage approaches [2][4]. - The introduction of VLA (Vision-Language Architecture) is seen as a new frontier in autonomous driving, with companies actively researching and developing new generation mass production solutions [21][22]. Group 2: Educational Initiatives - A new course titled "End-to-End and VLA Autonomous Driving" has been launched to address the challenges faced by newcomers in the field, focusing on practical applications and theoretical foundations [14][27]. - The course aims to provide a comprehensive understanding of E2E autonomous driving, covering various models and methodologies, including diffusion models and reinforcement learning [6][19][21]. Group 3: Job Market Insights - The job market for VLA/VLM algorithm experts is robust, with salaries for positions requiring 3-5 years of experience ranging from 40K to 70K monthly, indicating a strong demand for skilled professionals [11][12]. - Positions such as VLA model quantization deployment engineers and multi-modal VLA model direction experts are particularly sought after, reflecting the industry's shift towards advanced algorithmic solutions [11][12].

微信扫码添加小助理邀请进群， 备注自驾+昵称+求职 ； 最近和很多准备校招的小伙伴接触，发现大家在学校学习的东西和工作的差距越来越大。有不少工作多年 的小伙伴表示也在看机会，感知转大模型、世界模型，传统规控想转具身。但却不知道业内实际在做什 么，导致秋招的时候没有什么优势...... 峰哥一直在鼓励大家坚持、多和其他人交流，但归根结底个人的力量是有限的。我们希望共建一个大的社 群和大家一起成长，真正能够帮助到一些有需要的小伙伴，成为一个汇集全行业人才的综合型平台，真正 做一个链接学校和公司的桥梁。所以我们也开始正式运营求职与行业相关的社群。社群内部主要讨论相关 产业、公司、产品研发、求职与跳槽相关内容。如果您想结交更多同行业的朋友，第一时间了解产业。欢 迎加入我们！ ...

Core Viewpoint - The intelligent driving industry is transitioning from Level 2 (L2) to Level 3 (L3), with significant technological advancements improving user experience [2]. Group 1: Industry Development - The intelligent driving sector is gaining momentum, with companies like Xiaomi achieving impressive sales, such as the YU7 model reaching over 200,000 pre-orders in just three minutes [2]. - The industry is entering a more complex phase, requiring deeper engagement and collaboration among stakeholders to tackle challenges [2]. Group 2: Educational Initiatives - The company is inviting experts in the autonomous driving field to contribute to the development of online courses and consulting services [3]. - There is a focus on advanced topics such as large models, reinforcement learning, and 3D simulation, encouraging participation from individuals with relevant expertise [3][4]. Group 3: Collaboration and Opportunities - The company aims to create a platform for collaboration among global developers and researchers in the autonomous driving sector [2]. - It offers flexible working arrangements, including part-time and full-time opportunities, along with significant profit-sharing and resource sharing within the industry [6].