自动驾驶算法

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又帮到了一位同学拿到了自动驾驶算法岗......
自动驾驶之心· 2025-08-23 14:44
最近有个开学即将研三的同学找柱哥诉苦,同门都在转具身智能,或者打算主攻大模型、Agent之类的互联网的 大厂,自己还在搞自动驾驶算法。从去年开始,行业就开始出现诸多裁员的消息,明年秋招了有些迷茫。。。最 开始大家都是做感知相关的,慢慢开始有些区别了。想问下自己是继续投身智驾行业,还是考虑转行。 这两天 才刚看到我们自动驾驶之心的社区,体系很完整,就怕有些晚了。 "什么时候都不算太晚。" 况且你还有时间聚焦在一些技术壁垒更高的方向,像VLA或者端到端后面转大模型或 者具身也更容易,不用太担心。尽快把自己的技术栈扩展和打牢才是重中之重。 如果你没有较强独立学习和搜 索问题的能力,可以来我们的自驾社区,也是目前国内最大最全的自驾学习平台【自动驾驶之心】知识星球。 "自动驾驶之心知识星球"目前集视频 + 图文 + 学习路线 + 问答 + 求职交流为一体,是一个综合类的自驾社区, 超过4000人了。 我们期望未来2年内做到近万人的规模。给大家打造一个交流+技术分享的聚集地,是许多初学 者和进阶的同学经常逛的地方。 社区内部还经常为大家解答各类实用问题:端到端如何入门?自动驾驶多模态大模型如何学习?自动驾驶VLA 的学习 ...
从最初的2D方案到当前的VLA大框架,一代又一代的自驾路线是怎么样演变的?
自动驾驶之心· 2025-08-22 04:00
能让学习变得有趣,一定是件了不起的事情。能推动行业发展,成为企业和高校沟通的桥梁,就更 伟大了!1个月前,在和朋友聊天的时候说过,我们的愿景是让AI与自动驾驶走进每个有需要的同 学。 自动驾驶之心知识星球,见证了自驾方案的多代变更,从早期的2D方案到如今VLA的大框架,一个 身经百战的地方、一个饱含内容的地方。截止到目前,社区已经完成了产业、学术、求职、问答交 流等多个领域的闭环。几个运营的小伙伴每天都在复盘,什么样的社区才是大家需要的?有没有什 么地方没有考虑到?花拳绣腿的不行、没人交流的也不行、找不到工作的更不行。 于是我们就给大家准备了学术领域最前沿的内容、工业界大佬级别圆桌、开源的代码方案、最及时 的求职信息、24h内的及时反馈... 星球内部为大家梳理了近40+技术路线,无论你是咨询行业应用、还是要找最新的VLA benchmark、 综述和学习入门路线,都能极大缩短检索时间。星球还为大家邀请了数十位自动驾驶领域嘉宾,都 是活跃在一线产业界和工业界的大佬(经常出现的顶会和各类访谈中哦)。欢迎随时提问,他们将 会为大家答疑解惑。欢迎扫码加入,和我们一起承担未来的领袖工作。 这是一个认真做内容的社区,一 ...
英伟达自动驾驶算法工程师面试
自动驾驶之心· 2025-07-27 14:41
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].
对话四维图新CEO程鹏:智驾上岸的只有华为和理想,但我还可以干20年
雷峰网· 2025-07-22 09:48
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].
推荐几个PNC和端到端岗位(待遇丰厚)
自动驾驶之心· 2025-07-14 06:20
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]
暑假打比赛!RealADSim Workshop智驾挑战赛正式开启,奖池总金额超30万(ICCV'25)
自动驾驶之心· 2025-07-11 09:42
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].
想去华为,算法方向不对口,找工作有点慌了。。。
自动驾驶之心· 2025-07-08 12:45
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].
SOTA端到端算法如何设计?CVPR'25 WOD纯视觉端到端比赛Top3技术分享~
自动驾驶之心· 2025-06-25 09:54
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].
自研算法是否将成为主机厂的必选项?——第三方算法厂商的“护城河”探讨
2025-05-13 15:19
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.
产业资本正成为上市公司股权出售的主要买家
阿尔法工场研究院· 2025-03-26 13:33
Core Viewpoint - The acquisition of Tianmai Technology by Qiming Venture Partners for 452 million yuan marks a significant shift in the role of private equity funds from "financial catchers" to "industry operators" in the Chinese capital market, amidst a transition from "incremental expansion" to "stock renewal" due to IPO slowdowns and asset revitalization [2][3][4]. Policy Environment and Regulatory Framework - The release of the "Six Merger Rules" by the China Securities Regulatory Commission (CSRC) in September 2024 signifies a new era for mergers and acquisitions in the Chinese capital market, explicitly supporting private equity funds in acquiring listed companies for industrial integration [5][6]. - The policy introduces a "reverse linkage" mechanism, reducing the lock-up period for private equity funds from 12 months to 6 months after a 5-year investment period, significantly lowering exit costs and encouraging deeper participation in industrial integration [6][7][8]. - Following the policy's implementation, the number of major merger transactions surged by 460% year-on-year, while non-major transactions increased by 32% [11]. Historical Evolution - The legitimacy of private equity funds controlling listed companies was long questioned, especially after the cautious regulatory stance post the "Baowan dispute" in 2016, which led to scrutiny of leveraged buyouts [12][13][15]. - The introduction of the "Six Merger Rules" has redefined private equity funds as "industry integrators," emphasizing the enhancement of listed company quality through mergers and acquisitions [16]. Transaction Design and Implementation - The acquisition involved a differentiated pricing strategy, where the original controlling shareholder transferred shares at a higher price compared to other shareholders, reflecting control premium and liquidity needs [24][28]. - A phased payment structure was established to alleviate financial pressure and ensure transaction certainty, with a total of 60% paid initially, followed by 30% and 10% in subsequent phases [32][36]. - Governance restructuring was crucial, with a board composed of members nominated by both Qiming and the original controlling shareholder, ensuring a balance of power and strategic decision-making [40][42]. Fund Structure - Qiming's fund structure includes a mix of internal and external capital, with 65% from its main fund and 35% from external investors, ensuring compliance with regulatory requirements [47][48]. - The fund's exit strategy involves asset injection to enhance valuation, with plans for future divestments post-lock-up period [50][51]. Industry Implications - The case of Qiming Venture Partners illustrates a shift from "early and small investments" to "industry-led" strategies, highlighting the importance of policy support and innovative transaction structures in the evolving landscape of private equity in China [67]. - The successful integration of technology and assets post-acquisition is expected to enhance the performance of Tianmai Technology, showcasing the potential for private equity to drive value creation in listed companies [60][63].