信息技术应用创新产业（以下简称"信创产业"）作为中国为构建自主可控的IT生态体系而重点发展 的战略性新兴产业，正迎来蓬勃发展。日前，2025第二届教育信息技术应用创新大赛在杭州举办，来自 全国295所高校的320支队伍、888名师生代表参加了决赛。 大赛覆盖信息技术应用创新的多个方向，既有应用软件适配迁移、操作系统技能挑战等基础实操类 赛项，也专设了人工智能方向，如基于大模型的教育教学应用开发、自动驾驶算法挑战等10个创新赛 道。比赛中，选手们完成了系统部署、应用开发、网络安全等高难度挑战，展现出扎实的技术功底与创 新能力。 当前，信创产业正经历从"可用"到"好用"、从试点到规模化的关键转型。中国教育技术协会副会 长、教育部教育管理信息中心副主任、教育信创实验室主任曾德华认为，大赛赛项设置精准对接教育场 景实际需求，通过以赛促学的方式有效促进了产业与教育的深度融合，为信创人才培养体系建设提供了 实践范本。 ...

Core Insights - The article discusses the intricacies of job interviews in the autonomous driving sector, particularly focusing on the detailed role divisions within companies like NV and the technical expectations from candidates [3][4][5][8][11][12][14]. Group 1: Interview Process - The interview process for positions in autonomous driving involves multiple rounds, including technical assessments and coding challenges, with a focus on specific skills such as dynamic programming and algorithm optimization [4][5][8][11][12]. - Candidates are expected to demonstrate their understanding of advanced concepts like Model Predictive Control (MPC), Simultaneous Localization and Mapping (SLAM), and various optimization techniques [5][8][12][14]. - The coding challenges often include data structure manipulations, such as linked lists and dynamic programming problems, which are critical for assessing a candidate's problem-solving abilities [6][11][14]. Group 2: Technical Skills and Knowledge - A strong grasp of algorithms, particularly in the context of planning and control for autonomous vehicles, is essential. Candidates are often asked to explain complex algorithms like hybrid A* and kinodynamic-RRT [12][14]. - Knowledge of deep learning, especially in image processing and object detection, is increasingly important in the autonomous driving field, reflecting the industry's shift towards integrating AI technologies [11][12][14]. - Candidates are also evaluated on their ability to communicate technical concepts clearly, indicating the importance of both technical and soft skills in the hiring process [8][11][12]. Group 3: Industry Trends - The autonomous driving industry is experiencing a convergence of technology stacks, with a move towards unified models and higher technical barriers, which may impact job roles and required skills [22]. - There is a growing community focused on sharing knowledge and resources related to job opportunities and industry developments, highlighting the collaborative nature of the field [19][22]. - The article emphasizes the importance of networking and community engagement for professionals seeking to advance their careers in autonomous driving [22].

Core Viewpoint - The article emphasizes the importance of continuous learning and adaptation in the field of autonomous driving, particularly in light of industry shifts towards intelligent models and large models, while also highlighting the value of community support for knowledge sharing and job opportunities [1][2]. Group 1: Community and Learning Resources - The "Autonomous Driving Heart Knowledge Planet" is a comprehensive community platform that integrates video, text, learning paths, Q&A, and job exchange, aiming to grow from over 4,000 to nearly 10,000 members in two years [1][2]. - The community provides practical solutions for various topics such as entry points for end-to-end models, learning paths for multimodal large models, and engineering practices for data closed-loop 4D annotation [2][3]. - Members have access to over 40 technical routes, including industry applications, VLA benchmarks, and learning entry routes, significantly reducing search time for relevant information [2][3]. Group 2: Job Opportunities and Networking - The community has established internal referral mechanisms with multiple autonomous driving companies, facilitating job applications and resume submissions directly to desired companies [7]. - Regular job sharing and updates on available positions are provided, creating a complete ecosystem for autonomous driving professionals [15][30]. Group 3: Technical Learning and Development - The community offers a well-structured technical stack and roadmap for beginners, covering essential areas such as mathematics, computer vision, deep learning, and programming [11][32]. - Various learning routes are available for advanced topics, including end-to-end autonomous driving, 3DGS principles, and multimodal large models, catering to both newcomers and experienced professionals [16][34][40]. - The platform also hosts live sessions with industry leaders, providing insights into cutting-edge research and practical applications in autonomous driving [58][66].

Core Viewpoint - The article emphasizes the importance of creating an engaging learning environment in the field of autonomous driving and AI, aiming to bridge the gap between industry and academia while providing resources for career development and technical knowledge sharing [1][3]. Group 1: Community and Resources - The "Autonomous Driving Heart Knowledge Planet" has evolved through multiple iterations, providing a comprehensive platform for academic and industry exchanges, including job opportunities and technical discussions [1]. - The community has compiled over 40 technical routes and resources, significantly reducing the time needed for information retrieval in the autonomous driving sector [1]. - Members include individuals from renowned universities and leading companies in the autonomous driving field, fostering a rich environment for knowledge sharing [12]. Group 2: Technical Learning and Development - The community offers a structured learning path for newcomers, including foundational knowledge in mathematics, computer vision, and deep learning, as well as practical programming skills [12][20]. - Various learning routes are available, such as end-to-end learning, multi-modal large models, and simulation frameworks, catering to different levels of expertise [12][34]. - The platform provides access to numerous open-source projects and datasets relevant to autonomous driving, enhancing practical learning and application [30][32]. Group 3: Job Opportunities and Networking - The community has established a job referral mechanism with multiple autonomous driving companies, facilitating direct connections between job seekers and employers [6]. - Regular job postings and sharing of internship opportunities are available, helping members stay informed about the latest openings in the industry [11][22]. - Members can engage in discussions about career choices and research directions, receiving guidance from experienced professionals in the field [89]. Group 4: Technical Discussions and Innovations - The community hosts discussions on cutting-edge topics such as VLA (Vision Language Architecture), world models, and diffusion models, keeping members updated on the latest advancements [44][48]. - Regular live sessions with industry experts are conducted, allowing members to learn about new technologies and methodologies in autonomous driving [85]. - The platform encourages collaboration and knowledge exchange, aiming to cultivate future leaders in the autonomous driving industry [3].

Core Insights - The article discusses the recruitment process and experiences of candidates applying for positions in the autonomous driving sector, particularly focusing on the detailed interview process at a company referred to as "nv" [3][12][13]. Recruitment Process - The recruitment process includes multiple rounds of interviews, with candidates facing technical questions related to their projects and coding challenges [3][4][5][6][8][10][11][12][13]. - Candidates are evaluated on their understanding of various algorithms and optimization techniques, particularly in the context of motion planning and control [5][8][11]. Technical Skills and Knowledge - Candidates are expected to demonstrate knowledge in areas such as Model Predictive Control (MPC), Simultaneous Localization and Mapping (SLAM), and deep learning applications in autonomous driving [8][10][11][13]. - Coding challenges often involve data structures and algorithms, with specific tasks such as merging linked lists and dynamic programming problems [6][10][12][13]. Industry Trends - The article highlights a trend in the autonomous driving industry where the technology stack is becoming more standardized, leading to higher technical barriers for entry [20]. - There is a growing community focused on sharing knowledge and resources related to autonomous driving, with an emphasis on collaboration and support among professionals in the field [20][22]. Community and Networking - The establishment of a community platform for professionals in autonomous driving is mentioned, aimed at facilitating discussions on industry trends, job opportunities, and technical knowledge sharing [20][22]. - The community includes members from various companies and research institutions, fostering a collaborative environment for learning and career advancement [18][22].

Core Viewpoint - The article discusses the evolution and challenges faced by the company 四维图新 (Four-Dimensional Map) in the context of the competitive landscape shaped by major tech players like BAT (Baidu, Alibaba, Tencent) and the shift towards intelligent driving technologies. Group 1: Company Evolution - 四维图新 has undergone significant transformation since its IPO in 2010, facing intense competition from BAT, which has disrupted its core map business [4][10]. - The CEO, 程鹏, recognized the need to pivot from traditional map services to focus on intelligent driving, high-precision positioning, and automotive chips, leading to the divestment of unrelated business units [5][18]. - The company has faced financial challenges, reporting a revenue of 35.18 billion yuan in 2024, a 12.68% increase year-on-year, but still incurred a loss of 10 billion yuan [6][7]. Group 2: Competitive Landscape - The entry of internet giants into the map sector has been described as a "dimensionality reduction attack," making it difficult for traditional players like 四维图新 to compete [12][13]. - The concept of "no map" in autonomous driving, popularized by competitors, has been misinterpreted, impacting 四维图新’s market perception despite its advancements in high-precision mapping [6][57]. - The company has achieved a leading position in high-precision mapping but struggled to monetize this success due to market shifts towards "no map" solutions [6][26]. Group 3: Strategic Focus - The company has shifted its strategy to focus on becoming a new type of Tier 1 supplier in the automotive industry, emphasizing intelligent driving as its core business [18][38]. - 程鹏 emphasizes the importance of maintaining a long-term perspective, stating that the intelligent driving sector is a marathon, not a sprint, and that the company is committed to gradual growth and market share accumulation [75][76]. - The company is also exploring international markets and new product lines, including information security services, as part of its growth strategy [73][74].

Group 1 - The article discusses job opportunities in the autonomous driving sector, specifically for positions related to end-to-end and traditional control algorithms at a leading self-driving supplier [1] - Positions mentioned include Autonomous Driving Control Algorithm Engineer/PNC Expert with a salary range of 40k-100k/month and End-to-End/VLA Engineer with a salary range of 30k-80k/month [2][4] - The article highlights the responsibilities and requirements for various roles, emphasizing the need for advanced degrees and proficiency in programming languages such as C++ and Python, as well as familiarity with control algorithms and machine learning techniques [5][10] Group 2 - The article mentions a community called AutoRobo Knowledge Planet, which serves as a platform for job seekers in autonomous driving and embodied intelligence, currently hosting nearly 1000 members from various companies [11] - It outlines the internal resources available to members, including interview questions, industry reports, salary negotiation tips, and job referrals [13][14] - The community also provides insights into the autonomous driving industry, including trends, market opportunities, and research reports on embodied intelligence [23][24]

Core Viewpoint - The article emphasizes the significance of high-fidelity simulation technology in overcoming the challenges of testing autonomous driving algorithms, particularly through the introduction of New View Synthesis (NVS) technology, which allows for the creation of closed-loop driving simulation environments based on real-world data [1][2]. Group 1: Challenges and Tasks - The workshop addresses two main challenges in the application of NVS technology, focusing on the need for improved rendering quality in extrapolated views and the evaluation of driving algorithms in closed-loop simulation environments [2][3]. - The first track, "Extrapolated View New View Synthesis," aims to enhance rendering quality under sparse input views, which is crucial for evaluating autonomous driving algorithms in various scenarios [3][4]. - The second track, "Closed-Loop Simulation Evaluation," highlights the importance of creating high-fidelity simulation environments that bridge the gap between real-world data and interactive assessments, overcoming the limitations of traditional static datasets [5][6]. Group 2: Competition Details - Each track of the workshop offers awards, including a Creative Award of $9,000, and the competition is set to commence on June 30, 2025, with submissions due by August 31, 2025 [8][9]. - The workshop encourages global participation to advance autonomous driving technology, providing a platform for challenging and valuable research [10][11].

Core Viewpoint - The article emphasizes the challenges faced by students and job seekers in the autonomous driving sector, particularly in aligning their skills with job requirements, and introduces a new career coaching service aimed at helping individuals transition into this rapidly evolving field [2][4][3]. Group 1: Job Market Challenges - Many students struggle to find internships or job positions that match their skills, especially in autonomous driving algorithm roles, due to the fast-paced evolution of technology [2][3]. - There is a common issue among job seekers regarding the mismatch between their educational background and the current job market demands in the autonomous driving industry [3]. Group 2: Coaching Service Introduction - The newly launched career coaching service targets individuals looking to transition into intelligent driving roles, including recent graduates and professionals without relevant experience [4]. - The coaching program is designed to be completed in approximately two months and focuses on quickly addressing skill gaps to meet job requirements [4]. Group 3: Coaching Service Details - The basic service includes a minimum of 10 one-on-one online meetings, each lasting at least one hour, with a total fee of 8000 [6]. - The service offers personalized analysis of the participant's profile, assessing their knowledge structure and identifying gaps relative to their target positions [7]. Group 4: Advanced Service Options - Advanced services include practical project opportunities that participants can include in their resumes, as well as simulated interviews that mimic both HR and business interviews [11]. - The coaching covers various roles such as intelligent driving product manager, intelligent driving system engineer, and intelligent driving algorithm positions [11]. Group 5: Instructor Qualifications - The coaching instructors are industry experts with over eight years of experience, working in leading autonomous driving companies and manufacturers [12].

Core Insights - The article discusses the results of the 2025 Waymo Open Dataset End-to-End Driving Challenge, highlighting the advancements in end-to-end autonomous driving systems and the shift towards using large-scale public datasets for training models [2][18]. Group 1: Competition Results - The champion of the competition was the EPFL team, which utilized the DiffusionDrive model, nuPlan data, and an ensembling strategy [1]. - The runner-up was a collaboration between Nvidia and Tubingen teams, which also referenced DiffusionDrive and SmartRefine, employing multiple datasets to demonstrate the importance of training data quality [1][22]. - The third place was secured by Hanyang University from South Korea, which focused on a simplified structure using only front-view input and vehicle state [1][3]. Group 2: Methodology - The UniPlan framework was introduced, leveraging large-scale public driving datasets to enhance generalization in rare long-tail scenarios, achieving competitive results without relying on expensive multimodal large language models [3][18]. - The model architecture is based on DiffusionDrive, which employs a truncated diffusion strategy for efficient and diverse trajectory generation [4][6]. - The diffusion decoder utilizes a cross-attention mechanism to refine trajectory predictions based on scene context [5][6]. Group 3: Data Processing - The nuPlan dataset was processed to create a diverse training set, resulting in 90,000 samples by applying a sliding window approach [7]. - A similar filtering strategy was used for the WOD-E2E dataset, generating 35,000 training samples and 10,000 validation samples [8]. - The model was trained on a powerful computing setup with four H100 GPUs, achieving significant training efficiency [10]. Group 4: Experimental Results - The performance was evaluated using Rater Feedback Score (RFS) and Average Displacement Error (ADE), with various configurations tested [12][17]. - The results indicated that the combined training of WOD-E2E and nuPlan datasets led to slight improvements in average RFS, particularly in long-tail categories [23]. - The analysis showed that while additional datasets generally provide benefits, the quality of the data sources is more critical than quantity [39]. Group 5: Conclusion - The article emphasizes the potential of data-centric approaches in enhancing the robustness of autonomous driving systems, as demonstrated by the competitive results achieved with the UniPlan framework [18][39].