作者 | Neob0dy 编辑 | 自动驾驶之心 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 具体职位：规划控制方向，自主记忆泊车-自动开出，职位描述在最后。nv大军压境入局自动驾驶，和其他主机厂、L4创业公司相比最大的区别就 是职位划分真的非常细。hr表示nv今年没有校招名额，只有这个实习转正（说大概率可以转正），拿到offer后支持线上实习。笔试之后总共面了5 轮通过，笔试3道题，另外每面都有1-2道算法题。 笔试 已经记不太清了，一道打卡题图搜索，一道模拟没做出来，最后一道是leetcode难度中等的动态规划，给定一个数组，每次可以对相邻的两个数进 行异或操作，操作次数不限，问操作过后的结果相加最大是多少。问题转化之后和leetcode上一个小偷最多可以偷多少房间类似（不能偷相邻的不 然会触发警报）。测试用例第一题过了90，第二题寄了，第三题应该是边界没处理好过了70%。 一面 按流程自我介绍，问了几个项目，着重问了参加比赛的经历，怎么管理和领导团队。单独拎 ...

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].

Summary of Conference Call Notes Industry Overview - The conference call discusses the challenges and opportunities in the autonomous driving industry, particularly focusing on traditional automakers and their ability to develop self-driving algorithms and chips compared to new entrants and leading third-party companies [1][3][4]. Key Points and Arguments Challenges for Traditional Automakers - Traditional automakers are significantly weaker in self-developed autonomous driving algorithms compared to new players and leading third-party firms, due to factors such as leadership quality, development models, slow iteration speeds, and insufficient data accumulation [1]. - The main barriers for traditional automakers in self-developing algorithms include: - **Technical Capability**: Traditional firms lack the understanding and development capabilities for algorithms compared to new entrants [3]. - **Development Cycle**: New players can iterate versions in one to two weeks, while traditional firms have slower iteration speeds [3]. - **Financial Investment**: Developing autonomous driving algorithms is costly, with leading firms spending millions annually on talent and computational resources [3]. - **Data Closure**: Traditional automakers have lower data accumulation rates due to lower penetration of intelligent features [3]. Self-Developed Chips - The challenges in self-developing chips include: - **Technical Capability**: Traditional firms lag in core architecture and IP selection [4]. - **Development Cycle**: The fastest design to production cycle is about 1.5 years, but traditional firms face delays due to rigid development models [4]. - **Financial Support**: The cost of chip production exceeds 150 million yuan, which is burdensome for many traditional automakers [4]. - **Algorithm and Chip Optimization**: Many traditional firms struggle to define their algorithm direction, complicating optimization efforts [4]. Market Segmentation - The autonomous driving market can be segmented into three tiers: - **First Tier**: Companies like Huawei, Xiaopeng, and Li Auto that are fully self-developing and have achieved mass production [5]. - **Second Tier**: Companies like Xiaomi, Geely, and BYD that are combining self-development with third-party collaborations [5]. - **Third Tier**: Companies like SAIC and FAW that rely entirely on third-party solutions [5]. Opportunities for Mid-Tier Companies - Mid-tier companies have the potential to either advance or decline based on their ability to enhance R&D capabilities, increase financial investment, shorten development cycles, and collaborate with advanced technology partners [6]. Conditions for Successful Chip Development - Companies aiming to develop chips should have: - **Moderate Computational Power**: At least 200 TOPS or 80 TOPS [7]. - **Data Closure**: A significant amount of data from mass-produced vehicles, ideally over 600,000 units [7]. - **Computational Requirements**: A minimum of 300 million FLOPS to ensure iteration speed and closure capabilities [7]. - **Leadership and Organizational Support**: Strong leadership with business acumen and a supportive organizational structure for rapid iteration [7]. IP Licensing and Costs - The industry standard for IP licensing includes: - A one-time authorization fee of approximately 30 million yuan, with an annual maintenance fee of about 2 million yuan [8][9]. - Royalties based on chip sales, typically around 5% [8][9]. Data Scarcity and Its Importance - Data scarcity remains a critical issue, as companies with rich data resources can optimize and expand their capabilities more effectively than those with limited data [14]. Future Trends and Developments - The autonomous driving technology landscape is expected to undergo significant changes in the next two years, with a focus on world models and reinforcement learning [29][30]. - Companies that continue to invest in R&D and enhance their technical capabilities may catch up with or surpass current leaders in the long term [29]. Academic Insights - Academic discussions are focusing on using reinforcement learning for model generation and exploring new architectures to improve existing models [32]. Other Important Insights - The impact of new regulations from the Ministry of Industry and Information Technology (MIIT) is expected to widen the gap between first and second-tier companies, affecting market competition and investment decisions [20][21]. - The transition from software to hardware development poses challenges for companies like Monta, which require significant experience in hardware processes [11]. This summary encapsulates the key discussions and insights from the conference call, highlighting the competitive landscape and the challenges faced by traditional automakers in the autonomous driving sector.